With the rapid urbanization process,ground collapses caused by anthropogenic activities occur frequently.Accurate susceptibility mapping is of great significance for disaster prevention and control.In this study,1198 ...With the rapid urbanization process,ground collapses caused by anthropogenic activities occur frequently.Accurate susceptibility mapping is of great significance for disaster prevention and control.In this study,1198 ground collapse cases in Shenzhen from 2017 to 2020 were collected.Eight effective factors(elevation,relief,clay proportion,average annual precipitation,distance from water,land use type,building density,and road density)were selected to construct the evaluation index system.Ground collapse susceptibility was analyzed and mapped using the normalized frequency ratio(NFR),logistic regression(LR),and NFR-LR coupling models.Finally,the result rationality and performance of the three models were compared through frequency ratio(FR)and ROC curve.The results indicate that all three models can effectively evaluate the ground collapse susceptibility(AUC>0.7),and the NFR-LR model result is more rational and has the best performance(AUC=0.791).The very high and high susceptibility zones cover a total area of 545.68 km^(2) and involve Nanshan,Luohu,and Futian District,as well as some areas of Baoan,Guangming,and Longgang District.The ground collapses in Shenzhen mainly occurred in the built-up areas,and the greater intensity of anthropogenic activities,the more susceptible to the disaster.展开更多
Landslide susceptibility mapping(LSM) is crucial for reducing disaster risk in complex mountainous regions. This study evaluated the impact of various sampling methods on the accuracy of LSM over the next decade in Bi...Landslide susceptibility mapping(LSM) is crucial for reducing disaster risk in complex mountainous regions. This study evaluated the impact of various sampling methods on the accuracy of LSM over the next decade in Bijie City, Guizhou Province, China. Datasets were collected from 614 landslides and 500 non-landslides, and four sampling methods were proposed. Recurrent Neural Network(RNN), Gated Recurrent Unit(GRU), K-Nearest Neighbor(KNN), and Extreme Gradient Boosting(XGB) models were assessed utilising 15 metrics(Elevation, Slope, Aspect, Plan curvature, Profile curvature, Stream Power Index, Sediment Transport Index, Vector Ruggedness Measurement, Topographic Roughness Index, Lithology, Land use, Normalized Difference Vegetation Index(NDVI), Rainfall, Distance from Road, Distance from River). The results demonstrated that the GRU model combined with a 5-m sample boundary from the interior of the landslide and non-landslide areas exhibited superior performance with F1, Accuracy, and Area Under Curve(AUC) scores of 0.9700, 0.9450, and 0.8925, respectively. LSM will be projected for the next decade by coupling the Geophysical Fluid Dynamics Laboratory Earth System Model version 4(GFDLESM4) with the Shared Socioeconomic Pathway(SSP119). This study provides valuable insights into landslide risk management in landslide-prone areas.展开更多
Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with region...Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale.展开更多
Doline susceptibility mapping(DSM)in karst aquifer is important in terms of estimating the vulnerability of the aquifer to pollutants,estimating the infiltration rate,and infrastructures exposed to the development of ...Doline susceptibility mapping(DSM)in karst aquifer is important in terms of estimating the vulnerability of the aquifer to pollutants,estimating the infiltration rate,and infrastructures exposed to the development of dolines.In this research,doline susceptibility map was prepared in Saldaran mountain by generalized linear model(GLM)using 14 affecting parameters extracted from satellite images,digital elevation model,and geology map.Only 8 parameters have been inputted to the model which had correlation with dolines.In this regards,306 dolines were identified by the photogrammetric Unmanned Aerial Vehicles(UAV)method in 600 hectares of Salderan lands and then,these data were divided into the training(70%)and testing(30%)data for modelling.The results of DSM modeling showed that classified probability of doline occurrences in the Saldaran mountain were as follow:16.5%of the area high to very high,72%in the class of low to very low,and 5%in the moderate class.Also,locally,in Saldaran mountain,the Pirghar aquifer has the highest potential for the doline development,followed by Bagh Rostam and Sarab aquifers.Also,the precipitation,digital elevation model,Topographic Position Index,drainage density,slope,TRASP(transformed the circular aspect to a radiation index),Snow-Covered Days and vegetation cover index are of highest importance in the DSM modeling,respectively.Accurate evaluation of the model using the Receiver Operating Characteristics(ROC)curve represents a very good accuracy(AUC=0.953)of the DSM model.展开更多
This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble lear...This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.展开更多
Machine learning(ML)-based prediction models for mapping hazard(e.g.,landslide and debris flow)susceptibility have been widely developed in recent research.However,in some specific areas,ML models have limited applica...Machine learning(ML)-based prediction models for mapping hazard(e.g.,landslide and debris flow)susceptibility have been widely developed in recent research.However,in some specific areas,ML models have limited application because of the uncertainties in identifying negative samples.The Parlung Tsangpo Basin exemplifies a region prone to recurrent glacial debris flows(GDFs)and is characterized by a prominent landform featuring deep gullies.Considering the limitations of the ML model,we developed and compared two combined statistical models(FA-WE and FA-IC)based on factor analysis(FA),weight of evidence(WE),and the information content(IC)method.The final GDF susceptibility maps were generated by selecting 8 most important static factors and considering the influence of precipitation.The results show that the FA-IC model has the best performance.The areas with a very high susceptibility to GDFs are primarily located in the narrow valley section upstream,on both sides of the valley in the middle and downstream of the Parlung Tsangpo River,and in the narrow valley section of each tributary.These areas encompass 86 gullies and are characterized as"narrow and steep".展开更多
Regional landslide susceptibility mapping(LSM)is essential for risk mitigation.While deep learning algorithms are increasingly used in LSM,their extensive parameters and scarce labels(limited landslide records)pose tr...Regional landslide susceptibility mapping(LSM)is essential for risk mitigation.While deep learning algorithms are increasingly used in LSM,their extensive parameters and scarce labels(limited landslide records)pose training challenges.In contrast,classical statistical algorithms,with typically fewer parameters,are less likely to overfit,easier to train,and offer greater interpretability.Additionally,integrating physics-based and data-driven approaches can potentially improve LSM.This paper makes several contributions to enhance the practicality,interpretability,and cross-regional generalization ability of regional LSM models:(1)Two new hybrid models,composed of data-driven and physics-based modules,are proposed and compared.Hybrid ModelⅠcombines the infinite slope stability analysis(ISSA)with logistic regression,a classical statistical algorithm.Hybrid ModelⅡintegrates ISSA with a convolutional neural network,a representative of deep learning techniques.The physics-based module constructs a new explanatory factor with higher nonlinearity and reduces prediction uncertainty caused by incomplete landslide inventory by pre-selecting non-landslide samples.The data-driven module captures the rela-tion between explanatory factors and landslide inventory.(2)A step-wise deletion process is proposed to assess the importance of explanatory factors and identify the minimum necessary factors required to maintain satisfactory model performance.(3)Single-pixel and local-area samples are compared to understand the effect of pixel spatial neighborhood.(4)The impact of nonlinearity in data-driven algorithms on hybrid model performance is explored.Typical landslide-prone regions in the Three Gorges Reservoir,China,are used as the study area.The results show that,in the testing region,by using local-area samples to account for pixel spatial neighborhoods,Hybrid ModelⅠachieves roughly a 4.2%increase in the AUC.Furthermore,models with 30 m resolution land-cover data surpass those using 1000 m resolution data,showing a 5.5%improvement in AUC.The optimal set of explanatory factors includes elevation,land-cover type,and safety factor.These findings reveal the key elements to enhance regional LSM,offering valuable insights for LSM practices.展开更多
