顾及多方法集成特征选择与负样本优化的滑坡易发性评价

Landslide susceptibility assessment considering multi-method integratedfeature selection and negative sample optimization

针对滑坡易发性评价中特征因子选取主观性强、滑坡负样本选取随机性强,导致预测精度较低的问题,本文提出了采用多方法集成选择特征因子,并结合信息量法优化负样本提取的滑坡易发性评价方法。以四川省巴中市为例,首先,将最大相关最小冗余(mRMR)、梯度提升树(GBT)、极端梯度提升树(XGBoost)、普通最小二乘法(OLS)、信息增益法(IG)5种方法特征选择的结果归一化并累加得到综合评分。其次,通过信息量法选取负样本构建样本数据集。然后,采用支持向量机(SVM)模型进行滑坡易发性分析,并与逻辑回归(LR)模型做对比试验。最后,从滑坡灾害易发性分区图、点密度统计、ROC曲线3个方面,验证预测结果的准确性。试验结果表明,本文提出的多方法集成特征选择并应用信息量法进行负样本优化可以有效提高模型的预测精度,易发性评价结果更为准确可靠。

In view of the problem that the selection of characteristic factors in landslide susceptibility evaluation is highly subjective and the selection of landslide negative samples is highly random,resulting in low prediction accuracy,this paper proposes a landslide susceptibility evaluation method that uses multi-method integration to select characteristic factors and combines the information volume method to optimize the extraction of negative samples.Taking Bazhong city,Sichuan province as an example,the results of feature selection by five methods,namely maximum relevance minimum redundancy(mRMR),gradient boosting trees(GBT),extreme gradient boosting(XGBoost),ordinary least squares(OLS),and information gain(IG)are first normalized and accumulated to obtain a comprehensive score.Secondly,negative samples are selected by the information volume method to construct a sample data set.Then,the support vector machine(SVM)model is used to analyze landslide susceptibility,and a comparative experiment is conducted with the logistic regression(LR)model.Finally,the accuracy of the prediction results is verified from three aspects:landslide susceptibility zoning map,point density statistics,and ROC curve.The experimental results show that the multi-method integrated feature selection proposed in this paper and the application of information volume method for negative sample optimization can effectively improve the prediction accuracy of the model,and the susceptibility evaluation results are more accurate and reliable.