China is one of the countries where landslides caused the most fatalities in the last decades. The threat that landslide disasters pose to people might even be greater in the future, due to climate change and the incr...China is one of the countries where landslides caused the most fatalities in the last decades. The threat that landslide disasters pose to people might even be greater in the future, due to climate change and the increasing urbanization of mountainous areas. A reliable national-scale rainfall induced landslide susceptibility model is therefore of great relevance in order to identify regions more and less prone to landsliding as well as to develop suitable risk mitigating strategies. However, relying on imperfect landslide data is inevitable when modelling landslide susceptibility for such a large research area. The purpose of this study is to investigate the influence of incomplete landslide data on national scale statistical landslide susceptibility modeling for China. In this context, it is aimed to explore the benefit of mixed effects modelling to counterbalance associated bias propagations. Six influencing factors including lithology, slope,soil moisture index, mean annual precipitation, land use and geological environment regions were selected based on an initial exploratory data analysis. Three sets of influencing variables were designed to represent different solutions to deal with spatially incomplete landslide information: Set 1(disregards the presence of incomplete landslide information), Set 2(excludes factors related to the incompleteness of landslide data), Set 3(accounts for factors related to the incompleteness via random effects). The variable sets were then introduced in a generalized additive model(GAM: Set 1 and Set 2) and a generalized additive mixed effect model(GAMM: Set 3) to establish three national-scale statistical landslide susceptibility models: models 1, 2 and 3. The models were evaluated using the area under the receiver operating characteristics curve(AUROC) given by spatially explicit and non-spatial cross-validation. The spatial prediction pattern produced by the models were also investigated. The results show that the landslide inventory incompleteness had a substantial impact on the outcomes of the statistical landslide susceptibility models. The cross-validation results provided evidence that the three established models performed well to predict model-independent landslide information with median AUROCs ranging from 0.8 to 0.9.However, although Model 1 reached the highest AUROCs within non-spatial cross-validation(median of 0.9), it was not associated with the most plausible representation of landslide susceptibility. The Model 1 modelling results were inconsistent with geomorphological process knowledge and reflected a large extent the underlying data bias. The Model 2 susceptibility maps provided a less biased picture of landslide susceptibility. However, a lower predicted likelihood of landslide occurrence still existed in areas known to be underrepresented in terms of landslide data(e.g., the Kuenlun Mountains in the northern Tibetan Plateau). The non-linear mixed-effects model(Model 3) reduced the impact of these biases best by introducing bias-describing variables as random effects. Among the three models, Model 3 was selected as the best national-scale susceptibility model for China as it produced the most plausible portray of rainfall induced landslide susceptibility and the highest spatially explicit predictive performance(median AUROC of spatial cross validation 0.84) compared to the other two models(median AUROCs of 0.81 and 0.79, respectively). We conclude that ignoring landslide inventory-based incompleteness can entail misleading modelling results and that the application of non-linear mixed-effect models can reduce the propagation of such biases into the final results for very large areas.展开更多
In recent decades, data-driven landslide susceptibility models(Dd LSM), which are based on statistical or machine learning approaches, have become popular to estimate the relative spatial probability of landslide occu...In recent decades, data-driven landslide susceptibility models(Dd LSM), which are based on statistical or machine learning approaches, have become popular to estimate the relative spatial probability of landslide occurrence. The available literature is composed of a wealth of published studies and that has identified a large variety of challenges and innovations in this field. This review presents a comprehensive up-to-date overview focusing on the topic of Dd LSM. This research begins with an introduction of the theoretical aspects of Dd LSM research and is followed by an in-depth bibliometric analysis of 2585 publications. This analysis is based on the Web of Science, Clarivate Analytics database and provides insights into the transient characteristics and research trends within published spatial landslide assessments. Following the bibliometric analysis, a more detailed review of the most recent publications from 1985 to 2020 is given. A variety of different criteria are explored in detail, including research design, study area extent,inventory characteristics, classification algorithms, predictors utilized, and validation technique performed. This section, dealing with a quantitativeoriented review expands the time-frame of the review publication done by Reichenbach et al. in 2018 by also accounting for the four years, 2017-2020. The originality of this research is acknowledged by combining together:(a) a recap of important theoretical aspects of Dd LSM;(b) a bibliometric analysis on the topic;(c) a quantitative-oriented review of relevant publications;and(d) a systematic summary of the findings, indicating important aspects and potential developments related to the Dd LSM research topic. The results show that Dd LSM are used within a wide range of applications with study area extents ranging from a few kilometers to national and even continental scales. In more than 70% of publications, a combination of the predictors, slope angle, aspect and geology are used. Simple classifiers, such as, logistic regression or approaches based on frequency ratio are still popular, despite the upcoming trend of applying machine learning algorithms. When analyzing validation techniques, 38% of the publications were not clear about the validation method used. Within the studies that included validation techniques, the AUROC was the most popular validation metric, being used accounting for 44% of the studies. Finally, it can be concluded that the application of new classification techniques is often cited as a main research scope, even though the most relevant innovation could also lie in tackling data-quality issues and research designs adaptations to fit the input data particularities in order to improve prediction quality.展开更多
