Landslide susceptibility prediction assesses the likelihood of landslides occurring in specific areas,providing crucial scientific support for mitigating the threat to people’s lives and property posed by landslide d...Landslide susceptibility prediction assesses the likelihood of landslides occurring in specific areas,providing crucial scientific support for mitigating the threat to people’s lives and property posed by landslide disasters.To address the challenges in the existing landslide susceptibility prediction methods,such as insufficient representativeness of selected nonlandslide points,limited ability to capture nonlinear relationships,and a tendency to fall into local optima,the traditional Self-Organizing Maps(SOM)is improved in this paper by using a hierarchical approach to form Hierarchical Self-Organizing Maps(HSOM),and a model integrating the Information Value(IV),Adaptive Bat Precise Algorithm(ABPA),and Convolutional Neural Network(CNN)is proposed,termed IABPA-CNN.Evaluation factors such as topography,basic geology and hydrometeorology were selected with the 2008 Wenchuan earthquake-hit disaster area as the study area.The data were preprocessed,Pearson correlation coefficient,tolerance(TOL),and variance inflation factor(VIF)were employed to assess the correlation among all the factors.Subsequently,the information value for each evaluation factor's classification was calculated,thereby establishing a high-quality sample dataset,which was input into the IABPA-CNN model and IVCNN model(contrast model),respectively.The Receiver Operating Characteristic(ROC)curve was used to compare and analyze the accuracy and performance.The Area Under Curve(AUC)values for the two models is 0.89 and 0.85,respectively,indicating that the IABPA-CNN model has higher predictive accuracy.Compared with the IV-CNN model,the proportion of landslides predicted by the IABPA-CNN model in the high susceptibility and very high susceptibility area increased to 28.18%and 30.76%,respectively.Although the area proportions of very low susceptibility and low susceptibility area increased,the proportion of landslides quantity decreased.Furthermore,Monte Carlo method was employed to analyze the uncertainty of IABPA-CNN model,and average variance of 0.0083,which indicates that the model has high reliability in landslide susceptibility prediction.Therefore,the research results in this study provide a reliable scientific basis for the work of landslide disaster prevention and mitigation in Wenchuan earthquake disaster area.展开更多
Unmanned aerial vehicle(UAV)paths in the field directly affect the efficiency and accuracy of payload data collection.Path planning of UAV advancing along river valleys in wild environments is one of the first and mos...Unmanned aerial vehicle(UAV)paths in the field directly affect the efficiency and accuracy of payload data collection.Path planning of UAV advancing along river valleys in wild environments is one of the first and most difficult problems faced by unmanned surveys of debris flow valleys.This study proposes a new hybrid bat optimization algorithm,GRE-Bat(Good point set,Reverse learning,Elite Pool-Bat algorithm),for unmanned exploration path planning of debris flow sources in outdoor environments.In the GRE-Bat algorithm,the good point set strategy is adopted to evenly distribute the population,ensure sufficient coverage of the search space,and improve the stability of the convergence accuracy of the algorithm.Subsequently,a reverse learning strategy is introduced to increase the diversity of the population and improve the local stagnation problem of the algorithm.In addition,an Elite pool strategy is added to balance the replacement and learning behaviors of particles within the population based on elimination and local perturbation factors.To demonstrate the effectiveness of the GRE-Bat algorithm,we conducted multiple simulation experiments using benchmark test functions and digital terrain models.Compared to commonly used path planning algorithms such as the Bat Algorithm(BA)and the Improved Sparrow Search Algorithm(ISSA),the GRE-Bat algorithm can converge to the optimal value in different types of test functions and obtains a near-optimal solution after an average of 60 iterations.The GRE-Bat algorithm can obtain higher quality flight routes in the designated environment of unmanned investigation in the debris flow gully basin,demonstrating its potential for practical application.展开更多
基金supported by National Natural Science Foundation of China(Grant No.42302336)Project of the Department of Science and Technology of Sichuan Province(Grant Nos.2024YFHZ0098+1 种基金2023NSFSC0751)Open Project of Chengdu University of Information Technology(KYQN202317,760115027).
