The applicability of statistics-based landslide susceptibility assessment methods is affected by the number of historical landslides.Previous studies have proposed support vector machine(SVM)as a small-sample learning...The applicability of statistics-based landslide susceptibility assessment methods is affected by the number of historical landslides.Previous studies have proposed support vector machine(SVM)as a small-sample learning method.However,those studies demonstrated that different parameters can affect model performance.We optimized the SVM and obtained models as 5-fold cross validation(5-CV)SVM,genetic algorithm(GA)SVM,and particle swarm optimization(PSO)SVM.This study compared the prediction performances of logistic regression(LR),5-CV SVM,GA SVM,and PSO SVM on landslide susceptibility mapping,to explore the spatial distribution of landslide susceptibility in the study area in Tibetan Plateau,China.A geospatial database was established based on 392 historical landslides and 392 non-landslides in the study area.We used 11 influencing factors of altitude,slope,aspect,curvature,lithology,normalized difference vegetation index(NDVI),distance to road,distance to river,distance to fault,peak ground acceleration(PGA),and rainfall to construct an influencing factor evaluation system.To evaluate the models,four susceptibility maps were compared via receiver operating characteristics(ROC)curve and the results showed that prediction rates for the models are 84%(LR),87%(5-CV SVM),85%(GA SVM),and 90%(PSO SVM).We also used precision,recall,F1-score and accuracy to assess the quality performance of these models.The results showed that the PSO SVM had greater potential for future implementation in the Tibetan Plateau area because of its superior performance in the landslide susceptibility assessment.展开更多
Objective The Cenozoic Indo-Asian collision caused significant crustal shortening and plateau uplift in the central Tibet. The extrusion tectonic model has been widely accepted to explain the strike-slip faults around...Objective The Cenozoic Indo-Asian collision caused significant crustal shortening and plateau uplift in the central Tibet. The extrusion tectonic model has been widely accepted to explain the strike-slip faults around the Tibetan Plateau. Previous studies indicate that the lower crust flow is the main drive force of the extrusion tectonics. Whether mantle extrusion process occurred during the Cenozoic uplift is a major problem to be addressed, which is significant for understanding the uplift mechanism and tectonic evolution of the Tibetan Plateau.展开更多
The border areas of the Tibetan Plateau and the neighboring mountainous areashave a high incidence of earthquakes with a magnitude greater than Ms 5.0, as well as havinga dense distribution of geological disasters suc...The border areas of the Tibetan Plateau and the neighboring mountainous areashave a high incidence of earthquakes with a magnitude greater than Ms 5.0, as well as havinga dense distribution of geological disasters such as collapses, landslides, and debris flows.Revealing the post-disaster economic development and recovery process is very importantfor enhancing disaster prevention and response capacity in order to formulate control policiesand recovery methods for post-disaster economic reconstruction based on economic resilience.Using long-term socioeconomic data and the autoregressive integrated moving average(ARIMA) model, this paper calculated the economic resilience index of the areas mostseverely affected by the Wenchuan Earthquake of 2008 and adopted the improved variablereturns to scale (VRS) date envelopment analysis (DEA) model and the Malmquist productivityindex to analyze the efficiency and effect of annual post-disaster recovery. The resultsshow that: (1) the economic resilience index of the areas most severely affected by theWenchuan Earthquake was 0.877. The earthquake resulted in a short-term economic recessionin the affected areas, but the economy returned to pre-quake levels within two years. Inaddition, the industrial economy was less resilient than agriculture and the service industry. (2)The comprehensive economic recovery efficiency of the disaster-stricken area in the yearfollowing the disaster was 0.603. The comprehensive efficiency, the pure technical efficiency,and the scale efficiency of the plain and hilly areas were significantly greater than those of theplateau and mountain areas. (3) The annual fluctuation in total factor productivity (TFP) followingthe disaster was considerable, and the economic recovery efficiency decreased significantly,resulting in a short-term economic recession. The TFP index returned to steadystate following decreases of 33.7% and 15.2%, respectively, in the two years following thedisaster. (4) The significant decrease in the post-disaster recovery efficiency was causedmainly by technological changes, and the renewal of the production system was the leading factor in determining the economic resilience following the disaster. With the decline in thescale of economic recovery following the earthquake, long-term economic recovery in thedisaster-stricken areas depended mainly on pure technical efficiency, and the improvement inthe latter was the driving force for maintaining the long-term growth of the post-disastereconomy. Therefore, according to the local characteristics of natural environment and economicsystem, the disaster-stricken areas need to actively change and readjust their economicstructures. At the same time, attention should be paid to updating the production systemto enhance the level of technological progress and give full play to the scale effects oflarge-scale capital, new facilities, human resources, and other investment factors followingthe disaster so as to enhance the impact of economic resilience and recovery efficiency inresponse to the disaster.展开更多