Landslide susceptibility mapping is a crucial tool for disaster prevention and management.The performance of conventional data-driven model is greatly influenced by the quality of the samples data.The random selection...Landslide susceptibility mapping is a crucial tool for disaster prevention and management.The performance of conventional data-driven model is greatly influenced by the quality of the samples data.The random selection of negative samples results in the lack of interpretability throughout the assessment process.To address this limitation and construct a high-quality negative samples database,this study introduces a physics-informed machine learning approach,combining the random forest model with Scoops 3D,to optimize the negative samples selection strategy and assess the landslide susceptibility of the study area.The Scoops 3D is employed to determine the factor of safety value leveraging Bishop’s simplified method.Instead of conventional random selection,negative samples are extracted from the areas with a high factor of safety value.Subsequently,the results of conventional random forest model and physics-informed data-driven model are analyzed and discussed,focusing on model performance and prediction uncertainty.In comparison to conventional methods,the physics-informed model,set with a safety area threshold of 3,demonstrates a noteworthy improvement in the mean AUC value by 36.7%,coupled with a reduced prediction uncertainty.It is evident that the determination of the safety area threshold exerts an impact on both prediction uncertainty and model performance.展开更多
Quantitative susceptibility mapping(QSM)is an advanced post‐processing technique in magnetic resonance imaging that offers precise measurements of tissue magnetic susceptibility with impressive spatial resolution and...Quantitative susceptibility mapping(QSM)is an advanced post‐processing technique in magnetic resonance imaging that offers precise measurements of tissue magnetic susceptibility with impressive spatial resolution and sensitivity.This review examines the potential of QSM as a biomarker for early detection and monitoring of amyotrophic lateral sclerosis(ALS).Since 2015,studies have consistently reported increased QSM values in the motor regions of individuals with ALS,indicating significant iron deposition.Iron accumulation is associated with dysfunction of the upper motor neurons and faster disease progression.Notably,increased QSM values were also observed in the critical subcortical areas responsible for motor function and cognitive control.However,standardizing optimized protocols,including background field removal algorithms,phase unwrapping approaches,and methods for final susceptibility map reconstruction,has the potential to enhance the consistency and reliability of QSM as an ALS biomarker.Overall,the current body of evidence strongly supports QSM in detecting iron dysregulation associated with neurodegeneration in both motor and extra‐motor regions in ALS.Furthermore,QSM's remarkable sensitivity to early pathological iron changes and its high specificity in distinguishing ALS positions make it a promising diagnostic and progression‐tracking biomarker.展开更多
Rainfall-induced landslides,exacerbated by climate change,require urgent attention to identify vulnerable regions and propose effective risk mitigation measures.Extensive research underscores the significant impact of...Rainfall-induced landslides,exacerbated by climate change,require urgent attention to identify vulnerable regions and propose effective risk mitigation measures.Extensive research underscores the significant impact of vegetation on soil properties and slope stability,emphasizing the necessity to incorporate vegetation effects into regional landslide susceptibility mapping.This review thoroughly examines research integrating vegetation into landslide susceptibility mapping,encompassing qualitative,semi-quantitative,and quantitative forecasting methods.It highlights the importance of incorporating vegetation aspects into these methods for comprehensive and accurate landslide susceptibility assessment.This review explores the diverse roles of vegetation in slope stability,covering both aggregated impacts and individual influences,including mechanical and hydrological effects on soil properties,as well as the implications of evapotranspiration and rainwater interception on slope stability.While aggregated roles are integrated into non-deterministic methods as input layers,individual roles are considered in deterministic methods.In the application of deterministic methods,it is noteworthy that a considerable number of studies primarily concentrate on the mechanical impact,particularly the reinforcement provided by root cohesion.The review also explores limitations and highlights future research prospects.In the context of mapping landslide susceptibility amid changing climatic conditions,data-driven techniques encounter challenges,while deterministic methods present their advantages.Stressing the significance of hydrological impacts,the paper recommends incorporating vegetation influences on unsaturated soil properties,including the soil water characteristic curve and soil permeability,along with pre-wetting suction due to evapotranspiration and potential rainwater interception.展开更多
The present study aims to develop two hybrid models to optimize the factors and enhance the predictive ability of the landslide susceptibility models.For this,a landslide inventory map was created with 406 historical ...The present study aims to develop two hybrid models to optimize the factors and enhance the predictive ability of the landslide susceptibility models.For this,a landslide inventory map was created with 406 historical landslides and 2030 non-landslide points,which was randomly divided into two datasets for model training(70%)and model testing(30%).22 factors were initially selected to establish a landslide factor database.We applied the GeoDetector and recursive feature elimination method(RFE)to address factor optimization to reduce information redundancy and collinearity in the data.Thereafter,the frequency ratio method,multicollinearity test,and interactive detector were used to analyze and evaluate the optimized factors.Subsequently,the random forest(RF)model was used to create a landslide susceptibility map with original and optimized factors.The resultant hybrid models GeoDetector-RF and RFE-RF were evaluated and compared by the area under the receiver operating characteristic curve(AUC)and accuracy.The accuracy of the two hybrid models(0.868 for GeoDetector-RF and 0.869 for RFE-RF)were higher than that of the RF model(0.860),indicating that the hybrid models with factor optimization have high reliability and predictability.Both RFE-RF GeoDetector-RF had higher AUC values,respectively 0.863 and 0.860,than RF(0.853).These results confirm the ability of factor optimization methods to improve the performance of landslide susceptibility models.展开更多
The Ms 8.0 May 12,2008 Wenchuan earthquake triggered tens of thousands of landslides.The widespread landslides have caused serious casualties and property losses,and posed a great threat to post-earthquake reconstruct...The Ms 8.0 May 12,2008 Wenchuan earthquake triggered tens of thousands of landslides.The widespread landslides have caused serious casualties and property losses,and posed a great threat to post-earthquake reconstruction.A spatial database,inventoried 43,842 landslides with a total area of 632 km 2,was developed by interpretation of multi-resolution remote sensing images.The landslides can be classified into three categories:swallow,disrupted slides and falls;deep-seated slides and falls,and rock avalanches.The correlation between landslides distribution and the influencing parameters including distance from co-seismic fault,lithology,slope gradient,elevation,peak ground acceleration(PGA) and distance from drainage were analyzed.The distance from co-seismic fault was the most significant parameter followed by slope gradient and PGA was the least significant one.A logistic regression model combined with bivariate statistical analysis(BSA) was adopted for landslide susceptibility mapping.The study area was classified into five categories of landslide susceptibility:very low,low,medium,high and very high.92.0% of the study area belongs to low and very low categories with corresponding 9.0% of the total inventoried landslides.Medium susceptible zones make up 4.2% of the area with 17.7% of the total landslides.The rest of the area was classified into high and very high categories,which makes up 3.9% of the area with corresponding 73.3% of the total landslides.Although the susceptibility map can reveal the likelihood of future landslides and debris flows,and it is helpful for the rebuilding process and future zoning issues.展开更多
