安全与环境工程2026，Vol.33Issue(1)：69-85,17.DOI:10.13578/j.cnki.issn.1671-1556.20250852
综合负样本优化指数与CNN-LSTM-ATT模型的滑坡易发性评价
Integration of negative sample optimization index and CNN-LSTM-ATT model for landslide susceptibility assessment
摘要
Abstract
Traditional random sampling of non-landslide introduces high uncertainty with low accuracy during landslide susceptibility assessment.Moreover,computational models often struggle to effectively process multidimensional data and improve prediction accuracy.To address these challenges,this study proposes the negative sample optimization index(NSI)to enhance the selection strategy for non-landslide samples and employs a deep neural network model integrating attention mechanism(ATT),convolutional neural network(CNN),and long short-term memory(LSTM)for landslide susceptibility assessment in Yihe Town,Suide County,located in the Loess Plateau of Shaanxi Province.Firstly,a total of fourteen landslide influencing factors were considered as the input variables to estimate the probability of landslide occurrence.The NSI was calculated by weighting,summing,and normalizing the outputs of three machine learning base models according to their Matthews correlation coefficient(MCC)scores.Then,the NSI and historical landslide records were used to construct the model training database.Finally,the CNN-LSTM-ATT model was applied for landslide susceptibility assessment,with factor importance analyzed using Shapley additive explanations(SHAP).The results indicate that NSI improves the quality of non-landslide samples by constraining the sampling space,thereby mitigating prediction errors induced by excessively biased negative samples,with the model accuracy increasing by up to 7%.Meanwhile,compared to individual models,the CNN-LSTM-ATT model,which integrates multiple complex layers,demonstrates a high performance up to 0.925.Additionally,slope,elevation,and distance to buildings are the key factors influencing the landslide susceptibility mapping in the study area.The proposed sampling strategy and CNN-LSTM-ATT model provide valuable technical support for the spatial prediction of landslides in the Loess Plateau region.关键词
滑坡灾害/易发性/负样本优化指数(NSI)/卷积神经网络(CNN)/长短时记忆(LSTM)网络/注意力机制(ATT)Key words
landslide/landslide susceptibility/negative sample optimization index(NSI)/convolutional neural network(CNN)/long short-term memory(LSTM)network/attention mechanism(ATT)分类
资源环境引用本文复制引用
曹琰波,移康军,梁鑫,荆海宇,孙颢宸,张越轩,刘思缘,范文..综合负样本优化指数与CNN-LSTM-ATT模型的滑坡易发性评价[J].安全与环境工程,2026,33(1):69-85,17.基金项目
国家重点研发计划项目(2022YFC3003401) （2022YFC3003401）
陕西省自然科学基础研究计划项目(2025JC-YBQN-336) （2025JC-YBQN-336）
中央高校基本科研业务费专项资金项目(300102265104) （300102265104）
2023年长安大学教育教学改革研究项目(ZY202364) （ZY202364）
