Slope units are divided according to the real topography and have clear geological characteristics,making them ideal units for evaluating the susceptibility to geological disasters.Based on the results of automaticall...Slope units are divided according to the real topography and have clear geological characteristics,making them ideal units for evaluating the susceptibility to geological disasters.Based on the results of automatically and manually corrected hydrological slope unit division,the Longhua District,Shenzhen City,Guangdong Province,was selected as the study area.A total of 15 influencing factors,namely Fluctuation,slope,slope aspect,curvature,topographic witness index(TWI),stream power index(SPI),topographic roughness index(TRI),annual average rainfall,distance to water system,engineering rock group,distance to fault,land use,normalized difference vegetation index(NDVI),nighttime light,and distance to road,were selected as evaluation indicators.The information volume model(IV)and random points were used to select non-geological disaster units,and then the random forest model(RF)was used to evaluate the susceptibility to geological disasters.The automatic slope unit and the hydrological slope unit were compared and analyzed in the random forest and information volume random forest models.The results show that the area under the curve(AUC)values of the automatic slope unit evaluation results are 0.931 for the IV-RF model and 0.716 for the RF model,which are 0.6%(IV-RF model)and 1.9%(RF model)higher than those for the hydrological slope unit.Based on a comparison of the evaluation methods based on the two types of slope units,the hydrological slope unit evaluation method based on manual correction is highly subjective,is complicated to operate,and has a low evaluation accuracy,whereas the evaluation method based on automatic slope unit division is efficient and accurate,is suitable for large-scale efficient geological disaster evaluation,and can better deal with the problem of geological disaster susceptibility evaluation.展开更多
Although the concentration of fine particulate matter(PM_(2.5))is reducing continuously,the proportion of secondary organic aerosols(SOA)in PM_(2.5) and the O_(3) levels are increasing.This is causing severe complex a...Although the concentration of fine particulate matter(PM_(2.5))is reducing continuously,the proportion of secondary organic aerosols(SOA)in PM_(2.5) and the O_(3) levels are increasing.This is causing severe complex atmospheric pollution in North China.It is essential to identify and quantify the driving factors of SOA and O_(3),including the various pollution sources and meteorological factors.PM_(2.5) and volatile organic compounds(VOCs)samples were collected simultaneously in three cities in Shandong Province during different pollution scenarios from 2021 to 2023.Then,the carbonaceous aerosol and 99 VOC species were analyzed.Random forest(RF)combined with positive matrix factorization and an observation-based model(OBM)were used to quantify the key drivers of SOA and O_(3).Aromatic hydrocarbons were the main contributors to secondary organic aerosol potential(74.3%-89.9%),whereas alkenes contributed the most to the ozone formation potential(27.0%-62.3%).The RF modeling identified temperature and NOx as the dominant drivers of ozone formation.These accounted for 47.8%and 17.4%,respectively.Temperature showed a positive correlation with O_(3) because an increase in temperature can promote ozone formation.NOx had a significant negative correlation with O_(3),which was consistent with the conclusions from the sensitivity analysis of the OBM.The dominant contributors to SOA were vehicle emissions,solvent use,and industrial emissions.These accounted for 43.9%,18.2%,and 10.5%,respectively.An evident positive correlation existed between these emission sources and SOA.展开更多
In response to the challenges of inadequate predictive accuracy and limited generalization capability in data-driven modeling for the mechanical properties of the cold-rolled strip steel,a predictive modeling method n...In response to the challenges of inadequate predictive accuracy and limited generalization capability in data-driven modeling for the mechanical properties of the cold-rolled strip steel,a predictive modeling method named RFR-WOA is developed based on random forest regression(RFR)and whale optimization algorithm(WOA).Firstly,using Pearson and Spearman correlation analysis and Gini coefficient importance ranking on an actual production dataset containing 37,878 samples,22 key variables are selected as model inputs from 112 variables that affect mechanical properties.Subsequently,an RFR-based predictive model for the mechanical properties of cold-rolled strip steel is constructed.Then,with the combination of the coefficient of determination(R^(2))and root mean square error as the optimization objective,the hyperparameters of RFR model are iteratively optimized using WOA,and better predictive effectiveness is obtained.Finally,the mechanical properties prediction model based on RFR-WOA is compared with models established using deep neural networks,convolutional neural networks,and other methods.The test results on 9469 samples of actual production data show that the model developed present has better predictive accuracy and generalization capability.展开更多