Numerous researches have been published on the application of landslide susceptibility assessment models;however,they were only applied in the same areas as the models were originated,the effect of applying the models...Numerous researches have been published on the application of landslide susceptibility assessment models;however,they were only applied in the same areas as the models were originated,the effect of applying the models to other areas than the origin of the models has not been explored.This study is purposed to develop an optimized random forest(RF)model with best ratios of positive-to-negative cells and 10-fold cross-validation for landslide susceptibility mapping(LSM),and then explore its generalization ability not only in the area where the model is originated but also in area other than the origin of the model.Two typical counties(Fengjie County and Wushan County)in the Three Gorges Reservoir area,China,which have the same terrain and geological conditions,were selected as an example.To begin with,landslide inventory was prepared based on field investigations,satellite images,and historical records,and 1522 landslides were then identified in Fengjie County.22 landslide-conditioning factors under the influence of topography,geology,environmental conditions,and human activities were prepared.Then,combined with 10-fold cross-validation,three typical ratios of positive-to-negative cells,i.e.,1:1,1:5,and 1:10,were adopted for comparative analyses.An optimized RF model(Fengjie-based model)with the best ratios of positive-to-negative cells and 10-fold cross-validation was constructed.Finally,the Fengjie-based model was applied to Fengjie County and Wushan County,and the confusion matrix and area under the receiver operating characteristic(ROC)curve value(AUC)were used to estimate the accuracy.The Fengjie-based model delivered high stability and predictive capability in Fengjie County,indicating a great generalization ability of the model to the area where the model is originated.The LSM in Wushan County generated by the Fengjie-based model had a reasonable reference value,indicating the Fengjiebased model had a great generalization ability in area other than the origin of the model.The Fengjiebased model in this study could be applied in other similar areas/countries with the same terrain and geological conditions,and a LSM may be generated without collecting landslide information for modeling,so as to reduce workload and improve efficiency in practice.展开更多
The primary objective of landslide susceptibility mapping is the prediction of potential landslides in landslide-prone areas. The predictive power of a landslide susceptibility mapping model could be tested in an adja...The primary objective of landslide susceptibility mapping is the prediction of potential landslides in landslide-prone areas. The predictive power of a landslide susceptibility mapping model could be tested in an adjacent area of similar geo- environmental conditions to find out the reliability. Both the 2oo8 Wenchuan Earthquake and the 2o13 Lushan Earthquake occurred in the Longmen Mountain seismic zone, with similar topographical and geological conditions. The two earthquakes are both featured by thrust fault and similar seismic mechanism This paper adopted the susceptibility mapping model of co-seismic landslides triggered by Wenchuan earthquake to predict the spatial distribution of landslides induced by Lushan earthquake. Six influencing parameters were taken into consideration： distance from the seismic fault, slope gradient, lithology, distance from drainage, elevation and Peak Ground Acceleration （PGA）. The preliminary results suggested that the zones with high susceptibility of co- seismic landslides were mainly distributed in the mountainous areas of Lushan, Baoxing and Tianquan counties. The co-seismic landslide susceptibility map was completed in two days after the quake and sent to the field investigators to provide guidance for rescue and relief work. The predictive power of the susceptibility map was validated by ROC curve analysis method using 2o37 co-seismic landslides in the epicenter area. The AUC value of o.71o indicated that the susceptibility model derived from Wenchuan Earthquake landslides showed good accuracy inpredicting the landslides triggered by Lushan earthquake.展开更多
Investigation on landslide phenomenon is necessary for understanding and delineating the landslide prone and safer places for different land use practices. On this basis, a new model known as genetic algorithm for the...Investigation on landslide phenomenon is necessary for understanding and delineating the landslide prone and safer places for different land use practices. On this basis, a new model known as genetic algorithm for the rule set production was applied in order to assess its efficacy to obtain a better result and a more precise landslide susceptibility map in Klijanerestagh area of Iran. This study considered twelve landslide conditioning factors(LCF) like altitude, slope, aspect, plan curvature, profile curvature, topographic wetness index(TWI), distance from rivers, faults, and roads, land use/cover, and lithology. For modeling purpose, the Genetic Algorithm for the Rule Set Production(GARP) algorithm was applied in order to produce the landslide susceptibility map. Finally, to evaluate the efficacy of the GARP model, receiver operating characteristics curve as well as the Kappa index were employed. Based on these indices, the GARP model predicted the probability of future landslide incidences with the area under the receiver operating characteristics curve(AUC-ROC) values of 0.932, and 0.907 for training and validating datasets, respectively. In addition, Kappa values for the training and validating datasets were computed as 0.775, and 0.716, respectively. Thus, it can be concluded that the GARP algorithm can be a new but effective method for generating landslide susceptibility maps(LSMs). Furthermore, higher contribution of the lithology, distance from roads, and distance from faults was observed, while lower contribution was attributed to soil, profile curvature, and TWI factors. The introduced methodology in this paper can be suggested for other areas with similar topographical and hydrogeological characteristics for land use planning and reducing the landslide damages.展开更多
The purpose of this study was to assess the susceptibility of landslides around the area of Guizhou province based on fuzzy theory.In first instance, slope, elevation, lithology, proximity to tectonic lines, proximity...The purpose of this study was to assess the susceptibility of landslides around the area of Guizhou province based on fuzzy theory.In first instance, slope, elevation, lithology, proximity to tectonic lines, proximity to drainage and annual precipitation were taken as independent, causal factors in this study.A landslide hazard evaluation factor system was established by classifying these factors into more subclasses according to some rules.Secondly, a trapezoidal fuzzy number weighting(TFNW) approach was used to assess the importance of six causal factors to landslides in an ArcGIS environment.Thirdly, a landslide susceptibility map was created based on a weighted linear combination model.According to this susceptibility map, the study area was classified into four categories of landslide susceptibility:low, moderate, high and very high.Finally, in order to verify the results obtained, the susceptibility map and the landslide inventory map were combined in the GIS.In addition, the weighting procedure showed that TFNW is an efficient method for weighting causal landslide factors.展开更多
