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基于梯度提升的优化集成机器学习算法对滑坡易发性评价:以雅鲁藏布江与尼洋河两岸为例

Evaluation of Landslide Susceptibility by Optimization Integrated Machine Learning Algorithm Based on Gradient Boosting:Take Both Banks of Yarlung Zangbo River and Niyang River as Examples
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摘要 雅鲁藏布江与尼洋河两岸地质构造活跃,山体滑坡时常发生,滑坡易发性评价能有效的减少因灾害发生所造成的对人类生命和财产的伤害。笔者基于基尼系数的加权随机森林、XGBoost和LightGBM算法在滑坡易发性中的性能。选取188个滑坡样本和7个影响因素,应用五折交叉验证法训练模型,训练过程中同时考虑特征选择算法、运用贝叶斯方法优化超参数后,采用precision、recall、F1、Accuracy指标对各个级别的预测结果进行分析。结果表明:在高程为32~1544 m与2722~3752 m、坡度为30°~40°、距断裂带、河流与道路200 m以内的区域最容易发生滑坡;滑坡极高与高易发性分布为12.14%和12.41%,低和极低易发性占比分别为26.47%与29.55%,区内一半以上的地区不容易发生滑坡灾害;LightGBM模型在所有模型中表现最好,AUC值为0.8432,准确度为0.8531,F1分数为0.8345;墨脱县的达木乡、帮辛乡,林芝县的丹娘、里龙、扎西饶登乡,朗县的陇村,工布江达的江达乡位于极高易发区,发生滑坡概率极大,在这些地区应采取相应的地质灾害防治措施。 The geological structures on both banks of the Yarlung Zangbo river and the Niyang river are active,and landslides occur frequently.The landslide susceptibility assessment can effectively reduce the damage to human life and property caused by disasters.This paper studies the performances of Weighted Random Forests,XGBoost and LightGBM algorithms based on Gini coefficient in landslide susceptibility.Select 188 landslide samples and 7 influencing factors,and use the 50-fold cross-validation method to train the model.During the training process,the feature selection algorithm is considered at the same time,and the Bayesian method is used to optimize the hyperparameters.Analysis of forecast results at the level.The results show that landslide is most likely to occur within the elevation of 32~1544 m and 2722~3752 m,the gradient of 30°~40°,and the distance of 200 m from the fault zone,river and road.The extremely high and high landslide prone areas account for 12.14%and 12.41%respectively,and the low and extremely low landslide prone areas account for 26.47%and 29.55%respectively.More than half of the areas in Nyingchi prefecture are not prone to landslide disasters.Among all models,LightGBM model performs best,with AUC value of 0.8432,accuracy of 0.8531,and F1 score of 0.8345.Damu township and Bangxin township in Motuo county,Danniang,Lilong,Zhaxi Raodeng township in Linzhi county,Long village in Lang county,and Jiangda township in Gongbujiangda county are positioned in extraordinarily high-risk areas,with a excessive likelihood of landslides.Corresponding prevention and control measures should be taken in these areas.
作者 林琴 郭永刚 吴升杰 臧烨祺 王国闻 LIN Qin;GUO Yonggang;WU Shengjie;ZANG Yeqi;WANG Guowen(College of Water Conservancy and Civil Engineering,Tibet Agriculture and Animal Husbandry University,Linzhi 860000,Xizang,China)
出处 《西北地质》 CSCD 北大核心 2024年第1期12-22,共11页 Northwestern Geology
基金 国家自然科学基金重点支持项目“高原重大工程地质灾害监测与分析”(U21A20158) 西藏农牧学院研究生创新计划“基于层次分析法的林芝地区滑坡灾害稳定性模糊综合评价”(YJS2022-25) 西藏自治区科技重点研发计划项目“基于大数据下西藏重大水电工程强震监测关键技术”(XZ202201ZY0034G)联合资助。
关键词 梯度提升 XGBoost LightGBM 机器学习 滑坡易发性 gradient boosting XGBoost LightGBM machine learning landslide susceptibility
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