Landslide susceptibility mapping is well recognized as an essential element in supporting decision-making activities for preventing and mitigating landslide hazards as it provides information regarding locations where landslides are most likely to occur. The main purpose of this study is to produce a landslide susceptibility map of Mt. Umyeon in Korea using an artificial neural network (ANN) involving the factor selection method and various non-linear activation functions. A total of 151 historical landslide events and 20 predisposing factors consisting of Geographic Information System (GIS)-based morphological, hydrological, geological, and land cover datasets were constructed with a resolution of 5 x 5 m. The collected datasets were applied to information gain ratio analysis to confirm the predictive power and multicollinearity diagnosis to ensure the correlation of independence among the landslide predisposing factors. The best 11 predisposing factors that were selected in this study were randomly divided into a 70:30 ratio for training and validation datasets, which were used to produce ANN-based landslide susceptibility models. The ANN model used in this study had a multi-layer perceptron (MLP) structure consisting of an input layer, one hidden layer, and an output layer. In the output layer, the logistic sigmoid function was used to represent the result value within the range of 0 to 1, and six non-linear activation functions were used for the hidden layer. The performance of the landslide susceptibility models was evaluated using the receiver operating characteristic curve, Kappa index, and five statistical indices (sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV)) with the training dataset. In addition, the landslide susceptibility models were validated using the aforementioned measures with the validation dataset and were compared using the Friedman test to check the significant differences among the six developed models. The optimal number of neurons was determined based on the aforementioned performance evaluation and validation results. Overall, the model with the best performance was the MLP model with the logistic sigmoid activation function in the output layer and the hyperbolic tangent sigmoid activation function with five neurons in the hidden layer. The validation results of the best model showed a sensitivity of 82.61%, specificity of 78.26%, accuracy of 80.43%, PPV of 79.17%, NPV of 81.82%, a Kappa index of 0.609, and AUC of 0.879. The results of this study highlight the effectiveness of selecting an optimal MLP model structure for shallow landslide susceptibility mapping using an appropriate predisposing factor section method.

中文翻译：

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考虑因素选择方法和多种非线性激活函数的基于人工神经网络的浅层滑坡敏感性模型

滑坡敏感性地图被公认为是支持决策活动以预防和减轻滑坡危害的基本要素，因为它提供了有关最有可能发生滑坡的位置的信息。这项研究的主要目的是绘制山的滑坡敏感性图。韩国的Umyeon使用人工神经网络（ANN），涉及因素选择方法和各种非线性激活函数。共构建了151个历史滑坡事件和20个诱发因素，包括基于地理信息系统（GIS）的形态，水文，地质和土地覆盖数据集，其分辨率为5 x 5 m。将收集的数据集用于信息增益比分析，以确认预测能力和多重共线性诊断，以确保滑坡易感因素之间的独立性相关。在这项研究中选择的最佳11个诱发因素被随机分为70:30的比例用于训练和验证数据集，这些数据被用于生成基于ANN的滑坡敏感性模型。本研究中使用的ANN模型具有多层感知器（MLP）结构，该结构由输入层，一个隐藏层和输出层组成。在输出层中，使用逻辑S形函数表示0到1范围内的结果值，并且六个非线性激活函数用于隐藏层。使用接收者的操作特征曲线，Kappa指数和五个统计指标（敏感性，特异性，准确性，阳性预测值（PPV），阴性预测值（NPV））和训练数据集评估了滑坡敏感性模型的性能。此外，滑坡敏感性模型使用验证数据集的上述措施进行了验证，并使用弗里德曼检验进行了比较，以检查六个开发模型之间的显着差异。根据上述性能评估和验证结果确定最佳神经元数量。总体，性能最好的模型是在输出层中具有逻辑乙状结肠激活功能的MLP模型，在隐藏层中具有五个神经元的双曲线正切乙状结肠激活功能。最佳模型的验证结果显示灵敏度为82.61％，特异性为78.26％，准确度为80.43％，PPV为79.17％，NPV为81.82％，Kappa指数为0.609，AUC为0.879。这项研究的结果强调了使用适当的易感因子剖面方法为浅层滑坡敏感性图选择最佳的MLP模型结构的有效性。