Landslide hazard is as the probability of occurrence of a potentially damaging landslide phenomenon within specified period of time and within a given area. The susceptibility map provides the relative spatial probabi...Landslide hazard is as the probability of occurrence of a potentially damaging landslide phenomenon within specified period of time and within a given area. The susceptibility map provides the relative spatial probability of landslides occurrence. A study is presented of the application of GIS and artificial neural network model to landslide susceptibility mapping, with particular reference to landslides on natural terrain in this paper. The method has been applied to Lantau Island, the largest outlying island within the territory of Hong Kong. A three-level neural network model was constructed and trained by the back-propagate algorithm in the geographical database of the study area. The data in the database includes digital elevation modal and its derivatives, landslides distribution and their attributes, superficial geological maps, vegetation cover, the raingauges distribution and their 14 years 5-minute observation. Based on field inspection and analysis of correlation between terrain variables and landslides frequency, lithology, vegetation cover, slope gradient, slope aspect, slope curvature, elevation, the characteristic value, the rainstorms corresponding to the landslide, and distance to drainage Une are considered to be related to landslide susceptibility in this study. The artificial neural network is then coupled with the ArcView3.2 GIS software to produce the landslide susceptibility map, which classifies the susceptibility into three levels: low, moderate, and high. The results from this study indicate that GIS coupled with artificial neural network model is a flexible and powerful approach to identify the spatial probability of hazards.展开更多
An ill-posed inverse problem in quantitative susceptibility mapping （QSM） is usually solved using a regularization and optimization solver, which is time consuming considering the three-dimensional volume data. Howe...An ill-posed inverse problem in quantitative susceptibility mapping （QSM） is usually solved using a regularization and optimization solver, which is time consuming considering the three-dimensional volume data. However, in clinical diagnosis, it is necessary to reconstruct a susceptibility map efficiently with an appropriate method. Here, a modified QSM reconstruction method called weighted total variation using split Bregman （WTVSB） is proposed. It reconstructs the susceptibility map with fast computational speed and effective artifact suppression by incorporating noise-suppressed data weighting with split Bregman iteration. The noise-suppressed data weighting is determined using the Laplacian of the calculated local field, which can prevent the noise and errors in field maps from spreading into the susceptibility inversion. The split Bregman iteration accelerates the solution of the Ll-regularized reconstruction model by utilizing a preconditioned conjugate gradient solver. In an experiment, the proposed reconstruction method is compared with truncated k-space division （TKD）, morphology enabled dipole inversion （MEDI）, total variation using the split Bregman （TVSB） method for numerical simulation, phantom and in vivo human brain data evaluated by root mean square error and mean structure similarity. Experimental results demonstrate that our proposed method can achieve better balance between accuracy and efficiency of QSM reconstruction than conventional methods, and thus facilitating clinical applications of QSM.展开更多
Earthquake induced landslides are one of the most severe geo-environmental hazards that cause enormous damage to infrastructure, property, and loss of life in Nuweiba area. This study developed a model for mapping the...Earthquake induced landslides are one of the most severe geo-environmental hazards that cause enormous damage to infrastructure, property, and loss of life in Nuweiba area. This study developed a model for mapping the earthquake-induced landslide susceptibility in Nuweiba area in Egypt with considerations of geological, geomorphological, topographical, and seismological factors. An integrated approach of remote sensing and GIS technologies were applied for that target. Several data sources including Terra SAR-X and SPOT 5 satellite imagery, topographic maps, field data, and other geospatial resources were used to model landslide susceptibility. These data were used specifically to produce important thematic layers contributing to landslide occurrences in the region. A rating scheme was developed to assign ranks for the thematic layers and weights for their classes based on their contribution in landslide susceptibility. The ranks and weights were defined based on the knowledge from field survey and authors experiences related to the study area. The landslide susceptibility map delineates the hazard zones to three relative classes of susceptibility: high, moderate, and low. Therefore, the current approach provides a way to assess landslide hazards and serves for geo-hazard planning and prediction in Nuweiba area.展开更多
Landslide susceptibility mapping of mountain roads is frequently confronted by insufficient historical landslide sample data,multicollinearity of existing evaluation index factors,and inconsistency of evaluation facto...Landslide susceptibility mapping of mountain roads is frequently confronted by insufficient historical landslide sample data,multicollinearity of existing evaluation index factors,and inconsistency of evaluation factors due to regional environmental variations.Then,a single machine learning model can easily become overfitting,thus reducing the accuracy and robustness of the evaluation model.This paper proposes a combined machine-learning model to address the issues.The landslide susceptibility in mountain roads were mapped by using factor analysis to normalize and reduce the dimensionality of the initial condition factor and generating six new combination factors as evaluation indexes.The mountain roads in the Youxi County,Fujian Province,China were used for the landslide susceptibility mapping.Three most frequently used machine learning techniques,support vector machine(SVM),random forest(RF),and artificial neural network(ANN)models,were used to model the landslide susceptibility of the study area and validate the accuracy of this evaluation index system.The global minimum variance portfolio was utilized to construct a machine learning combined model.5-fold cross-validation,statistical indexes,and AUC(Area Under Curve)values were implemented to evaluate the predictive accuracy of the landslide susceptibility model.The mean AUC values for the SVM,RF,and ANN models in the training stage were 89.2%,88.5%,and 87.9%,respectively,and 78.0%,73.7%,and 76.7%,respectively,in the validating stage.In the training and validation stages,the mean AUC values of the combined model were 92.4% and 87.1%,respectively.The combined model provides greater prediction accuracy and model robustness than one single model.展开更多
基金jointed supported by the National Natural Science Foundation of China(Nos.41920104007,41731284)。
文摘With the rapid urbanization process,ground collapses caused by anthropogenic activities occur frequently.Accurate susceptibility mapping is of great significance for disaster prevention and control.In this study,1198 ground collapse cases in Shenzhen from 2017 to 2020 were collected.Eight effective factors(elevation,relief,clay proportion,average annual precipitation,distance from water,land use type,building density,and road density)were selected to construct the evaluation index system.Ground collapse susceptibility was analyzed and mapped using the normalized frequency ratio(NFR),logistic regression(LR),and NFR-LR coupling models.Finally,the result rationality and performance of the three models were compared through frequency ratio(FR)and ROC curve.The results indicate that all three models can effectively evaluate the ground collapse susceptibility(AUC>0.7),and the NFR-LR model result is more rational and has the best performance(AUC=0.791).The very high and high susceptibility zones cover a total area of 545.68 km^(2) and involve Nanshan,Luohu,and Futian District,as well as some areas of Baoan,Guangming,and Longgang District.The ground collapses in Shenzhen mainly occurred in the built-up areas,and the greater intensity of anthropogenic activities,the more susceptible to the disaster.