Accurate Electric Load Forecasting(ELF)is crucial for optimizing production capacity,improving operational efficiency,and managing energy resources effectively.Moreover,precise ELF contributes to a smaller environment...Accurate Electric Load Forecasting(ELF)is crucial for optimizing production capacity,improving operational efficiency,and managing energy resources effectively.Moreover,precise ELF contributes to a smaller environmental footprint by reducing the risks of disruption,downtime,and waste.However,with increasingly complex energy consumption patterns driven by renewable energy integration and changing consumer behaviors,no single approach has emerged as universally effective.In response,this research presents a hybrid modeling framework that combines the strengths of Random Forest(RF)and Autoregressive Integrated Moving Average(ARIMA)models,enhanced with advanced feature selection—Minimum Redundancy Maximum Relevancy and Maximum Synergy(MRMRMS)method—to produce a sparse model.Additionally,the residual patterns are analyzed to enhance forecast accuracy.High-resolution weather data from Weather Underground and historical energy consumption data from PJM for Duke Energy Ohio and Kentucky(DEO&K)are used in this application.This methodology,termed SP-RF-ARIMA,is evaluated against existing approaches;it demonstrates more than 40%reduction in mean absolute error and root mean square error compared to the second-best method.展开更多
To enhance the prediction accuracy of landslides in in Longyan City,China,this study developed a methodology for geologic hazard susceptibility assessment based on a coupled model composed of a Geographic Information ...To enhance the prediction accuracy of landslides in in Longyan City,China,this study developed a methodology for geologic hazard susceptibility assessment based on a coupled model composed of a Geographic Information System(GIS)with integrated spatial data,a frequency ratio(FR)model,and a random forest(RF)model(also referred to as the coupled FR-RF model).The coupled FR-RF model was constructed based on the analysis of nine influential factors,including distance from roads,normalized difference vegetation index(NDVI),and slope.The performance of the coupled FR-RF model was assessed using metrics such as Receiver Operating Characteristic(ROC)and Precision-Recall(PR)curves,yielding Area Under the Curve(AUC)values of 0.93 and 0.95,which indicate high predictive accuracy and reliability for geological hazard forecasting.Based on the model predictions,five susceptibility levels were determined in the study area,providing crucial spatial information for geologic hazard prevention and control.The contributions of various influential factors to landslide susceptibility were determined using SHapley Additive exPlanations(SHAP)analysis and the Gini index,enhancing the model interpretability and transparency.Additionally,this study discussed the limitations of the coupled FR-RF model and the prospects for its improvement using new technologies.This study provides an innovative method and theoretical support for geologic hazard prediction and management,holding promising prospects for application.展开更多
【目的】耒阳市滑坡灾害频发,对人民生命财产和生态安全构成严重威胁。为提高滑坡易发性评价的精度,【方法】以湖南省耒阳市为研究区,构建信息量模型(information value model,IV)与随机森林模型(random forest,RF)耦合的IV-RF模型,引...【目的】耒阳市滑坡灾害频发,对人民生命财产和生态安全构成严重威胁。为提高滑坡易发性评价的精度,【方法】以湖南省耒阳市为研究区,构建信息量模型(information value model,IV)与随机森林模型(random forest,RF)耦合的IV-RF模型,引入空间约束采样策略优化负样本选取策略,开展滑坡易发性评价。通过ROC曲线和AUC值对3种模型进行对比分析,同时提出综合性能指数用于综合评价模型表现。【结果】1)IV-RF耦合模型表现优于单一模型,AUC=0.952,综合性能指数(Accuracy+F1+MCC)为2.593。极高-高易发区滑坡点分布密集,极低-低易发区滑坡点极少,验证模型具有较高的空间预测精度。2)工程地质岩组因子是影响研究区滑坡发育最重要的评价因子之一。【结论】IV-RF耦合模型结合IV的数据定量解译与RF的非线性识别能力,可有效提升模型识别精度,研究结果可为研究区滑坡灾害风险防控、水土保持和国土空间规划提供科学依据。展开更多