In order to reach the designated final water level of 175 m, there were three impoundment stages in the Three Gorges Reservoir, with water levels of 135 m, 156 m and 175 m. Baishuihe landslide in the Reservoir was cho...In order to reach the designated final water level of 175 m, there were three impoundment stages in the Three Gorges Reservoir, with water levels of 135 m, 156 m and 175 m. Baishuihe landslide in the Reservoir was chosen to analyze its displacement characteristics and displacement variability at the different stages. Based on monitoring data, the landslide displacement was mainly influenced by rainfall and drawdown of the reservoir water level. However, the magnitude of the rise and drawdown of the water level after the reservoir water level reached 175 m did not accelerate landslide displacement. The prediction of landslide displacement for active landslides is very important for landslide risk management. The time series of cumulative displacement was divided into a trend term and a periodic term using the Hodrick-Prescott(HP) filter method. The polynomial model was used to predict the trend term. The extreme learning machine(ELM) and least squares support vector machine(LS-SVM) were chosen to predict theperiodic term. In the prediction model for the periodic term, input variables based on the effects of rainfall and reservoir water level in landslide displacement were selected using grey relational analysis. Based on the results, the prediction precision of ELM is better than that of LS-SVM for predicting landslide displacement. The method for predicting landslide displacement could be applied by relevant authorities in making landslide emergency plans in the future.展开更多
andslide risk analysis is one of the primary studies providing essential instructions to the subsequent risk management process. The quantification of tangible and intangible potential losses is a critical step becau...andslide risk analysis is one of the primary studies providing essential instructions to the subsequent risk management process. The quantification of tangible and intangible potential losses is a critical step because it provides essential data upon which judgments can be made and policy can be formulated. This study aims at quantifying direct economic losses from debris flows at a medium scale in the study area in Italian Central Alps. Available hazard maps were the main inputs of this study. These maps were overlaid with information concerning elements at risk and their economic value. Then, a combination of both market and construction values was used to obtain estimates of future economic losses. As a result, two direct economic risk maps were prepared together with risk curves, useful to summarize expected monetary damage against the respective hazard probability. Afterwards, a qualitative risk map derived using a risk matrix officially provided by the set of laws issued by the regional government, was prepared. The results delimit areas of high economic as well as strategic importance which might be affected by debris flows in the future. Aside from limitations and inaccuracies inherently included in risk analysis process, identification of high risk areas allows local authorities to focus their attention on the “hot-spots”, where important consequences may arise and local （large） scale analysis needs to be performed with more precise cost-effectiveness ratio. The risk maps can be also used by the local authorities to increase population’s adaptive capacity in the disaster prevention process.展开更多
Sagarmatha National Park and Buffer Zone (SNPBZ) in the Everest region in Nepal is among the most popular destinations for trekking tourism in Nepal. The dramatic growth of the tourism industry has increased pressures...Sagarmatha National Park and Buffer Zone (SNPBZ) in the Everest region in Nepal is among the most popular destinations for trekking tourism in Nepal. The dramatic growth of the tourism industry has increased pressures on the environment and the National Park is heavily affected by the rapidly growing waste issue. Besides, major mass movements play an important role in the Himalaya and have been observed in SNPBZ. Also, seasonal monsoon floods, debris flows, rock falls, landslides and the creation of glacial lake outburst floods are frequently occurring in the region. This paper explores the reciprocal interactions between the geo-environment and solid waste management in Everest’s SNPBZ. Therefore, geological characteristics and geomorphological processes, especially the two large rockslides in Lukla and Khumjung, as well as their consequences for rock quality, climatic and hydrologic conditions, are analyzed and simultaneously connected to the rapidly growing tourism-induced waste issue. Rockslide material shows high porosity and permeability. Thus, we argue that rockslide facies are particularly vulnerable to contamination by waste water and washed out agricultural fertilizers, which pose threats to the population especially in Namche Bazaar but probably also in Lukla. Also, the landfill sites are often affected by geomorphological processes and may consequently contaminate surface and ground water. Results highlight that regional infrastructure planning of landfill sites often collides with the natural features of the geo-environment and often causes harm to human health and the environment. The implications of the results can be applied to similar areas (such as Marsyandi Valley, Kali Gandaki Valley) with special geological characteristics and rapidly growing waste issues.展开更多