基金supported by the Technological Innovation Center of Geological Disaster Prevention and ecological Restoration in the western region of the Ministry of Natural Resources opened the fund (Grant No.TICGP2023K003)the Project of Sichuan Disaster Economic Research Center (Grant No.ZHJJ2024YJS004)。
文摘Landslide susceptibility mapping(LSM) is crucial for reducing disaster risk in complex mountainous regions. This study evaluated the impact of various sampling methods on the accuracy of LSM over the next decade in Bijie City, Guizhou Province, China. Datasets were collected from 614 landslides and 500 non-landslides, and four sampling methods were proposed. Recurrent Neural Network(RNN), Gated Recurrent Unit(GRU), K-Nearest Neighbor(KNN), and Extreme Gradient Boosting(XGB) models were assessed utilising 15 metrics(Elevation, Slope, Aspect, Plan curvature, Profile curvature, Stream Power Index, Sediment Transport Index, Vector Ruggedness Measurement, Topographic Roughness Index, Lithology, Land use, Normalized Difference Vegetation Index(NDVI), Rainfall, Distance from Road, Distance from River). The results demonstrated that the GRU model combined with a 5-m sample boundary from the interior of the landslide and non-landslide areas exhibited superior performance with F1, Accuracy, and Area Under Curve(AUC) scores of 0.9700, 0.9450, and 0.8925, respectively. LSM will be projected for the next decade by coupling the Geophysical Fluid Dynamics Laboratory Earth System Model version 4(GFDLESM4) with the Shared Socioeconomic Pathway(SSP119). This study provides valuable insights into landslide risk management in landslide-prone areas.
基金funding support from the National Natural Science Foundation of China(Grant Nos.U22A20594,52079045)Hong-Zhi Cui acknowledges the financial support of the China Scholarship Council(Grant No.CSC:202206710014)for his research at Universitat Politecnica de Catalunya,Barcelona.
文摘Landslide susceptibility mapping(LSM)plays a crucial role in assessing geological risks.The current LSM techniques face a significant challenge in achieving accurate results due to uncertainties associated with regional-scale geotechnical parameters.To explore rainfall-induced LSM,this study proposes a hybrid model that combines the physically-based probabilistic model(PPM)with convolutional neural network(CNN).The PPM is capable of effectively capturing the spatial distribution of landslides by incorporating the probability of failure(POF)considering the slope stability mechanism under rainfall conditions.This significantly characterizes the variation of POF caused by parameter uncertainties.CNN was used as a binary classifier to capture the spatial and channel correlation between landslide conditioning factors and the probability of landslide occurrence.OpenCV image enhancement technique was utilized to extract non-landslide points based on the POF of landslides.The proposed model comprehensively considers physical mechanics when selecting non-landslide samples,effectively filtering out samples that do not adhere to physical principles and reduce the risk of overfitting.The results indicate that the proposed PPM-CNN hybrid model presents a higher prediction accuracy,with an area under the curve(AUC)value of 0.85 based on the landslide case of the Niangniangba area of Gansu Province,China compared with the individual CNN model(AUC=0.61)and the PPM(AUC=0.74).This model can also consider the statistical correlation and non-normal probability distributions of model parameters.These results offer practical guidance for future research on rainfall-induced LSM at the regional scale.
文摘Doline susceptibility mapping(DSM)in karst aquifer is important in terms of estimating the vulnerability of the aquifer to pollutants,estimating the infiltration rate,and infrastructures exposed to the development of dolines.In this research,doline susceptibility map was prepared in Saldaran mountain by generalized linear model(GLM)using 14 affecting parameters extracted from satellite images,digital elevation model,and geology map.Only 8 parameters have been inputted to the model which had correlation with dolines.In this regards,306 dolines were identified by the photogrammetric Unmanned Aerial Vehicles(UAV)method in 600 hectares of Salderan lands and then,these data were divided into the training(70%)and testing(30%)data for modelling.The results of DSM modeling showed that classified probability of doline occurrences in the Saldaran mountain were as follow:16.5%of the area high to very high,72%in the class of low to very low,and 5%in the moderate class.Also,locally,in Saldaran mountain,the Pirghar aquifer has the highest potential for the doline development,followed by Bagh Rostam and Sarab aquifers.Also,the precipitation,digital elevation model,Topographic Position Index,drainage density,slope,TRASP(transformed the circular aspect to a radiation index),Snow-Covered Days and vegetation cover index are of highest importance in the DSM modeling,respectively.Accurate evaluation of the model using the Receiver Operating Characteristics(ROC)curve represents a very good accuracy(AUC=0.953)of the DSM model.
基金the University of Transport Technology under the project entitled“Application of Machine Learning Algorithms in Landslide Susceptibility Mapping in Mountainous Areas”with grant number DTTD2022-16.
文摘This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.
基金funded by the National Natural Science Foundation of China(Grant Nos.42377170).
文摘Machine learning(ML)-based prediction models for mapping hazard(e.g.,landslide and debris flow)susceptibility have been widely developed in recent research.However,in some specific areas,ML models have limited application because of the uncertainties in identifying negative samples.The Parlung Tsangpo Basin exemplifies a region prone to recurrent glacial debris flows(GDFs)and is characterized by a prominent landform featuring deep gullies.Considering the limitations of the ML model,we developed and compared two combined statistical models(FA-WE and FA-IC)based on factor analysis(FA),weight of evidence(WE),and the information content(IC)method.The final GDF susceptibility maps were generated by selecting 8 most important static factors and considering the influence of precipitation.The results show that the FA-IC model has the best performance.The areas with a very high susceptibility to GDFs are primarily located in the narrow valley section upstream,on both sides of the valley in the middle and downstream of the Parlung Tsangpo River,and in the narrow valley section of each tributary.These areas encompass 86 gullies and are characterized as"narrow and steep".
基金supported by the National Natural Science Foundation of China(Project Nos.52025094,51979158)support from Shanghai Municipal Education Commission(Project No.2021-01-07-00-02-E00089).
