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1. 新疆大学地质与矿业工程学院
2. 新疆大学计算机科学与技术学院
Published:2025
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[1]李凤霞,刘桂萍,杜光辉,等.基于信息量和机器学习的新疆托克逊县地质灾害易发性评价[J].新疆大学学报(自然科学版中英文),2025,42(04):469-484.
[1]李凤霞,刘桂萍,杜光辉,等.基于信息量和机器学习的新疆托克逊县地质灾害易发性评价[J].新疆大学学报(自然科学版中英文),2025,42(04):469-484. DOI: 10.13568/j.cnki.651094.651316.2024.12.26.0001.
DOI:10.13568/j.cnki.651094.651316.2024.12.26.0001.
地质灾害易发性评价对防灾减灾工作至关重要,有效的评价方法与评价模型在地质灾害易发性评估中发挥着重要作用.本文以ArcGIS为平台,采用信息量法和机器学习方法,构建了IV-RF、IV-XGBoost、IV-CatBoost、IV-KNN四种耦合模型,对托克逊县地质灾害易发性进行评价,并利用SHAP值深入剖析最高AUC值的耦合模型,明确各影响因子对预测结果的贡献.结果表明:在四种模型中,IV-CatBoost模型具有较高精度,其中距道路距离、距水系距离和地形起伏度是最重要的三个影响因子.研究区地质灾害极高易发区、高易发区主要位于阿拉沟山、鱼儿沟、甘沟等沟谷沿线,阿拉沟河和乌斯通沟中下游地区,以及吐-和高速公路(G3012)甘沟段、S301沿线.
The evaluation of geological disaster susceptibility is crucial for disaster prevention and mitigation.It is very important to select effective evaluation methods and models for the assessment of geological disaster susceptibility. By integrating the information quantity method and machine learning to construct four coupling models
namely IV-RF
IV-XGBoost
IV-CatBoost and IV-KNN based on ArcGIS
this paper constructs coupling models for geological disaster susceptibility evaluation in Toksun County. Using SHAP values to deeply analyze the coupled model with the highest AUC value
clarify the contribution of each influencing factor to the prediction results. The results show that the IV-CatBoost model has higher accuracy among the four models. In the IVCatBoost model
the top three factors in terms of importance are the distance from the road
the distance from the water system and the topographic relief. The extremely high and high prone areas of geological disasters are predominantly situated along the valleys of Alagou mountain
Yuergou and Gangou
the middle and lower reaches of Alagou river and Wusitonggou
the Gangou section of Turpan-Hotan expressway(G3012)
and along S301 road.
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