Detecting cyber attacks in networks connected to the Internet of Things(IoT)is of utmost importance because of the growing vulnerabilities in the smart environment.Conventional models,such as Naive Bayes and support v...Detecting cyber attacks in networks connected to the Internet of Things(IoT)is of utmost importance because of the growing vulnerabilities in the smart environment.Conventional models,such as Naive Bayes and support vector machine(SVM),as well as ensemble methods,such as Gradient Boosting and eXtreme gradient boosting(XGBoost),are often plagued by high computational costs,which makes it challenging for them to perform real-time detection.In this regard,we suggested an attack detection approach that integrates Visual Geometry Group 16(VGG16),Artificial Rabbits Optimizer(ARO),and Random Forest Model to increase detection accuracy and operational efficiency in Internet of Things(IoT)networks.In the suggested model,the extraction of features from malware pictures was accomplished with the help of VGG16.The prediction process is carried out by the random forest model using the extracted features from the VGG16.Additionally,ARO is used to improve the hyper-parameters of the random forest model of the random forest.With an accuracy of 96.36%,the suggested model outperforms the standard models in terms of accuracy,F1-score,precision,and recall.The comparative research highlights our strategy’s success,which improves performance while maintaining a lower computational cost.This method is ideal for real-time applications,but it is effective.展开更多
Zenith wet delay(ZWD)is a key parameter for the precise positioning of global navigation satellite systems(GNSS)and occupies a central role in meteorological research.Currently,most models only consider the periodic v...Zenith wet delay(ZWD)is a key parameter for the precise positioning of global navigation satellite systems(GNSS)and occupies a central role in meteorological research.Currently,most models only consider the periodic variability of the ZWD,neglecting the effect of nonlinear factors on the ZWD estimation.This oversight results in a limited capability to reflect the rapid fluctuations of the ZWD.To more accurately capture and predict complicated variations in ZWD,this paper developed the CRZWD model by a combination of the GPT3 model and random forests(RF)algorithm using 5-year atmospheric profiles from 70 radiosonde(RS)stations across China.Taking the external 25 test stations data as reference,the root mean square(RMS)of the CRZWD model is 29.95 mm.Compared with the GPT3 model and another model using backpropagation neural network(BPNN),the accuracy has improved by 24.7%and 15.9%,respectively.Notably,over 56%of the test stations exhibit an improvement of more than 20%in contrast to GPT3-ZWD.Further temporal and spatial characteristic analyses also demonstrate the significant accuracy and stability advantages of the CRZWD model,indicating the potential prospects for GNSS-based applications.展开更多
This paper presents new trading models for the stock market and test whether they are able to consistently generate excess returns from the Singapore Exchange (SGX). Instead of conventional ways of modeling stock pric...This paper presents new trading models for the stock market and test whether they are able to consistently generate excess returns from the Singapore Exchange (SGX). Instead of conventional ways of modeling stock prices, we construct models which relate the market indicators to a trading decision directly. Furthermore, unlike a reversal trading system or a binary system of buy and sell, we allow three modes of trades, namely, buy, sell or stand by, and the stand-by case is important as it caters to the market conditions where a model does not produce a strong signal of buy or sell. Linear trading models are firstly developed with the scoring technique which weights higher on successful indicators, as well as with the Least Squares technique which tries to match the past perfect trades with its weights. The linear models are then made adaptive by using the forgetting factor to address market changes. Because stock markets could be highly nonlinear sometimes, the Random Forest is adopted as a nonlinear trading model, and improved with Gradient Boosting to form a new technique—Gradient Boosted Random Forest. All the models are trained and evaluated on nine stocks and one index, and statistical tests such as randomness, linear and nonlinear correlations are conducted on the data to check the statistical significance of the inputs and their relation with the output before a model is trained. Our empirical results show that the proposed trading methods are able to generate excess returns compared with the buy-and-hold strategy.展开更多
A machine learning-based APP may quickly and non-destructively evaluate the quality of parameters,such as hardness and anthocyanin content in blue honeysuckle berries(Lonicera caerulea L.,BHB),based on changes in peri...A machine learning-based APP may quickly and non-destructively evaluate the quality of parameters,such as hardness and anthocyanin content in blue honeysuckle berries(Lonicera caerulea L.,BHB),based on changes in pericarp color characteristics.The color feature information of the BHB pericarp was extracted,and the corresponding hardness and anthocyanin content were determined at various growing stages.Correlation analysis of BHB quality indexes was conducted by single and combined components of BHB epidermal color features.The