{"title":"预测山体滑坡易发性和评估易发因素的方法","authors":"Wanxin Guo, Jian Ye, Chengbing Liu, Yijie Lv, Qiuyu Zeng, Xin Huang","doi":"10.1016/j.jag.2024.104217","DOIUrl":null,"url":null,"abstract":"Effectively leveraging landslide spatial location information is crucial for improving the accuracy of deep learning in predicting landslide susceptibility and exploring the impacts of predisposing factors. Current single deep learning models for landslide susceptibility assessment require enhancements in both prediction accuracy and robustness. Inclusion of non-interrelated positional information among samples leads to reduced prediction accuracy and challenges in quantifying landslide risk covariates. This study proposes a landslide susceptibility assessment method that integrates ensemble learning with geographically weighted concepts. Using a stacking method, a 1D convolutional neural network (1D-CNN), a recurrent neural network (RNN), and a long short-term memory (LSTM) network were combined to form the CRNN-LSTM ensemble model. Additionally, we constructed a deep learning geographically weighted regression (GW-DNN) model based on the deep learning principles and geographically weighted regression to quantify the impacts of landslide-predisposing factors.The experimental results show that the CRNN-LSTM model achieved AUC values of 0.977 and 0.961 on the training and validation sets, significantly outperforming the individual classifiers (AUC of 0.944 and 0.940 for the 1D-CNN model, 0.950 and 0.948 for the RNN model, and 0.956 and 0.952 for the LSTM model). Additionally, the GW-DNN model achieved R<ce:sup loc=\"post\">2</ce:sup> coefficients of 0.876 and 0.860 during the training and validation phases. These findings indicate that our proposed method not only highly accurately predicts landslide susceptibility but also provides a precise quantitative assessment of the impact of landslide-predisposing factors at specific spatial points (landslide units) in high-risk areas. These findings offer valuable technical support for landslide disaster prevention and mitigation.","PeriodicalId":50341,"journal":{"name":"International Journal of Applied Earth Observation and Geoinformation","volume":"120 1","pages":""},"PeriodicalIF":7.5000,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An approach for predicting landslide susceptibility and evaluating predisposing factors\",\"authors\":\"Wanxin Guo, Jian Ye, Chengbing Liu, Yijie Lv, Qiuyu Zeng, Xin Huang\",\"doi\":\"10.1016/j.jag.2024.104217\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Effectively leveraging landslide spatial location information is crucial for improving the accuracy of deep learning in predicting landslide susceptibility and exploring the impacts of predisposing factors. Current single deep learning models for landslide susceptibility assessment require enhancements in both prediction accuracy and robustness. Inclusion of non-interrelated positional information among samples leads to reduced prediction accuracy and challenges in quantifying landslide risk covariates. This study proposes a landslide susceptibility assessment method that integrates ensemble learning with geographically weighted concepts. Using a stacking method, a 1D convolutional neural network (1D-CNN), a recurrent neural network (RNN), and a long short-term memory (LSTM) network were combined to form the CRNN-LSTM ensemble model. Additionally, we constructed a deep learning geographically weighted regression (GW-DNN) model based on the deep learning principles and geographically weighted regression to quantify the impacts of landslide-predisposing factors.The experimental results show that the CRNN-LSTM model achieved AUC values of 0.977 and 0.961 on the training and validation sets, significantly outperforming the individual classifiers (AUC of 0.944 and 0.940 for the 1D-CNN model, 0.950 and 0.948 for the RNN model, and 0.956 and 0.952 for the LSTM model). Additionally, the GW-DNN model achieved R<ce:sup loc=\\\"post\\\">2</ce:sup> coefficients of 0.876 and 0.860 during the training and validation phases. These findings indicate that our proposed method not only highly accurately predicts landslide susceptibility but also provides a precise quantitative assessment of the impact of landslide-predisposing factors at specific spatial points (landslide units) in high-risk areas. These findings offer valuable technical support for landslide disaster prevention and mitigation.\",\"PeriodicalId\":50341,\"journal\":{\"name\":\"International Journal of Applied Earth Observation and Geoinformation\",\"volume\":\"120 1\",\"pages\":\"\"},\"PeriodicalIF\":7.5000,\"publicationDate\":\"2024-12-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Applied Earth Observation and Geoinformation\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1016/j.jag.2024.104217\",\"RegionNum\":1,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Earth and Planetary Sciences\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Applied Earth Observation and Geoinformation","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1016/j.jag.2024.104217","RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Earth and Planetary Sciences","Score":null,"Total":0}
An approach for predicting landslide susceptibility and evaluating predisposing factors
Effectively leveraging landslide spatial location information is crucial for improving the accuracy of deep learning in predicting landslide susceptibility and exploring the impacts of predisposing factors. Current single deep learning models for landslide susceptibility assessment require enhancements in both prediction accuracy and robustness. Inclusion of non-interrelated positional information among samples leads to reduced prediction accuracy and challenges in quantifying landslide risk covariates. This study proposes a landslide susceptibility assessment method that integrates ensemble learning with geographically weighted concepts. Using a stacking method, a 1D convolutional neural network (1D-CNN), a recurrent neural network (RNN), and a long short-term memory (LSTM) network were combined to form the CRNN-LSTM ensemble model. Additionally, we constructed a deep learning geographically weighted regression (GW-DNN) model based on the deep learning principles and geographically weighted regression to quantify the impacts of landslide-predisposing factors.The experimental results show that the CRNN-LSTM model achieved AUC values of 0.977 and 0.961 on the training and validation sets, significantly outperforming the individual classifiers (AUC of 0.944 and 0.940 for the 1D-CNN model, 0.950 and 0.948 for the RNN model, and 0.956 and 0.952 for the LSTM model). Additionally, the GW-DNN model achieved R2 coefficients of 0.876 and 0.860 during the training and validation phases. These findings indicate that our proposed method not only highly accurately predicts landslide susceptibility but also provides a precise quantitative assessment of the impact of landslide-predisposing factors at specific spatial points (landslide units) in high-risk areas. These findings offer valuable technical support for landslide disaster prevention and mitigation.
期刊介绍:
The International Journal of Applied Earth Observation and Geoinformation publishes original papers that utilize earth observation data for natural resource and environmental inventory and management. These data primarily originate from remote sensing platforms, including satellites and aircraft, supplemented by surface and subsurface measurements. Addressing natural resources such as forests, agricultural land, soils, and water, as well as environmental concerns like biodiversity, land degradation, and hazards, the journal explores conceptual and data-driven approaches. It covers geoinformation themes like capturing, databasing, visualization, interpretation, data quality, and spatial uncertainty.