Integrating a multi-dimensional deep convolutional neural network with optimized sample selection for landslide susceptibility assessment - Yueyue Wang; Xueling Wu; Kun Zhou; Guo Lin; Bo Peng; Zhice Fang (Geo-spatial Information Science)
To address the errors of negative samples in landslide susceptibility modeling and traditional methods in exploring the regularities hidden in the evaluation factors, this paper proposes a stacking one- and three-dimensional Convolutional Neural Network (Stacking-1D-3D-CNN) landslide susceptibility assessment method considering sample optimization selection. First, in order to select negative samples rationally, this paper adopts the Relative Frequency Ratio combined with Certainty Factor Method (RFR-CFM) to determine the negative samples; secondly, the Stacking-1D-3D-CNN proposed is combined with RFR-CFM for the first time for landslide susceptibility assessment. In this work, the negative samples determined by RFR-CFM and the Information Quality Model (IQM) were combined with historical disaster points to form a total modeling sample, and modeled at different ratios. Finally, it is compared with several other models in terms of the landslide hazard susceptibility zoning results, prone zone statistics, and model performance. The findings show that the degree of spatial aggregation of training samples and testing samples has a much greater impact on the accuracy of landslide susceptibility modeling than the impact of their proportions. Furthermore, compared with other models, RFR-CFM-Stacking-1D-3D-CNN has the highest AUC value, precision, recall, F-score, and accuracy, which are 0.95, 0.83, 0.89, 0.85, and 84.76%, respectively, and the lowest RMSE and MAE, 0.39 and 0.15, respectively. This proves the RFR-CFM sample selection method’s rationality and the Stacking-1D-3D-CNN model’s effectiveness.