基金financially supported by the National Natural Science Foundation of China(41977213)the Second Tibetan Plateau Scientific Expedition and Research Program(2019QZKK0906)+3 种基金Science and Technology Department of Sichuan Province(2021YJ0032)Sichuan Transportation Science and Technology Project(2021-A-03)Sichuan Science and Technology Program(2022NSFSC0425)CREC Sichuan Eco-City Investment Co,Ltd.(R110121H01092)。
文摘The applicability of statistics-based landslide susceptibility assessment methods is affected by the number of historical landslides.Previous studies have proposed support vector machine(SVM)as a small-sample learning method.However,those studies demonstrated that different parameters can affect model performance.We optimized the SVM and obtained models as 5-fold cross validation(5-CV)SVM,genetic algorithm(GA)SVM,and particle swarm optimization(PSO)SVM.This study compared the prediction performances of logistic regression(LR),5-CV SVM,GA SVM,and PSO SVM on landslide susceptibility mapping,to explore the spatial distribution of landslide susceptibility in the study area in Tibetan Plateau,China.A geospatial database was established based on 392 historical landslides and 392 non-landslides in the study area.We used 11 influencing factors of altitude,slope,aspect,curvature,lithology,normalized difference vegetation index(NDVI),distance to road,distance to river,distance to fault,peak ground acceleration(PGA),and rainfall to construct an influencing factor evaluation system.To evaluate the models,four susceptibility maps were compared via receiver operating characteristics(ROC)curve and the results showed that prediction rates for the models are 84%(LR),87%(5-CV SVM),85%(GA SVM),and 90%(PSO SVM).We also used precision,recall,F1-score and accuracy to assess the quality performance of these models.The results showed that the PSO SVM had greater potential for future implementation in the Tibetan Plateau area because of its superior performance in the landslide susceptibility assessment.
基金supported by the National Natural Science Foundation of China (grant No.41072052)
文摘Objective The Cenozoic Indo-Asian collision caused significant crustal shortening and plateau uplift in the central Tibet. The extrusion tectonic model has been widely accepted to explain the strike-slip faults around the Tibetan Plateau. Previous studies indicate that the lower crust flow is the main drive force of the extrusion tectonics. Whether mantle extrusion process occurred during the Cenozoic uplift is a major problem to be addressed, which is significant for understanding the uplift mechanism and tectonic evolution of the Tibetan Plateau.
基金Second Tibetan Plateau Scientific Expedition and Research Program(STEP),No.2019QZKK0406National Natural Science Foundation of China,No.41807510,No.41501139。
文摘The border areas of the Tibetan Plateau and the neighboring mountainous areashave a high incidence of earthquakes with a magnitude greater than Ms 5.0, as well as havinga dense distribution of geological disasters such as collapses, landslides, and debris flows.Revealing the post-disaster economic development and recovery process is very importantfor enhancing disaster prevention and response capacity in order to formulate control policiesand recovery methods for post-disaster economic reconstruction based on economic resilience.Using long-term socioeconomic data and the autoregressive integrated moving average(ARIMA) model, this paper calculated the economic resilience index of the areas mostseverely affected by the Wenchuan Earthquake of 2008 and adopted the improved variablereturns to scale (VRS) date envelopment analysis (DEA) model and the Malmquist productivityindex to analyze the efficiency and effect of annual post-disaster recovery. The resultsshow that: (1) the economic resilience index of the areas most severely affected by theWenchuan Earthquake was 0.877. The earthquake resulted in a short-term economic recessionin the affected areas, but the economy returned to pre-quake levels within two years. Inaddition, the industrial economy was less resilient than agriculture and the service industry. (2)The comprehensive economic recovery efficiency of the disaster-stricken area in the yearfollowing the disaster was 0.603. The comprehensive efficiency, the pure technical efficiency,and the scale efficiency of the plain and hilly areas were significantly greater than those of theplateau and mountain areas. (3) The annual fluctuation in total factor productivity (TFP) followingthe disaster was considerable, and the economic recovery efficiency decreased significantly,resulting in a short-term economic recession. The TFP index returned to steadystate following decreases of 33.7% and 15.2%, respectively, in the two years following thedisaster. (4) The significant decrease in the post-disaster recovery efficiency was causedmainly by technological changes, and the renewal of the production system was the leading factor in determining the economic resilience following the disaster. With the decline in thescale of economic recovery following the earthquake, long-term economic recovery in thedisaster-stricken areas depended mainly on pure technical efficiency, and the improvement inthe latter was the driving force for maintaining the long-term growth of the post-disastereconomy. Therefore, according to the local characteristics of natural environment and economicsystem, the disaster-stricken areas need to actively change and readjust their economicstructures. At the same time, attention should be paid to updating the production systemto enhance the level of technological progress and give full play to the scale effects oflarge-scale capital, new facilities, human resources, and other investment factors followingthe disaster so as to enhance the impact of economic resilience and recovery efficiency inresponse to the disaster.
