Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China - Wang, Yi; Fang, Zhice; Hong, Haoyuan (The Science of the total environment, 2019)
Assessments of landslide disasters are becoming increasingly urgent. The aim of this study is to investigate a convolutional neural network (CNN) framework for landslide susceptibility mapping (LSM) in Yanshan County, China. The two primary contributions of this study are summarized as follows. First, to the best of our knowledge, this report describes the first time that the CNN framework is used for LSM. Second, different data representation algorithms are developed to construct three novel CNN architectures. In this work, sixteen influencing factors associated with landslide occurrence were considered and historical landslide locations were randomly divided into training (70% of the total) and validation (30%) sets. Validation of these CNNs was performed using different commonly used measures in comparison to several of the most popular machine learning and deep learning methods. The experimental results demonstrated that the proportions of highly susceptible zones in all of the CNN landslide susceptibility maps are highly similar and lower than 30%, which indicates that these CNNs are more practical for landslide prevention and management than conventional methods. Furthermore, the proposed CNN framework achieved higher or comparable prediction accuracy. Specifically, the proposed CNNs were 3.94%–7.45% and 0.079–0.151 higher than those of the optimized support vector machine (SVM) in terms of overall accuracy (OA) and Matthews correlation coefficient (MCC), respectively. [Display omitted] •Convolutional neural networks for landslide susceptibility mapping are carried out for the first time.•Three novel data representation algorithms are developed to fit the CNN architectures.•A comparative study on the proposed methods under the CNN framework is implemented in Yanshan County, China.•The proposed CNNs are superior to the state-of-the-art marching learning and deep learning methods.