Abstract
The main goal of this study was to use the synthetic minority oversampling technique (SMOTE) to expand the quantity of landslide samples for machine learning methods (i.e., support vector machine (SVM), logistic regression (LR), artificial neural network (ANN), and random forest (RF)) to produce high-quality landslide susceptibility maps for Lishui City in Zhejiang Province, China. Landslide-related factors were extracted from topographic maps, geological maps, and satellite images. Twelve factors were selected as independent variables using correlation coefficient analysis and the neighborhood rough set (NRS) method. In total, 288 soil landslides were mapped using field surveys, historical records, and satellite images. The landslides were randomly divided into two datasets: 70% of all landslides were selected as the original training dataset and 30% were used for validation. Then, SMOTE was employed to generate datasets with sizes ranging from two to thirty times that of the training dataset to establish and compare the four machine learning methods for landslide susceptibility mapping. In addition, we used slope units to subdivide the terrain to determine the landslide susceptibility. Finally, the landslide susceptibility maps were validated using statistical indexes and the area under the curve (AUC). The results indicated that the performances of the four machine learning methods showed different levels of improvement as the sample sizes increased. The RF model exhibited a more substantial improvement (AUC improved by 24.12%) than did the ANN (18.94%), SVM (17.77%), and LR (3.00%) models. Furthermore, the ANN model achieved the highest predictive ability (AUC = 0.98), followed by the RF (AUC = 0.96), SVM (AUC = 0.94), and LR (AUC = 0.79) models. This approach significantly improves the performance of machine learning techniques for landslide susceptibility mapping, thereby providing a better tool for reducing the impacts of landslide disasters.
Highlights
Landslides, which result in the loss of human life and property, represent some of the most destructive natural disasters in the world [1]
The predictive abilities of the four landslide models improved as the number of training datasets increased, as shown in Figure 10d; the random forest (RF) model improved the most (AUC increased by 24.12%), and the artificial neural network (ANN) and support vector machine (SVM) models had almost the same improvement (AUC increased by 18.94% and 17.77%, respectively), whereas the logistic regression (LR) model improved the least (AUC increased by 3.00%)
The novel contribution of this study is its application of synthetic minority oversampling technique (SMOTE) to oversample the landslide data to obtain a sufficient training dataset for machine learning models for landslide susceptibility mapping
Summary
Landslides, which result in the loss of human life and property, represent some of the most destructive natural disasters in the world [1]. Mountainous areas are especially to landslides, which are controlled by complex geographical environments and human impacts [2,3,4]. In China, landslides and debris falls occur over 20,000 times a year on average, resulting in more than 1000 casualties, affecting more than 900,000 disaster-stricken people, and causing 2–6 billion yuan of direct economic losses [6]. Zhejiang Province has been impacted by landslides due to its mountainous environment. A serious landslide occurred in Lishui City, Zhejiang Province, on September 28, 2016, destroying 20 residential buildings and causing 27 people to become lost [7]. Landslide susceptibility maps, which depict the spatial distribution of the likelihood of a landslide occurring, are vital for mitigating the effects of landslides through appropriate decision making in landslide-prone regions [8]
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