The application of ensemble learning models has been continuously improved in recent landslide susceptibility research,but most studies have no unified ensemble framework.Moreover,few papers have discussed the applica...The application of ensemble learning models has been continuously improved in recent landslide susceptibility research,but most studies have no unified ensemble framework.Moreover,few papers have discussed the applicability of the ensemble learning model in landslide susceptibility mapping at the township level.This study aims at defining a robust ensemble framework that can become the benchmark method for future research dealing with the comparison of different ensemble models.For this purpose,the present work focuses on three different basic classifiers:decision tree(DT),support vector machine(SVM),and multi-layer perceptron neural network model(MLPNN)and two homogeneous ensemble models such as random forest(RF)and extreme gradient boosting(XGBoost).The hierarchical construction of deep ensemble relied on two leading ensemble technologies(i.e.,homogeneous/heterogeneous model ensemble and bagging,boosting,stacking ensemble strategy)to provide a more accurate and effective spatial probability of landslide occurrence.The selected study area is Dazhou town,located in the Jurassic red-strata area in the Three Gorges Reservoir Area of China,which is a strategic economic area currently characterized by widespread landslide risk.Based on a long-term field investigation,the inventory counting thirty-three slow-moving landslide polygons was drawn.The results show that the ensemble models do not necessarily perform better;for instance,the Bagging based DT-SVM-MLPNNXGBoost model performed worse than the single XGBoost model.Amongst the eleven tested models,the Stacking based RF-XGBoost model,which is a homogeneous model based on bagging,boosting,and stacking ensemble,showed the highest capability of predicting the landslide-affected areas.Besides,the factor behaviors of DT,SVM,MLPNN,RF and XGBoost models reflected the characteristics of slow-moving landslides in the Three Gorges reservoir area,wherein unfavorable lithological conditions and intense human engineering activities(i.e.,reservoir water level fluctuation,residential area construction,and farmland development)are proven to be the key triggers.The presented approach could be used for landslide spatial occurrence prediction in similar regions and other fields.展开更多
基金This work was supported primarily by the National Key Research and Development Program of China(Grant Nos.2016YFA0602403,2017YFC1502505)the National Natural Science Funds(Grant No.41271544)+1 种基金the Startup Foundation for Introducing Talent of NUISTthe Second Tibetan Plateau Scientific Expedition and Research Program(Grant Nos.2019QZKK0906,2019QZKK0606)。
文摘China is one of the countries where landslides caused the most fatalities in the last decades. The threat that landslide disasters pose to people might even be greater in the future, due to climate change and the increasing urbanization of mountainous areas. A reliable national-scale rainfall induced landslide susceptibility model is therefore of great relevance in order to identify regions more and less prone to landsliding as well as to develop suitable risk mitigating strategies. However, relying on imperfect landslide data is inevitable when modelling landslide susceptibility for such a large research area. The purpose of this study is to investigate the influence of incomplete landslide data on national scale statistical landslide susceptibility modeling for China. In this context, it is aimed to explore the benefit of mixed effects modelling to counterbalance associated bias propagations. Six influencing factors including lithology, slope,soil moisture index, mean annual precipitation, land use and geological environment regions were selected based on an initial exploratory data analysis. Three sets of influencing variables were designed to represent different solutions to deal with spatially incomplete landslide information: Set 1(disregards the presence of incomplete landslide information), Set 2(excludes factors related to the incompleteness of landslide data), Set 3(accounts for factors related to the incompleteness via random effects). The variable sets were then introduced in a generalized additive model(GAM: Set 1 and Set 2) and a generalized additive mixed effect model(GAMM: Set 3) to establish three national-scale statistical landslide susceptibility models: models 1, 2 and 3. The models were evaluated using the area under the receiver operating characteristics curve(AUROC) given by spatially explicit and non-spatial cross-validation. The spatial prediction pattern produced by the models were also investigated. The results show that the landslide inventory incompleteness had a substantial impact on the outcomes of the statistical landslide susceptibility models. The cross-validation results provided evidence that the three established models performed well to predict model-independent landslide information with median AUROCs ranging from 0.8 to 0.9.However, although Model 1 reached the highest AUROCs within non-spatial cross-validation(median of 0.9), it was not associated with the most plausible representation of landslide susceptibility. The Model 1 modelling results were inconsistent with geomorphological process knowledge and reflected a large extent the underlying data bias. The Model 2 susceptibility maps provided a less biased picture of landslide susceptibility. However, a lower predicted likelihood of landslide occurrence still existed in areas known to be underrepresented in terms of landslide data(e.g., the Kuenlun Mountains in the northern Tibetan Plateau). The non-linear mixed-effects model(Model 3) reduced the impact of these biases best by introducing bias-describing variables as random effects. Among the three models, Model 3 was selected as the best national-scale susceptibility model for China as it produced the most plausible portray of rainfall induced landslide susceptibility and the highest spatially explicit predictive performance(median AUROC of spatial cross validation 0.84) compared to the other two models(median AUROCs of 0.81 and 0.79, respectively). We conclude that ignoring landslide inventory-based incompleteness can entail misleading modelling results and that the application of non-linear mixed-effect models can reduce the propagation of such biases into the final results for very large areas.
