基于粗糙集的支持向量机滑坡易发性评价

区域滑坡易发性评价对灾害中长期预测预报具有重要意义。以三峡库区秭归至巴东段为研究区,利用粗糙集理论对20个初始评价因子进行属性约简,去掉冗余或干扰信息,得到13个核心评价因子,并以此作为支持向量机的输入特征集,构建支持向量机模型,实现滑坡易发性评价。在易发性分区图中高易发区占8.2%,主要分布在童庄河右岸、归州河沿岸、青干河左岸、树坪至范家坪长江右岸、牛口到东壤口长江左岸和巴东附近;不易发区占52.7%,主要分布于店子湾至巴东旧城以及远离长江水系及植被覆盖度高的区域。通过验证与分析,粗糙集-支持向量机模型在高中易发区中的预测精度为85.6%,其预测能力优于支持向量机模型;与野外调查对比,预测结果与实际情况吻合较好。研究表明,应用粗糙集和支持向量机相结合进行滑坡易发性评价具有预测能力强、计算效率高等优点。

Landslide Susceptibility Assessment Based on Rough Sets and Support Vector Machine

Susceptibility assessment has a great significance to predict and forcast landslide hazards in the mid and long term.The aim is to analyze the landslide susceptibility mapping combining rough sets(RS) theory and support vector machine(SVM) in the Three Gorges Reservoir area of Zigui to Badong County.Rough set theory is used to reduce the redundant information of 20 initial factors in the decision table and determine the kernel included 13 representative factors.Then,the kernel factors are used to train a SVM model,and landslide susceptibility maps were produced.The higher susceptibility zones is about 8.2% of the total study area,and primarily distributed in the right bank of Tongzhuang River,along the Guizhou River,the left bank of Qinggan River,the right bank of the Yangtze River of Shuping to Fanjiaping,Niukou to Dongrangkou and near the Badong.The stability zones are accounted for about 52.7% which mainly distribute in the Dianziwan to Badong,and the areas away from Yangtze River and the areas of high surface cover degree.Through verification and analysis of the results,it shows that the predictive power of the RS-SVM model is superior to the SVM model,and the prediction accuracy of the RS-SVM model is aoubt 85.6%.The method have advantages of excellent predict performacy and efficiency.Combining with the landslide survey data,the evaluation results are basically consistent with the status of the local landslide disasters and it is proved that the proposed method is an effective tool for landslide susceptibility assessment.