【摘要】 以灵台县为例,选取高程、坡度、坡向、地形起伏度、地层岩性、归一化植被指数、多年平均降雨量、河流缓冲区、土地利用和道路缓冲区10个因素作为地质灾害评价因子,利用逻辑回归(LR)、决策树(DT)和随机森林(RF)机器学习模型进行地质灾害易发性评价,用受试者工作特征曲线进行模型预测精度评价.结果表明, RF模型的地质灾害极高易发区包含57.32%的地质灾害点,高于LR和DT模型的54.88%和48.78%; RF模型的地质灾害点密度为0.47处/km~2,高于LR和DT模型,表明RF模型在预测成功率上高于LR和DT模型. RF模型评价结果的曲线下面积为0.883,优于LR和DT模型,其中极高易发区和高易发区面积占比分别为4.9%和13.8%.更多还原

【Abstract】 We taking Lingtai County as an example, selected 10 elements, including elevation, slope, slope direction, topographic relief, stratigraphic lithology, normalized vegetation index, average annual rainfall,river buffer zone, land use and road buffer zone, as geological hazard assessment factors, and used three machine learning models, namely logistic regression(LR), decision tree(DT) and random forest(RF), to evaluate geological hazard susceptibility. The accuracy of model prediction was evaluated via the receiver operating characteristic curve, and the results showed that 57.32% of the geological damage points were in the highly prone area of RF model, which was higher than 54.88% and 48.78% of LR and DT models. The density of geological damage points of RF model was 0.47/km~2, also higher than that of LR and DT models, indicating that the prediction success rate of the former being higher than that of the latters. The area under the curve of RF model was 0.883, which was better than LR and DT models. In the study area, the percentage of extremely prone area and high prone area was 4.9% and 13.8%, respectively.更多还原