基金support to the first author from CNPq,the National Council of Technological and Scientific Development—Brazil(Process number 234815/2014-0)。
文摘In recent decades, data-driven landslide susceptibility models(Dd LSM), which are based on statistical or machine learning approaches, have become popular to estimate the relative spatial probability of landslide occurrence. The available literature is composed of a wealth of published studies and that has identified a large variety of challenges and innovations in this field. This review presents a comprehensive up-to-date overview focusing on the topic of Dd LSM. This research begins with an introduction of the theoretical aspects of Dd LSM research and is followed by an in-depth bibliometric analysis of 2585 publications. This analysis is based on the Web of Science, Clarivate Analytics database and provides insights into the transient characteristics and research trends within published spatial landslide assessments. Following the bibliometric analysis, a more detailed review of the most recent publications from 1985 to 2020 is given. A variety of different criteria are explored in detail, including research design, study area extent,inventory characteristics, classification algorithms, predictors utilized, and validation technique performed. This section, dealing with a quantitativeoriented review expands the time-frame of the review publication done by Reichenbach et al. in 2018 by also accounting for the four years, 2017-2020. The originality of this research is acknowledged by combining together:(a) a recap of important theoretical aspects of Dd LSM;(b) a bibliometric analysis on the topic;(c) a quantitative-oriented review of relevant publications;and(d) a systematic summary of the findings, indicating important aspects and potential developments related to the Dd LSM research topic. The results show that Dd LSM are used within a wide range of applications with study area extents ranging from a few kilometers to national and even continental scales. In more than 70% of publications, a combination of the predictors, slope angle, aspect and geology are used. Simple classifiers, such as, logistic regression or approaches based on frequency ratio are still popular, despite the upcoming trend of applying machine learning algorithms. When analyzing validation techniques, 38% of the publications were not clear about the validation method used. Within the studies that included validation techniques, the AUROC was the most popular validation metric, being used accounting for 44% of the studies. Finally, it can be concluded that the application of new classification techniques is often cited as a main research scope, even though the most relevant innovation could also lie in tackling data-quality issues and research designs adaptations to fit the input data particularities in order to improve prediction quality.
基金funded by National Key R&D Program of China (No.2018YFC0809400)National Natural Science Foundation of China (No. 41772310 and No.41842062)China Geological Survey Foundation (No.DD20190641)
文摘In order to reach the designated final water level of 175 m, there were three impoundment stages in the Three Gorges Reservoir, with water levels of 135 m, 156 m and 175 m. Baishuihe landslide in the Reservoir was chosen to analyze its displacement characteristics and displacement variability at the different stages. Based on monitoring data, the landslide displacement was mainly influenced by rainfall and drawdown of the reservoir water level. However, the magnitude of the rise and drawdown of the water level after the reservoir water level reached 175 m did not accelerate landslide displacement. The prediction of landslide displacement for active landslides is very important for landslide risk management. The time series of cumulative displacement was divided into a trend term and a periodic term using the Hodrick-Prescott(HP) filter method. The polynomial model was used to predict the trend term. The extreme learning machine(ELM) and least squares support vector machine(LS-SVM) were chosen to predict theperiodic term. In the prediction model for the periodic term, input variables based on the effects of rainfall and reservoir water level in landslide displacement were selected using grey relational analysis. Based on the results, the prediction precision of ELM is better than that of LS-SVM for predicting landslide displacement. The method for predicting landslide displacement could be applied by relevant authorities in making landslide emergency plans in the future.
基金supported by the Marie Curie Research and Training Network "Mountain Risks" funded by the European Commission (2007–2010, Contract MCRTN-35098).
