A Comparative Assessment of Sampling Ratios Using Artificial Neural Network (ANN) for Landslide Predictive Model in Langat River Basin, Selangor, Malaysia
A Comparative Assessment of Sampling Ratios Using Artificial Neural Network (ANN) for Landslide Predictive Model in Langat River Basin, Selangor, Malaysia
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DOI：
10.3390/su15010861
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发表时间：
2023-01
期刊：
影响因子：
3.9
通讯作者：
S. N. Selamat;N. Abd Majid;A. Mohd Taib
中科院分区：
文献类型：
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作者：
S. N. Selamat;N. Abd Majid;A. Mohd Taib
山体滑坡已被列为世界上最危险的威胁，造成巨大的财产损失和生命损失。在滑坡易发地区，人类活动的增加是滑坡发生风险的一个主要因素。因此，机器学习已被用于滑坡研究，以建立滑坡预测模型。本研究的主要目的是利用人工神经网络（ann）对Langat河流域（LRB）滑坡预测模型进行最合适的采样比评估。采用4种抽样比例（50:50、60:40、70:30、80:20）将滑坡清单随机分为训练数据集和测试数据集。本次研究共考虑了12个滑坡影响因素，包括高程、坡度、坡向、曲率、地形湿度指数（TWI）、道路距离、河流距离、断层距离、土壤、岩性、土地利用和降雨量。采用一定的统计指标和曲线下面积（AUC）建立评价模型。最后，利用复合因子法（CF）对模型进行验证，选择最合适的预测模型。结果显示，80:20比例的预测模型具有较强的现实性，在众多预测模型中排名第一。训练数据集的AUC值为0.931，测试数据集的AUC值为0.964。这些尝试对于选择训练样本与测试样本的最佳比例，为LRB建立一个可靠、完整的滑坡预测模型有很大的帮助。
Landslides have been classified as the most dangerous threat around the world, causing huge damage to properties and loss of life. Increased human activity in landslide-prone areas has been a major contributor to the risk of landslide occurrences. Therefore, machine learning has been used in landslide studies to develop a landslide predictive model. The main objective of this study is to evaluate the most suitable sampling ratio for the predictive landslide model in the Langat River Basin (LRB) using Artificial Neural Networks (ANNs). The landslide inventory was divided randomly into training and testing datasets using four sampling ratios (50:50, 60:40, 70:30, and 80:20). A total of 12 landslide conditioning factors were considered in this study, including the elevation, slope, aspect, curvature, topography wetness index (TWI), distance to the road, distance to the river, distance to faults, soil, lithology, land use, and rainfall. The evaluation model was performed using certain statistical measures and area under the curve (AUC). Finally, the most suitable predictive model was chosen based on the model validation results using the compound factor (CF) method. Based on the results, the predictive model with an 80:20 ratio indicates a realistic finding and was classified as the first rank among others. The AUC value for the training dataset is 0.931, while the AUC value for the testing dataset is 0.964. These attempts will help a great deal when it comes to choosing the best ratio of training samples to testing samples to create a reliable and complete landslide prediction model for the LRB.