地质科技通报2025，Vol.44Issue(4)：316-329,14.DOI:10.19509/j.cnki.dzkq.tb20240091
基于Pearson卡方检验算法评价指标优选的波密-墨脱地区泥石流易发性评价
Evaluation of debris flow susceptibility in Bomi-Motuo area using Pearson Chi-square test algorithm based indicator optimization
摘要
Abstract
Complex geomorphic units and active geological structures provide favorable conditions for debris flow in Tibet,which poses a great threats to human life and property.[Objective]The evaluation of debris flow susceptibility can identify key areas for disaster reduction and prevention in this region.[Methods]Taking Bomi and Motuo Counties of Tibet Autonomous Region as the study area,12 factors with high influence on debris flow,including elevation,slope,stratigraphic lithology and rainfall,were selected by Pearson Chi-square test algorithm as evaluation indexes.Data collected from 282 sits with and without debris flows in the study area were taken as the sample database.Based on ArcGIS platform,four susceptibility evaluation models were established by using Information Value Method and Machine Learning Method.The ROC curve and AUC index were introduced to evaluate the accuracy of debris flow susceptibility obtained from the proposed methods.[Results]A debris flow susceptibility map for the study area was obtained.[Conclusion]The results indicate that:(1)Considering different types of debris flows in different dimensions and controlling factors,the normalization coefficients of latitude and temperature are used as the evaluation index of debris flow susceptibility,which can eliminate the excessive responses of debris flow to temperature in low altitude areas to a certain extent.(2)Air temperature,distance from water system,distance from road,formation lithology and elevation are the main factors of debris flow occurrence in the study area;Factors such as vegetation coverage,terrain humidity,and slope also play an important role.(3)Considering the relationship between the disaster points of debris flows and the classification attributes of the impact factors,the classification attributes of the impact factors are assigned scores and trained as input features.The machine learning model performs well,and its average AUC is 0.980,which was better than the traditional information models.(4)The AUC of SVM model is as high as 0.987,and the FR value of the highly prone region is 41.13.The prediction area of high-risk regions takes up the smallest proportion,demonstrating superior high-precision prediction capability in large-scale regions.关键词
卡方检验/评价指标优选/泥石流/易发性评价/高精度/信息量/机器学习/波密-墨脱地区Key words
Chi-square test/indicator optimization/debris flow/susceptibility evaluation/high precision/information value/machine learning/Bomi-Motuo area分类
天文与地球科学引用本文复制引用
李群,徐红剑,杨金,王林康,孙靖宜,章广成..基于Pearson卡方检验算法评价指标优选的波密-墨脱地区泥石流易发性评价[J].地质科技通报,2025,44(4):316-329,14.基金项目
扎墨公路沿线地质灾害发育规律与工程影响评价研究 （）