文摘andslide risk analysis is one of the primary studies providing essential instructions to the subsequent risk management process. The quantification of tangible and intangible potential losses is a critical step because it provides essential data upon which judgments can be made and policy can be formulated. This study aims at quantifying direct economic losses from debris flows at a medium scale in the study area in Italian Central Alps. Available hazard maps were the main inputs of this study. These maps were overlaid with information concerning elements at risk and their economic value. Then, a combination of both market and construction values was used to obtain estimates of future economic losses. As a result, two direct economic risk maps were prepared together with risk curves, useful to summarize expected monetary damage against the respective hazard probability. Afterwards, a qualitative risk map derived using a risk matrix officially provided by the set of laws issued by the regional government, was prepared. The results delimit areas of high economic as well as strategic importance which might be affected by debris flows in the future. Aside from limitations and inaccuracies inherently included in risk analysis process, identification of high risk areas allows local authorities to focus their attention on the “hot-spots”, where important consequences may arise and local （large） scale analysis needs to be performed with more precise cost-effectiveness ratio. The risk maps can be also used by the local authorities to increase population’s adaptive capacity in the disaster prevention process.
文摘Sagarmatha National Park and Buffer Zone (SNPBZ) in the Everest region in Nepal is among the most popular destinations for trekking tourism in Nepal. The dramatic growth of the tourism industry has increased pressures on the environment and the National Park is heavily affected by the rapidly growing waste issue. Besides, major mass movements play an important role in the Himalaya and have been observed in SNPBZ. Also, seasonal monsoon floods, debris flows, rock falls, landslides and the creation of glacial lake outburst floods are frequently occurring in the region. This paper explores the reciprocal interactions between the geo-environment and solid waste management in Everest’s SNPBZ. Therefore, geological characteristics and geomorphological processes, especially the two large rockslides in Lukla and Khumjung, as well as their consequences for rock quality, climatic and hydrologic conditions, are analyzed and simultaneously connected to the rapidly growing tourism-induced waste issue. Rockslide material shows high porosity and permeability. Thus, we argue that rockslide facies are particularly vulnerable to contamination by waste water and washed out agricultural fertilizers, which pose threats to the population especially in Namche Bazaar but probably also in Lukla. Also, the landfill sites are often affected by geomorphological processes and may consequently contaminate surface and ground water. Results highlight that regional infrastructure planning of landfill sites often collides with the natural features of the geo-environment and often causes harm to human health and the environment. The implications of the results can be applied to similar areas (such as Marsyandi Valley, Kali Gandaki Valley) with special geological characteristics and rapidly growing waste issues.
基金This research was funded by the National Natural Science Foundation of China(Grant No.41877525)the National Natural Science Foundation of China(Grant No.41601563)。
文摘The application of ensemble learning models has been continuously improved in recent landslide susceptibility research,but most studies have no unified ensemble framework.Moreover,few papers have discussed the applicability of the ensemble learning model in landslide susceptibility mapping at the township level.This study aims at defining a robust ensemble framework that can become the benchmark method for future research dealing with the comparison of different ensemble models.For this purpose,the present work focuses on three different basic classifiers:decision tree(DT),support vector machine(SVM),and multi-layer perceptron neural network model(MLPNN)and two homogeneous ensemble models such as random forest(RF)and extreme gradient boosting(XGBoost).The hierarchical construction of deep ensemble relied on two leading ensemble technologies(i.e.,homogeneous/heterogeneous model ensemble and bagging,boosting,stacking ensemble strategy)to provide a more accurate and effective spatial probability of landslide occurrence.The selected study area is Dazhou town,located in the Jurassic red-strata area in the Three Gorges Reservoir Area of China,which is a strategic economic area currently characterized by widespread landslide risk.Based on a long-term field investigation,the inventory counting thirty-three slow-moving landslide polygons was drawn.The results show that the ensemble models do not necessarily perform better;for instance,the Bagging based DT-SVM-MLPNNXGBoost model performed worse than the single XGBoost model.Amongst the eleven tested models,the Stacking based RF-XGBoost model,which is a homogeneous model based on bagging,boosting,and stacking ensemble,showed the highest capability of predicting the landslide-affected areas.Besides,the factor behaviors of DT,SVM,MLPNN,RF and XGBoost models reflected the characteristics of slow-moving landslides in the Three Gorges reservoir area,wherein unfavorable lithological conditions and intense human engineering activities(i.e.,reservoir water level fluctuation,residential area construction,and farmland development)are proven to be the key triggers.The presented approach could be used for landslide spatial occurrence prediction in similar regions and other fields.
