Landslide Susceptibility Model Using Artificial Neural Network (ANN) Approach in Langat River Basin, Selangor, Malaysia
Landslide Susceptibility Model Using Artificial Neural Network (ANN) Approach in Langat River Basin, Selangor, Malaysia
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DOI：
10.3390/land11060833
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发表时间：
2022-06
期刊：
影响因子：
3.9
通讯作者：
S. N. Selamat;N. A. Majid;M. Taha;Ashraf Osman
中科院分区：
文献类型：
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作者：
S. N. Selamat;N. A. Majid;M. Taha;Ashraf Osman
山体滑坡是一种自然灾害，可能危及人类生命并造成严重的环境破坏。山体滑坡敏感性图对于规划、管理和预防山体滑坡发生以最大限度地减少损失至关重要。采用多种技术来绘制滑坡敏感性图；然而，它们的能力因研究而异。该研究的目的是利用人工神经网络 (ANN) 绘制马来西亚雪兰莪州冷岳河流域的山体滑坡敏感性地图。滑坡清单地图共包含 140 个滑坡位置，按 70:30 的比例随机分为训练和测试。选择九个滑坡调节因子作为模型输入，包括：高程、坡度、坡向、曲率、地形湿度指数（TWI）、距道路的距离、距河流的距离、岩性和降雨量。曲线下面积（AUC）和几种统计分析指标（敏感性、特异性、准确性、阳性预测值和阴性预测值）用于验证滑坡预测模型。考虑了 ANN 预测模型，并在验证评估中取得了非常好的结果，训练和测试数据集的 AUC 值为 0.940。这项研究发现，降雨是影响冷岳河流域山体滑坡发生的最关键因素，权重指数为0.248，其次是距道路的距离（0.200）和海拔（0.136）。结果显示，最易受影响的地区位于冷岳河流域的东北部。该地图可能有助于开发规划和管理，以防止冷岳河流域发生山体滑坡。
Landslides are a natural hazard that can endanger human life and cause severe environmental damage. A landslide susceptibility map is essential for planning, managing, and preventing landslides occurrences to minimize losses. A variety of techniques are employed to map landslide susceptibility; however, their capability differs depending on the studies. The aim of the research is to produce a landslide susceptibility map for the Langat River Basin in Selangor, Malaysia, using an Artificial Neural Network (ANN). A landslide inventory map contained a total of 140 landslide locations which were randomly separated into training and testing with ratio 70:30. Nine landslide conditioning factors were selected as model input, including: elevation, slope, aspect, curvature, Topographic Wetness Index (TWI), distance to road, distance to river, lithology, and rainfall. The area under the curve (AUC) and several statistical measures of analyses (sensitivity, specificity, accuracy, positive predictive value, and negative predictive value) were used to validate the landslide predictive model. The ANN predictive model was considered and achieved very good results on validation assessment, with an AUC value of 0.940 for both training and testing datasets. This study found rainfall to be the most crucial factor affecting landslide occurrence in the Langat River Basin, with a 0.248 weight index, followed by distance to road (0.200) and elevation (0.136). The results showed that the most susceptible area is located in the north-east of the Langat River Basin. This map might be useful for development planning and management to prevent landslide occurrences in Langat River Basin.