文摘Regional landslide susceptibility mapping(LSM)is essential for risk mitigation.While deep learning algorithms are increasingly used in LSM,their extensive parameters and scarce labels(limited landslide records)pose training challenges.In contrast,classical statistical algorithms,with typically fewer parameters,are less likely to overfit,easier to train,and offer greater interpretability.Additionally,integrating physics-based and data-driven approaches can potentially improve LSM.This paper makes several contributions to enhance the practicality,interpretability,and cross-regional generalization ability of regional LSM models:(1)Two new hybrid models,composed of data-driven and physics-based modules,are proposed and compared.Hybrid ModelⅠcombines the infinite slope stability analysis(ISSA)with logistic regression,a classical statistical algorithm.Hybrid ModelⅡintegrates ISSA with a convolutional neural network,a representative of deep learning techniques.The physics-based module constructs a new explanatory factor with higher nonlinearity and reduces prediction uncertainty caused by incomplete landslide inventory by pre-selecting non-landslide samples.The data-driven module captures the rela-tion between explanatory factors and landslide inventory.(2)A step-wise deletion process is proposed to assess the importance of explanatory factors and identify the minimum necessary factors required to maintain satisfactory model performance.(3)Single-pixel and local-area samples are compared to understand the effect of pixel spatial neighborhood.(4)The impact of nonlinearity in data-driven algorithms on hybrid model performance is explored.Typical landslide-prone regions in the Three Gorges Reservoir,China,are used as the study area.The results show that,in the testing region,by using local-area samples to account for pixel spatial neighborhoods,Hybrid ModelⅠachieves roughly a 4.2%increase in the AUC.Furthermore,models with 30 m resolution land-cover data surpass those using 1000 m resolution data,showing a 5.5%improvement in AUC.The optimal set of explanatory factors includes elevation,land-cover type,and safety factor.These findings reveal the key elements to enhance regional LSM,offering valuable insights for LSM practices.
基金Project(G2022165004L)supported by the High-end Foreign Expert Introduction Program,ChinaProject(2021XM3008)supported by the Special Foundation of Postdoctoral Support Program,Chongqing,China+1 种基金Project(2018-ZL-01)supported by the Sichuan Transportation Science and Technology Project,ChinaProject(HZ2021001)supported by the Chongqing Municipal Education Commission,China。
文摘Landslide susceptibility mapping is a crucial tool for disaster prevention and management.The performance of conventional data-driven model is greatly influenced by the quality of the samples data.The random selection of negative samples results in the lack of interpretability throughout the assessment process.To address this limitation and construct a high-quality negative samples database,this study introduces a physics-informed machine learning approach,combining the random forest model with Scoops 3D,to optimize the negative samples selection strategy and assess the landslide susceptibility of the study area.The Scoops 3D is employed to determine the factor of safety value leveraging Bishop’s simplified method.Instead of conventional random selection,negative samples are extracted from the areas with a high factor of safety value.Subsequently,the results of conventional random forest model and physics-informed data-driven model are analyzed and discussed,focusing on model performance and prediction uncertainty.In comparison to conventional methods,the physics-informed model,set with a safety area threshold of 3,demonstrates a noteworthy improvement in the mean AUC value by 36.7%,coupled with a reduced prediction uncertainty.It is evident that the determination of the safety area threshold exerts an impact on both prediction uncertainty and model performance.
文摘Quantitative susceptibility mapping(QSM)is an advanced post‐processing technique in magnetic resonance imaging that offers precise measurements of tissue magnetic susceptibility with impressive spatial resolution and sensitivity.This review examines the potential of QSM as a biomarker for early detection and monitoring of amyotrophic lateral sclerosis(ALS).Since 2015,studies have consistently reported increased QSM values in the motor regions of individuals with ALS,indicating significant iron deposition.Iron accumulation is associated with dysfunction of the upper motor neurons and faster disease progression.Notably,increased QSM values were also observed in the critical subcortical areas responsible for motor function and cognitive control.However,standardizing optimized protocols,including background field removal algorithms,phase unwrapping approaches,and methods for final susceptibility map reconstruction,has the potential to enhance the consistency and reliability of QSM as an ALS biomarker.Overall,the current body of evidence strongly supports QSM in detecting iron dysregulation associated with neurodegeneration in both motor and extra‐motor regions in ALS.Furthermore,QSM's remarkable sensitivity to early pathological iron changes and its high specificity in distinguishing ALS positions make it a promising diagnostic and progression‐tracking biomarker.
文摘Rainfall-induced landslides,exacerbated by climate change,require urgent attention to identify vulnerable regions and propose effective risk mitigation measures.Extensive research underscores the significant impact of vegetation on soil properties and slope stability,emphasizing the necessity to incorporate vegetation effects into regional landslide susceptibility mapping.This review thoroughly examines research integrating vegetation into landslide susceptibility mapping,encompassing qualitative,semi-quantitative,and quantitative forecasting methods.It highlights the importance of incorporating vegetation aspects into these methods for comprehensive and accurate landslide susceptibility assessment.This review explores the diverse roles of vegetation in slope stability,covering both aggregated impacts and individual influences,including mechanical and hydrological effects on soil properties,as well as the implications of evapotranspiration and rainwater interception on slope stability.While aggregated roles are integrated into non-deterministic methods as input layers,individual roles are considered in deterministic methods.In the application of deterministic methods,it is noteworthy that a considerable number of studies primarily concentrate on the mechanical impact,particularly the reinforcement provided by root cohesion.The review also explores limitations and highlights future research prospects.In the context of mapping landslide susceptibility amid changing climatic conditions,data-driven techniques encounter challenges,while deterministic methods present their advantages.Stressing the significance of hydrological impacts,the paper recommends incorporating vegetation influences on unsaturated soil properties,including the soil water characteristic curve and soil permeability,along with pre-wetting suction due to evapotranspiration and potential rainwater interception.
文摘The present study aims to develop two hybrid models to optimize the factors and enhance the predictive ability of the landslide susceptibility models.For this,a landslide inventory map was created with 406 historical landslides and 2030 non-landslide points,which was randomly divided into two datasets for model training(70%)and model testing(30%).22 factors were initially selected to establish a landslide factor database.We applied the GeoDetector and recursive feature elimination method(RFE)to address factor optimization to reduce information redundancy and collinearity in the data.Thereafter,the frequency ratio method,multicollinearity test,and interactive detector were used to analyze and evaluate the optimized factors.Subsequently,the random forest(RF)model was used to create a landslide susceptibility map with original and optimized factors.The resultant hybrid models GeoDetector-RF and RFE-RF were evaluated and compared by the area under the receiver operating characteristic curve(AUC)and accuracy.The accuracy of the two hybrid models(0.868 for GeoDetector-RF and 0.869 for RFE-RF)were higher than that of the RF model(0.860),indicating that the hybrid models with factor optimization have high reliability and predictability.Both RFE-RF GeoDetector-RF had higher AUC values,respectively 0.863 and 0.860,than RF(0.853).These results confirm the ability of factor optimization methods to improve the performance of landslide susceptibility models.