results showed that fruit hardness had a significantly negative correlation with color feature parameter R-G,and its anthocyanin content had a significantly positive correlation with color feature parameter R.Comparing the eight models,random forest(RF)was established to evaluate the hardness and anthocyanin content of BHB according to the correlation between pericarp color features and hardness and anthocyanin content on BHB quality evaluation APP on the WeChat platform.The credibility of APP embedding RF model for evaluating hardness and anthocyanin content in BHB was validated with the determination coefficient of 0.89 and 0.93 in practice.This approach could efficiently and conveniently evaluate the quality indexes of BHB in real time and serve as a technical reference for the detection of quality indicators of other berries using smartphones.展开更多
Prelaunch rolling of maritime rockets threatens the reliability of launch in rough sea conditions.In order to suppress the prelaunch rolling,this study introduces advanced smart prediction designed especially for mari...Prelaunch rolling of maritime rockets threatens the reliability of launch in rough sea conditions.In order to suppress the prelaunch rolling,this study introduces advanced smart prediction designed especially for maritime rockets.The suggested approach introduces a hybrid model that combines random forest(RF)and Adaptive boosting(Ada Boost)methods to describe the coupling mechanism of factors affecting rocket rolling and to suppress the rolling.This combination improves forecast accuracy.Thereafter,the dimensionality reduced response surfaces are used to visually present the coupling between rocket rolling and influencing factors,which reveals the prelaunch rolling mechanism.When angle between the launch device and the ship's bow is within 80°-100°,the dynamic friction coefficient between adapters and guideways is 0.4,and the dynamic friction coefficient between the rocket and launchpad is within 0-0.15 or0.5-0.7,the prelaunch rolling of rocket during one motion cycle of the ship is less than 0.065°,originally 0.27°,reduced by 75.93%,effectively suppressing the prelaunch rolling.This study improves the prelaunch stability of maritime rockets in rough sea conditions and establishes a mapping relationship between the factors affecting rocket rolling and the structure of the sea launch system,guiding the optimization of future sea launch systems.展开更多
Stand age plays a crucial role in forest biomass estimation and carbon cycle modeling.Assessing the uncertainty of stand age prediction models and identifying the key driving factors in the modeling process have becom...Stand age plays a crucial role in forest biomass estimation and carbon cycle modeling.Assessing the uncertainty of stand age prediction models and identifying the key driving factors in the modeling process have become major challenges in forestry research.In this study,we selected the Shaanxi-Gansu-Ningxia region of Northeast China as the research area and utilized multi-source datasets from the summer of 2019 to extract information on spectral,textural,climatic,water balance,and stand characteristics.By integrating the Random Forest(RF)model with Monte Carlo(MC)simulation,we constructed six regression models based on different combina-tions of features and evaluated the uncertainty of each model.Furthermore,we investigated the driving factors influencing stand age modeling by analyzing the effects of different types of features on age inversion.Model performance and accuracy were assessed using the root mean square error(RMSE),mean absolute error(MAE),and the coefficient of determination(R^(2)),while the relative root mean square error(rRMSE)was employed to quantify model uncertainty.The results indicate that the scenarios with more obvious improve-ment in accuracy and effective reduction in uncertainty were Scenario 3 with the inclusion of climate and water balance information(RMSE=25.54 yr,MAE=18.03 yr,R^(2)=0.51,rRMSE=19.17%)and Scenario 5 with the inclusion of stand characterization informa-tion(RMSE=18.47 yr,MAE=13.05 yr,R^(2)=0.74,rRMSE=16.99%).Scenario 6,incorporating all feature types,achieved the highest accuracy(RMSE=17.60 yr,MAE=12.06 yr,R^(2)=0.77,rRMSE=14.19%).In this study,elevation,minimum temperature,and diameter at breast height(DBH)emerged as the key drivers of stand-age modeling.The proposed method can be used to identify drivers and to quantify uncertainty in stand-age estimation,providing a useful reference for improving model accuracy and uncertainty assessment.展开更多
