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首页> 外文期刊>Nordic hydrology >Rainfall-induced landslide susceptibility assessment using random forest weight at basin scale
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Rainfall-induced landslide susceptibility assessment using random forest weight at basin scale

机译：利用流域尺度上的随机森林权重评估降雨诱发的滑坡敏感性

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摘要

Rainfall-induced landslide susceptibility assessment is currently considered an effective tool for landslide hazard assessment as well as for appropriate warning and forecasting. As part of the assessment procedure, a credible index weight matrix can strongly increase the rationality of the assessment result. This study proposed a novel weight-determining method by using random forests (RFs) to find a suitable weight. Random forest weights (RFWs) and eight indexes were used to construct an assessment model of the Dongjiang River basin based on fuzzy comprehensive evaluation. The results show that RF identified the elevation (EL) and slope angle (SL) as the two most important indexes, and soil erodibility factor (SEF) and shear resistance capacity (SRC) as the two least important indexes. The assessment accuracy of RFW can be as high as 79.71%, which is higher than the entropy weight (EW) of 63.77%. Two experiments were conducted by respectively removing the most dominant and the weakest indexes to examine the rationality and feasibility of RFW; both precision validation and contrastive analysis indicated the assessment results of RFW to be reasonable and satisfactory. The initial application of RF for weight determination shows significant potential and the use of RFW is therefore recommended.
机译：目前，降雨引起的滑坡敏感性评估被认为是滑坡灾害评估以及适当的预警和预报的有效工具。作为评估程序的一部分，可靠的指标权重矩阵可以极大地提高评估结果的合理性。这项研究提出了一种新的权重确定方法，即使用随机森林（RF）来找到合适的权重。基于模糊综合评价，采用随机森林权重（RFWs）和8个指标构建东江流域评价模型。结果表明，RF确定高程（EL）和倾斜角（SL）是两个最重要的指标，土壤易蚀性因子（SEF）和抗剪切能力（SRC）是两个最不重要的指标。 RFW的评估准确性可以高达79.71％，高于63.77％的熵权（EW）。通过分别去除最主要和最弱指标进行了两个实验，以检验RFW的合理性和可行性。精密度验证和对比分析均表明RFW的评估结果是合理且令人满意的。 RF在体重测定中的最初应用显示出巨大的潜力，因此建议使用RFW。

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