基金supported by the National Key Technology R&D Program(Grant No. 2011BAK12B01)the Young Foundation of National Natural Science of China(Grant No.41202210)+1 种基金the Education Department Innovation Research Team Program(Grant No.IRT0812)the Young Foundation of Chengdu University of Technology and the Education Department of Sichuan Province (Grant Nos.2010QJ15 and 11ZB262)
文摘The Ms 8.0 May 12,2008 Wenchuan earthquake triggered tens of thousands of landslides.The widespread landslides have caused serious casualties and property losses,and posed a great threat to post-earthquake reconstruction.A spatial database,inventoried 43,842 landslides with a total area of 632 km 2,was developed by interpretation of multi-resolution remote sensing images.The landslides can be classified into three categories:swallow,disrupted slides and falls;deep-seated slides and falls,and rock avalanches.The correlation between landslides distribution and the influencing parameters including distance from co-seismic fault,lithology,slope gradient,elevation,peak ground acceleration(PGA) and distance from drainage were analyzed.The distance from co-seismic fault was the most significant parameter followed by slope gradient and PGA was the least significant one.A logistic regression model combined with bivariate statistical analysis(BSA) was adopted for landslide susceptibility mapping.The study area was classified into five categories of landslide susceptibility:very low,low,medium,high and very high.92.0% of the study area belongs to low and very low categories with corresponding 9.0% of the total inventoried landslides.Medium susceptible zones make up 4.2% of the area with 17.7% of the total landslides.The rest of the area was classified into high and very high categories,which makes up 3.9% of the area with corresponding 73.3% of the total landslides.Although the susceptibility map can reveal the likelihood of future landslides and debris flows,and it is helpful for the rebuilding process and future zoning issues.
基金the National Natural Science Foundation of China(No.41807498)the National Key Research and Development Program of China(No.2018YFC1505501)the Humanities and Social Sciences Foundation of the Ministry of Education of China(No.20XJAZH002)。
文摘Numerous researches have been published on the application of landslide susceptibility assessment models;however,they were only applied in the same areas as the models were originated,the effect of applying the models to other areas than the origin of the models has not been explored.This study is purposed to develop an optimized random forest(RF)model with best ratios of positive-to-negative cells and 10-fold cross-validation for landslide susceptibility mapping(LSM),and then explore its generalization ability not only in the area where the model is originated but also in area other than the origin of the model.Two typical counties(Fengjie County and Wushan County)in the Three Gorges Reservoir area,China,which have the same terrain and geological conditions,were selected as an example.To begin with,landslide inventory was prepared based on field investigations,satellite images,and historical records,and 1522 landslides were then identified in Fengjie County.22 landslide-conditioning factors under the influence of topography,geology,environmental conditions,and human activities were prepared.Then,combined with 10-fold cross-validation,three typical ratios of positive-to-negative cells,i.e.,1:1,1:5,and 1:10,were adopted for comparative analyses.An optimized RF model(Fengjie-based model)with the best ratios of positive-to-negative cells and 10-fold cross-validation was constructed.Finally,the Fengjie-based model was applied to Fengjie County and Wushan County,and the confusion matrix and area under the receiver operating characteristic(ROC)curve value(AUC)were used to estimate the accuracy.The Fengjie-based model delivered high stability and predictive capability in Fengjie County,indicating a great generalization ability of the model to the area where the model is originated.The LSM in Wushan County generated by the Fengjie-based model had a reasonable reference value,indicating the Fengjiebased model had a great generalization ability in area other than the origin of the model.The Fengjiebased model in this study could be applied in other similar areas/countries with the same terrain and geological conditions,and a LSM may be generated without collecting landslide information for modeling,so as to reduce workload and improve efficiency in practice.
基金supported by the National Basic Research Program"973"Project of the Ministry of Science and Technology of the People’s Republic of China(GrantNo.2013CB733202)theNational Key Technology R&D Program(Grant No.2011BAK12B01)+1 种基金the Young Foundation of NationalNatural Science of China(Grant No.41202210)the National Science Fund for DistinguishedYoung Scholars(Grant No.41225011)
文摘The primary objective of landslide susceptibility mapping is the prediction of potential landslides in landslide-prone areas. The predictive power of a landslide susceptibility mapping model could be tested in an adjacent area of similar geo- environmental conditions to find out the reliability. Both the 2oo8 Wenchuan Earthquake and the 2o13 Lushan Earthquake occurred in the Longmen Mountain seismic zone, with similar topographical and geological conditions. The two earthquakes are both featured by thrust fault and similar seismic mechanism This paper adopted the susceptibility mapping model of co-seismic landslides triggered by Wenchuan earthquake to predict the spatial distribution of landslides induced by Lushan earthquake. Six influencing parameters were taken into consideration： distance from the seismic fault, slope gradient, lithology, distance from drainage, elevation and Peak Ground Acceleration （PGA）. The preliminary results suggested that the zones with high susceptibility of co- seismic landslides were mainly distributed in the mountainous areas of Lushan, Baoxing and Tianquan counties. The co-seismic landslide susceptibility map was completed in two days after the quake and sent to the field investigators to provide guidance for rescue and relief work. The predictive power of the susceptibility map was validated by ROC curve analysis method using 2o37 co-seismic landslides in the epicenter area. The AUC value of o.71o indicated that the susceptibility model derived from Wenchuan Earthquake landslides showed good accuracy inpredicting the landslides triggered by Lushan earthquake.
基金Science and Research Branch, Islamic Azad University
文摘Investigation on landslide phenomenon is necessary for understanding and delineating the landslide prone and safer places for different land use practices. On this basis, a new model known as genetic algorithm for the rule set production was applied in order to assess its efficacy to obtain a better result and a more precise landslide susceptibility map in Klijanerestagh area of Iran. This study considered twelve landslide conditioning factors(LCF) like altitude, slope, aspect, plan curvature, profile curvature, topographic wetness index(TWI), distance from rivers, faults, and roads, land use/cover, and lithology. For modeling purpose, the Genetic Algorithm for the Rule Set Production(GARP) algorithm was applied in order to produce the landslide susceptibility map. Finally, to evaluate the efficacy of the GARP model, receiver operating characteristics curve as well as the Kappa index were employed. Based on these indices, the GARP model predicted the probability of future landslide incidences with the area under the receiver operating characteristics curve(AUC-ROC) values of 0.932, and 0.907 for training and validating datasets, respectively. In addition, Kappa values for the training and validating datasets were computed as 0.775, and 0.716, respectively. Thus, it can be concluded that the GARP algorithm can be a new but effective method for generating landslide susceptibility maps(LSMs). Furthermore, higher contribution of the lithology, distance from roads, and distance from faults was observed, while lower contribution was attributed to soil, profile curvature, and TWI factors. The introduced methodology in this paper can be suggested for other areas with similar topographical and hydrogeological characteristics for land use planning and reducing the landslide damages.