文摘Slope units are divided according to the real topography and have clear geological characteristics,making them ideal units for evaluating the susceptibility to geological disasters.Based on the results of automatically and manually corrected hydrological slope unit division,the Longhua District,Shenzhen City,Guangdong Province,was selected as the study area.A total of 15 influencing factors,namely Fluctuation,slope,slope aspect,curvature,topographic witness index(TWI),stream power index(SPI),topographic roughness index(TRI),annual average rainfall,distance to water system,engineering rock group,distance to fault,land use,normalized difference vegetation index(NDVI),nighttime light,and distance to road,were selected as evaluation indicators.The information volume model(IV)and random points were used to select non-geological disaster units,and then the random forest model(RF)was used to evaluate the susceptibility to geological disasters.The automatic slope unit and the hydrological slope unit were compared and analyzed in the random forest and information volume random forest models.The results show that the area under the curve(AUC)values of the automatic slope unit evaluation results are 0.931 for the IV-RF model and 0.716 for the RF model,which are 0.6%(IV-RF model)and 1.9%(RF model)higher than those for the hydrological slope unit.Based on a comparison of the evaluation methods based on the two types of slope units,the hydrological slope unit evaluation method based on manual correction is highly subjective,is complicated to operate,and has a low evaluation accuracy,whereas the evaluation method based on automatic slope unit division is efficient and accurate,is suitable for large-scale efficient geological disaster evaluation,and can better deal with the problem of geological disaster susceptibility evaluation.
基金supported by Qingdao Natural Science Foundation(No. 23-2-1-224-zyyd-jch)。
文摘Although the concentration of fine particulate matter(PM_(2.5))is reducing continuously,the proportion of secondary organic aerosols(SOA)in PM_(2.5) and the O_(3) levels are increasing.This is causing severe complex atmospheric pollution in North China.It is essential to identify and quantify the driving factors of SOA and O_(3),including the various pollution sources and meteorological factors.PM_(2.5) and volatile organic compounds(VOCs)samples were collected simultaneously in three cities in Shandong Province during different pollution scenarios from 2021 to 2023.Then,the carbonaceous aerosol and 99 VOC species were analyzed.Random forest(RF)combined with positive matrix factorization and an observation-based model(OBM)were used to quantify the key drivers of SOA and O_(3).Aromatic hydrocarbons were the main contributors to secondary organic aerosol potential(74.3%-89.9%),whereas alkenes contributed the most to the ozone formation potential(27.0%-62.3%).The RF modeling identified temperature and NOx as the dominant drivers of ozone formation.These accounted for 47.8%and 17.4%,respectively.Temperature showed a positive correlation with O_(3) because an increase in temperature can promote ozone formation.NOx had a significant negative correlation with O_(3),which was consistent with the conclusions from the sensitivity analysis of the OBM.The dominant contributors to SOA were vehicle emissions,solvent use,and industrial emissions.These accounted for 43.9%,18.2%,and 10.5%,respectively.An evident positive correlation existed between these emission sources and SOA.
基金supported by National Natural Science Foundation of China(Grant 62573375)the Natural Science Foundation of Hebei Province(Grant F2024203038)+2 种基金the Science and Technology Research and Development Plan Project of Qinhuangdao City(Grant 202302B048)the Provincial Key Laboratory Performance Subsidy Project(Grant 22567612H)the Shandong Provincial Natural Science Foundation Youth Project(ZR2023QF044)。
文摘In response to the challenges of inadequate predictive accuracy and limited generalization capability in data-driven modeling for the mechanical properties of the cold-rolled strip steel,a predictive modeling method named RFR-WOA is developed based on random forest regression(RFR)and whale optimization algorithm(WOA).Firstly,using Pearson and Spearman correlation analysis and Gini coefficient importance ranking on an actual production dataset containing 37,878 samples,22 key variables are selected as model inputs from 112 variables that affect mechanical properties.Subsequently,an RFR-based predictive model for the mechanical properties of cold-rolled strip steel is constructed.Then,with the combination of the coefficient of determination(R^(2))and root mean square error as the optimization objective,the hyperparameters of RFR model are iteratively optimized using WOA,and better predictive effectiveness is obtained.Finally,the mechanical properties prediction model based on RFR-WOA is compared with models established using deep neural networks,convolutional neural networks,and other methods.The test results on 9469 samples of actual production data show that the model developed present has better predictive accuracy and generalization capability.
