摘要
目前滑坡易发性评价方法多为统计学方法评价和机器学习模型评价,存在样本误差和模型参数选取的误差等问题,模型训练的复杂性与模型预测结果的不确定性限制了机器学习模型在易发性领域的运用和发展。文章以宜昌市秭归县为研究区域,选取高程、归一化植被指数、地层岩性等常见评价因子,将贝叶斯优化算法、麻雀搜索算法(sparrow search algorithm,SSA)以及大猩猩优化算法(gorilla troops optimization,GTO)与BP神经网络相结合,获取最优参数对模型进行训练,最终构建贝叶斯-BP、SSA-BP和GTO-BP模型,对整个研究区进行预测并完成易发性评价。采用AUC值、F1分数、准确率等统计学标准对模型进行综合评价,并采用K折验证评估模型性能,使用SHAP(shapley additive explanation)分析对模型进行可解释分析。结果显示:3个优化模型的准确率、精确度、F1分数等指标均远高于BP单模型,表明算法优化效果明显,对比优化模型之间多项评价指标,结果表明GTO-BP模型更适用于秭归县的滑坡易发性评价,SHAP分析结果具有可解释性,可以为该地区未来滑坡防治提供技术支持。
Current landslide susceptibility evaluation methods predominantly rely on statistical techniques and machine learning models,both of which are prone to errors related to sample quality and parameter selection.The complexity of model training and the uncertainty of prediction results limit the broader application and development of machine learning models in this field.To address these issues,this study evaluates landslide susceptibility in Zigui County,Yichang City,using a backpropagation(BP)neural network optimized by three algorithms:Bayesian optimization,sparrow search algorithm(SSA),and gorilla troops optimization(GTO).Common susceptibility evaluation factors,such as elevation,NDVI,and stratigraphic lithology are used as inputs.Three hybrid models—Bayesian-BP,SSA-BP,and GTO-BP are constructed and trained using the respective algorithms.The models are comprehensively evaluated using statistical standards such as AUC value,F1 score,and accuracy,with K-fold cross-validation for robustness.Additionally,SHAP(shapley additive explanations)is used to enhance interpretability of the models.The results show that the accuracy,precision,F1 score,and other indicators of the three optimized models are significantly higher than those of the standalone BP model,confirming the effectiveness of the optimization strategies.Among the optimized models,the GTO-BP model exhibits superior overall performance and is better suited for landslide susceptibility evaluation in Zigui County.In addition,the SHAP analysis provides interpretable results,offering valuable technical support for future landslide prevention and mitigation efforts in the region.
作者
陆昊
卢书强
李佳乐
林振
李勇
LU Hao;LU Shuqiang;LI Jiale;LIN Zhen;LI Yong(National Filed Observation and Research Station of Landslides in Three Gorges Reservoir Area of Yangtze River,Yichang,Hubei 443002,China;School of Civil Engineering and Architecture,Three Gorges University,Yichang,Hubei 443002,China;Zongyang County Public Transportation Service Center,Tongling,Anhui 246700,China;China Construction Fourth Engineering Divion Co.Ltd.,Guangzhou,Guangdong 510665,China;Hydrogeology and Engineering Geology Institute of Hubei Geological Bureau,Jingzhou,Hubei 434020,China)
出处
《中国地质灾害与防治学报》
2025年第4期160-174,共15页
The Chinese Journal of Geological Hazard and Control
基金
国家自然科学基金项目(U23A2045)。
关键词
滑坡易发性评价
优化算法
BP神经网络模型
K折验证
SHAP分析
秭归县
landslide susceptibility assessment
optimization algorithm
BP neural network model
K-fold validation
SHAP analysis
Zigui County