基金Project 200331880201 supported by the West Project of the Ministry of Communication of China
文摘The purpose of this study was to assess the susceptibility of landslides around the area of Guizhou province based on fuzzy theory.In first instance, slope, elevation, lithology, proximity to tectonic lines, proximity to drainage and annual precipitation were taken as independent, causal factors in this study.A landslide hazard evaluation factor system was established by classifying these factors into more subclasses according to some rules.Secondly, a trapezoidal fuzzy number weighting(TFNW) approach was used to assess the importance of six causal factors to landslides in an ArcGIS environment.Thirdly, a landslide susceptibility map was created based on a weighted linear combination model.According to this susceptibility map, the study area was classified into four categories of landslide susceptibility:low, moderate, high and very high.Finally, in order to verify the results obtained, the susceptibility map and the landslide inventory map were combined in the GIS.In addition, the weighting procedure showed that TFNW is an efficient method for weighting causal landslide factors.
基金National Natural Science Foundation of China, No.49971066.
文摘Landslide hazard is as the probability of occurrence of a potentially damaging landslide phenomenon within specified period of time and within a given area. The susceptibility map provides the relative spatial probability of landslides occurrence. A study is presented of the application of GIS and artificial neural network model to landslide susceptibility mapping, with particular reference to landslides on natural terrain in this paper. The method has been applied to Lantau Island, the largest outlying island within the territory of Hong Kong. A three-level neural network model was constructed and trained by the back-propagate algorithm in the geographical database of the study area. The data in the database includes digital elevation modal and its derivatives, landslides distribution and their attributes, superficial geological maps, vegetation cover, the raingauges distribution and their 14 years 5-minute observation. Based on field inspection and analysis of correlation between terrain variables and landslides frequency, lithology, vegetation cover, slope gradient, slope aspect, slope curvature, elevation, the characteristic value, the rainstorms corresponding to the landslide, and distance to drainage Une are considered to be related to landslide susceptibility in this study. The artificial neural network is then coupled with the ArcView3.2 GIS software to produce the landslide susceptibility map, which classifies the susceptibility into three levels: low, moderate, and high. The results from this study indicate that GIS coupled with artificial neural network model is a flexible and powerful approach to identify the spatial probability of hazards.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.11474236,81671674,and 11775184)the Science and Technology Project of Fujian Province,China(Grant No.2016Y0078)
文摘An ill-posed inverse problem in quantitative susceptibility mapping （QSM） is usually solved using a regularization and optimization solver, which is time consuming considering the three-dimensional volume data. However, in clinical diagnosis, it is necessary to reconstruct a susceptibility map efficiently with an appropriate method. Here, a modified QSM reconstruction method called weighted total variation using split Bregman （WTVSB） is proposed. It reconstructs the susceptibility map with fast computational speed and effective artifact suppression by incorporating noise-suppressed data weighting with split Bregman iteration. The noise-suppressed data weighting is determined using the Laplacian of the calculated local field, which can prevent the noise and errors in field maps from spreading into the susceptibility inversion. The split Bregman iteration accelerates the solution of the Ll-regularized reconstruction model by utilizing a preconditioned conjugate gradient solver. In an experiment, the proposed reconstruction method is compared with truncated k-space division （TKD）, morphology enabled dipole inversion （MEDI）, total variation using the split Bregman （TVSB） method for numerical simulation, phantom and in vivo human brain data evaluated by root mean square error and mean structure similarity. Experimental results demonstrate that our proposed method can achieve better balance between accuracy and efficiency of QSM reconstruction than conventional methods, and thus facilitating clinical applications of QSM.
基金the Egyptian Ministry of Higher Education and Scientific Research
文摘Earthquake induced landslides are one of the most severe geo-environmental hazards that cause enormous damage to infrastructure, property, and loss of life in Nuweiba area. This study developed a model for mapping the earthquake-induced landslide susceptibility in Nuweiba area in Egypt with considerations of geological, geomorphological, topographical, and seismological factors. An integrated approach of remote sensing and GIS technologies were applied for that target. Several data sources including Terra SAR-X and SPOT 5 satellite imagery, topographic maps, field data, and other geospatial resources were used to model landslide susceptibility. These data were used specifically to produce important thematic layers contributing to landslide occurrences in the region. A rating scheme was developed to assign ranks for the thematic layers and weights for their classes based on their contribution in landslide susceptibility. The ranks and weights were defined based on the knowledge from field survey and authors experiences related to the study area. The landslide susceptibility map delineates the hazard zones to three relative classes of susceptibility: high, moderate, and low. Therefore, the current approach provides a way to assess landslide hazards and serves for geo-hazard planning and prediction in Nuweiba area.
基金the financial support from the National Natural Science Foundation of China(No.U2005205,No.42007235,No.41972268)the Science and Technology Innovation Platform Project of Fuzhou Science and Technology Bureau(No.2021-P-032)。
文摘Landslide susceptibility mapping of mountain roads is frequently confronted by insufficient historical landslide sample data,multicollinearity of existing evaluation index factors,and inconsistency of evaluation factors due to regional environmental variations.Then,a single machine learning model can easily become overfitting,thus reducing the accuracy and robustness of the evaluation model.This paper proposes a combined machine-learning model to address the issues.The landslide susceptibility in mountain roads were mapped by using factor analysis to normalize and reduce the dimensionality of the initial condition factor and generating six new combination factors as evaluation indexes.The mountain roads in the Youxi County,Fujian Province,China were used for the landslide susceptibility mapping.Three most frequently used machine learning techniques,support vector machine(SVM),random forest(RF),and artificial neural network(ANN)models,were used to model the landslide susceptibility of the study area and validate the accuracy of this evaluation index system.The global minimum variance portfolio was utilized to construct a machine learning combined model.5-fold cross-validation,statistical indexes,and AUC(Area Under Curve)values were implemented to evaluate the predictive accuracy of the landslide susceptibility model.The mean AUC values for the SVM,RF,and ANN models in the training stage were 89.2%,88.5%,and 87.9%,respectively,and 78.0%,73.7%,and 76.7%,respectively,in the validating stage.In the training and validation stages,the mean AUC values of the combined model were 92.4% and 87.1%,respectively.The combined model provides greater prediction accuracy and model robustness than one single model.