基金supported by the Startup Grant(PG18929)awarded to F.Shokoohi.
文摘Accurate Electric Load Forecasting(ELF)is crucial for optimizing production capacity,improving operational efficiency,and managing energy resources effectively.Moreover,precise ELF contributes to a smaller environmental footprint by reducing the risks of disruption,downtime,and waste.However,with increasingly complex energy consumption patterns driven by renewable energy integration and changing consumer behaviors,no single approach has emerged as universally effective.In response,this research presents a hybrid modeling framework that combines the strengths of Random Forest(RF)and Autoregressive Integrated Moving Average(ARIMA)models,enhanced with advanced feature selection—Minimum Redundancy Maximum Relevancy and Maximum Synergy(MRMRMS)method—to produce a sparse model.Additionally,the residual patterns are analyzed to enhance forecast accuracy.High-resolution weather data from Weather Underground and historical energy consumption data from PJM for Duke Energy Ohio and Kentucky(DEO&K)are used in this application.This methodology,termed SP-RF-ARIMA,is evaluated against existing approaches;it demonstrates more than 40%reduction in mean absolute error and root mean square error compared to the second-best method.
基金supported by the project of the China Geological Survey(DD20230591).
文摘To enhance the prediction accuracy of landslides in in Longyan City,China,this study developed a methodology for geologic hazard susceptibility assessment based on a coupled model composed of a Geographic Information System(GIS)with integrated spatial data,a frequency ratio(FR)model,and a random forest(RF)model(also referred to as the coupled FR-RF model).The coupled FR-RF model was constructed based on the analysis of nine influential factors,including distance from roads,normalized difference vegetation index(NDVI),and slope.The performance of the coupled FR-RF model was assessed using metrics such as Receiver Operating Characteristic(ROC)and Precision-Recall(PR)curves,yielding Area Under the Curve(AUC)values of 0.93 and 0.95,which indicate high predictive accuracy and reliability for geological hazard forecasting.Based on the model predictions,five susceptibility levels were determined in the study area,providing crucial spatial information for geologic hazard prevention and control.The contributions of various influential factors to landslide susceptibility were determined using SHapley Additive exPlanations(SHAP)analysis and the Gini index,enhancing the model interpretability and transparency.Additionally,this study discussed the limitations of the coupled FR-RF model and the prospects for its improvement using new technologies.This study provides an innovative method and theoretical support for geologic hazard prediction and management,holding promising prospects for application.