文摘Landslide susceptibility prediction assesses the likelihood of landslides occurring in specific areas,providing crucial scientific support for mitigating the threat to people’s lives and property posed by landslide disasters.To address the challenges in the existing landslide susceptibility prediction methods,such as insufficient representativeness of selected nonlandslide points,limited ability to capture nonlinear relationships,and a tendency to fall into local optima,the traditional Self-Organizing Maps(SOM)is improved in this paper by using a hierarchical approach to form Hierarchical Self-Organizing Maps(HSOM),and a model integrating the Information Value(IV),Adaptive Bat Precise Algorithm(ABPA),and Convolutional Neural Network(CNN)is proposed,termed IABPA-CNN.Evaluation factors such as topography,basic geology and hydrometeorology were selected with the 2008 Wenchuan earthquake-hit disaster area as the study area.The data were preprocessed,Pearson correlation coefficient,tolerance(TOL),and variance inflation factor(VIF)were employed to assess the correlation among all the factors.Subsequently,the information value for each evaluation factor's classification was calculated,thereby establishing a high-quality sample dataset,which was input into the IABPA-CNN model and IVCNN model(contrast model),respectively.The Receiver Operating Characteristic(ROC)curve was used to compare and analyze the accuracy and performance.The Area Under Curve(AUC)values for the two models is 0.89 and 0.85,respectively,indicating that the IABPA-CNN model has higher predictive accuracy.Compared with the IV-CNN model,the proportion of landslides predicted by the IABPA-CNN model in the high susceptibility and very high susceptibility area increased to 28.18%and 30.76%,respectively.Although the area proportions of very low susceptibility and low susceptibility area increased,the proportion of landslides quantity decreased.Furthermore,Monte Carlo method was employed to analyze the uncertainty of IABPA-CNN model,and average variance of 0.0083,which indicates that the model has high reliability in landslide susceptibility prediction.Therefore,the research results in this study provide a reliable scientific basis for the work of landslide disaster prevention and mitigation in Wenchuan earthquake disaster area.
基金supported by National Natural Science Foundation of China(No.42302336)Project of the Department of Science and Technology of Sichuan Province(No.2024YFHZ0098,No.2023NSFSC0751)Open Project of Chengdu University of Information Technology(KYQN202317,760115027,KYTZ202278,KYTZ202280).
文摘Unmanned aerial vehicle(UAV)paths in the field directly affect the efficiency and accuracy of payload data collection.Path planning of UAV advancing along river valleys in wild environments is one of the first and most difficult problems faced by unmanned surveys of debris flow valleys.This study proposes a new hybrid bat optimization algorithm,GRE-Bat(Good point set,Reverse learning,Elite Pool-Bat algorithm),for unmanned exploration path planning of debris flow sources in outdoor environments.In the GRE-Bat algorithm,the good point set strategy is adopted to evenly distribute the population,ensure sufficient coverage of the search space,and improve the stability of the convergence accuracy of the algorithm.Subsequently,a reverse learning strategy is introduced to increase the diversity of the population and improve the local stagnation problem of the algorithm.In addition,an Elite pool strategy is added to balance the replacement and learning behaviors of particles within the population based on elimination and local perturbation factors.To demonstrate the effectiveness of the GRE-Bat algorithm,we conducted multiple simulation experiments using benchmark test functions and digital terrain models.Compared to commonly used path planning algorithms such as the Bat Algorithm(BA)and the Improved Sparrow Search Algorithm(ISSA),the GRE-Bat algorithm can converge to the optimal value in different types of test functions and obtains a near-optimal solution after an average of 60 iterations.The GRE-Bat algorithm can obtain higher quality flight routes in the designated environment of unmanned investigation in the debris flow gully basin,demonstrating its potential for practical application.
