Abstract
Landslide susceptibility mapping is a method used to assess the probability and spatial distribution of landslide occurrences. Machine learning methods have been widely used in landslide susceptibility in recent years. In this paper, six popular machine learning algorithms namely logistic regression, multi-layer perceptron, random forests, support vector machine, Adaboost, and gradient boosted decision tree were leveraged to construct landslide susceptibility models with a total of 1365 landslide points and 14 predisposing factors. Subsequently, the landslide susceptibility maps (LSM) were generated by the trained models. LSM shows the main landslide zone is concentrated in the southeastern area of Wenchuan County. The result of ROC curve analysis shows that all models fitted the training datasets and achieved satisfactory results on validation datasets. The results of this paper reveal that machine learning methods are feasible to build robust landslide susceptibility models.
Highlights
Landslides, as one of the natural disasters, thread human society around the world
Machine learning methods have received a lot of attention from researchers and performed satisfactory results[2]
Multi-layer perceptron is a neural network that includes input, hidden, and output layers. It is different from logistic regression, the hidden layer can contain one or more non-linear activation function which can improve the capability of solving the complex problem[5]
Summary
Landslides, as one of the natural disasters, thread human society around the world. Assessment of landslide susceptibility is one of practical methods to manage landslide, which mainly includes qualitative and quantitative methods[1]. Machine learning methods have received a lot of attention from researchers and performed satisfactory results[2]. Different from the past, this paper used six popular machine learning algorithms to map the landslide susceptibility. The datasets contain the landslides triggered by the Great Wenchuan Earthquake were leveraged for model construction. We used the trained models to predict each pixel and map the susceptibility of Wenchuan County, a landslide-prone area
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