文摘【目的】耒阳市滑坡灾害频发,对人民生命财产和生态安全构成严重威胁。为提高滑坡易发性评价的精度,【方法】以湖南省耒阳市为研究区,构建信息量模型(information value model,IV)与随机森林模型(random forest,RF)耦合的IV-RF模型,引入空间约束采样策略优化负样本选取策略,开展滑坡易发性评价。通过ROC曲线和AUC值对3种模型进行对比分析,同时提出综合性能指数用于综合评价模型表现。【结果】1)IV-RF耦合模型表现优于单一模型,AUC=0.952,综合性能指数(Accuracy+F1+MCC)为2.593。极高-高易发区滑坡点分布密集,极低-低易发区滑坡点极少,验证模型具有较高的空间预测精度。2)工程地质岩组因子是影响研究区滑坡发育最重要的评价因子之一。【结论】IV-RF耦合模型结合IV的数据定量解译与RF的非线性识别能力,可有效提升模型识别精度,研究结果可为研究区滑坡灾害风险防控、水土保持和国土空间规划提供科学依据。
基金funded by Institutional Fund Projects under grant no.(IFPDP-261-22)。
文摘Detecting cyber attacks in networks connected to the Internet of Things(IoT)is of utmost importance because of the growing vulnerabilities in the smart environment.Conventional models,such as Naive Bayes and support vector machine(SVM),as well as ensemble methods,such as Gradient Boosting and eXtreme gradient boosting(XGBoost),are often plagued by high computational costs,which makes it challenging for them to perform real-time detection.In this regard,we suggested an attack detection approach that integrates Visual Geometry Group 16(VGG16),Artificial Rabbits Optimizer(ARO),and Random Forest Model to increase detection accuracy and operational efficiency in Internet of Things(IoT)networks.In the suggested model,the extraction of features from malware pictures was accomplished with the help of VGG16.The prediction process is carried out by the random forest model using the extracted features from the VGG16.Additionally,ARO is used to improve the hyper-parameters of the random forest model of the random forest.With an accuracy of 96.36%,the suggested model outperforms the standard models in terms of accuracy,F1-score,precision,and recall.The comparative research highlights our strategy’s success,which improves performance while maintaining a lower computational cost.This method is ideal for real-time applications,but it is effective.
基金supported by the National Natural Science Foundation of China[42030109,42074012]the Scientific Study Project for institutes of Higher Learning,Ministry of Education,Liaoning Province[LJKMZ20220673]+2 种基金the Project supported by the State Key Laboratory of Geodesy and Earths'Dynamics,Innovation Academy for Precision Measurement Science and Technology[SKLGED2023-3-2]Liaoning Revitalization Talent Program[XLYC2203162]Natural Science Foundation of Hebei Province in China[D2023402024].
文摘Zenith wet delay(ZWD)is a key parameter for the precise positioning of global navigation satellite systems(GNSS)and occupies a central role in meteorological research.Currently,most models only consider the periodic variability of the ZWD,neglecting the effect of nonlinear factors on the ZWD estimation.This oversight results in a limited capability to reflect the rapid fluctuations of the ZWD.To more accurately capture and predict complicated variations in ZWD,this paper developed the CRZWD model by a combination of the GPT3 model and random forests(RF)algorithm using 5-year atmospheric profiles from 70 radiosonde(RS)stations across China.Taking the external 25 test stations data as reference,the root mean square(RMS)of the CRZWD model is 29.95 mm.Compared with the GPT3 model and another model using backpropagation neural network(BPNN),the accuracy has improved by 24.7%and 15.9%,respectively.Notably,over 56%of the test stations exhibit an improvement of more than 20%in contrast to GPT3-ZWD.Further temporal and spatial characteristic analyses also demonstrate the significant accuracy and stability advantages of the CRZWD model,indicating the potential prospects for GNSS-based applications.
文摘This paper presents new trading models for the stock market and test whether they are able to consistently generate excess returns from the Singapore Exchange (SGX). Instead of conventional ways of modeling stock prices, we construct models which relate the market indicators to a trading decision directly. Furthermore, unlike a reversal trading system or a binary system of buy and sell, we allow three modes of trades, namely, buy, sell or stand by, and the stand-by case is important as it caters to the market conditions where a model does not produce a strong signal of buy or sell. Linear trading models are firstly developed with the scoring technique which weights higher on successful indicators, as well as with the Least Squares technique which tries to match the past perfect trades with its weights. The linear models are then made adaptive by using the forgetting factor to address market changes. Because stock markets could be highly nonlinear sometimes, the Random Forest is adopted as a nonlinear trading model, and improved with Gradient Boosting to form a new technique—Gradient Boosted Random Forest. All the models are trained and evaluated on nine stocks and one index, and statistical tests such as randomness, linear and nonlinear correlations are conducted on the data to check the statistical significance of the inputs and their relation with the output before a model is trained. Our empirical results show that the proposed trading methods are able to generate excess returns compared with the buy-and-hold strategy.
基金Supported by the National Natural Science Foundation of China(32072352)the National Key Research and Development Program Project of China(2022YFD1600500)。
文摘A machine learning-based APP may quickly and non-destructively evaluate the quality of parameters,such as hardness and anthocyanin content in blue honeysuckle berries(Lonicera caerulea L.,BHB),based on changes in pericarp color characteristics.The color feature information of the BHB pericarp was extracted,and the corresponding hardness and anthocyanin content were determined at various growing stages.Correlation analysis of BHB quality indexes was conducted by single and combined components of BHB epidermal color features.The results showed that fruit hardness had a significantly negative correlation with color feature parameter R-G,and its anthocyanin content had a significantly positive correlation with color feature parameter R.Comparing the eight models,random forest(RF)was established to evaluate the hardness and anthocyanin content of BHB according to the correlation between pericarp color features and hardness and anthocyanin content on BHB quality evaluation APP on the WeChat platform.The credibility of APP embedding RF model for evaluating hardness and anthocyanin content in BHB was validated with the determination coefficient of 0.89 and 0.93 in practice.This approach could efficiently and conveniently evaluate the quality indexes of BHB in real time and serve as a technical reference for the detection of quality indicators of other berries using smartphones.
文摘Prelaunch rolling of maritime rockets threatens the reliability of launch in rough sea conditions.In order to suppress the prelaunch rolling,this study introduces advanced smart prediction designed especially for maritime rockets.The suggested approach introduces a hybrid model that combines random forest(RF)and Adaptive boosting(Ada Boost)methods to describe the coupling mechanism of factors affecting rocket rolling and to suppress the rolling.This combination improves forecast accuracy.Thereafter,the dimensionality reduced response surfaces are used to visually present the coupling between rocket rolling and influencing factors,which reveals the prelaunch rolling mechanism.When angle between the launch device and the ship's bow is within 80°-100°,the dynamic friction coefficient between adapters and guideways is 0.4,and the dynamic friction coefficient between the rocket and launchpad is within 0-0.15 or0.5-0.7,the prelaunch rolling of rocket during one motion cycle of the ship is less than 0.065°,originally 0.27°,reduced by 75.93%,effectively suppressing the prelaunch rolling.This study improves the prelaunch stability of maritime rockets in rough sea conditions and establishes a mapping relationship between the factors affecting rocket rolling and the structure of the sea launch system,guiding the optimization of future sea launch systems.
基金Under the auspices of the Natural Science Foundation of China(No.32371875,32001249)。
文摘Stand age plays a crucial role in forest biomass estimation and carbon cycle modeling.Assessing the uncertainty of stand age prediction models and identifying the key driving factors in the modeling process have become major challenges in forestry research.In this study,we selected the Shaanxi-Gansu-Ningxia region of Northeast China as the research area and utilized multi-source datasets from the summer of 2019 to extract information on spectral,textural,climatic,water balance,and stand characteristics.By integrating the Random Forest(RF)model with Monte Carlo(MC)simulation,we constructed six regression models based on different combina-tions of features and evaluated the uncertainty of each model.Furthermore,we investigated the driving factors influencing stand age modeling by analyzing the effects of different types of features on age inversion.Model performance and accuracy were assessed using the root mean square error(RMSE),mean absolute error(MAE),and the coefficient of determination(R^(2)),while the relative root mean square error(rRMSE)was employed to quantify model uncertainty.The results indicate that the scenarios with more obvious improve-ment in accuracy and effective reduction in uncertainty were Scenario 3 with the inclusion of climate and water balance information(RMSE=25.54 yr,MAE=18.03 yr,R^(2)=0.51,rRMSE=19.17%)and Scenario 5 with the inclusion of stand characterization informa-tion(RMSE=18.47 yr,MAE=13.05 yr,R^(2)=0.74,rRMSE=16.99%).Scenario 6,incorporating all feature types,achieved the highest accuracy(RMSE=17.60 yr,MAE=12.06 yr,R^(2)=0.77,rRMSE=14.19%).In this study,elevation,minimum temperature,and diameter at breast height(DBH)emerged as the key drivers of stand-age modeling.The proposed method can be used to identify drivers and to quantify uncertainty in stand-age estimation,providing a useful reference for improving model accuracy and uncertainty assessment.
