Methods for Evaluating Debris Flow Susceptibility Based on OOD Generalization Verification and Deep Fully Connected Neural Networks - Pengning GUO; Huige XING; Congjiang LI; Yuxin WU; Haibo LI (工程科学与技术, 2024)
More–accurate predictions of susceptibility to debris flows would help greatly in the early identification and large-scale monitoring of debris–flow disasters. Some existing models for doing so perform well during training and test but less well in practice, and this has adverse effects on engineering site selection and disaster prevention and mitigation. To find a method with high accuracy in training, prediction, and application, the present study considered four models—i.e., deep fully connected neural network, gradient boosting decision tree, random forest, and Bayesian network and assessed them for accuracy and out-of-distribution (OOD) generalization. Taking Ya’an City in China’s Sichuan Province as an example, small watershed units were used for regional meshing, and the data were divided randomly into a training set and a test set at the ratio of 7:3. During data cleansing, missing values were tackled using a K–means and IterativeImputer method, and excrescent data were rejected using the 3–sigma rule. After analyzing the collinearity, sensitivity, and predictive ability of various debris–flow susceptibility factors, 14 were selected for model operation. The four aforementioned models were then constructed for debris–flow susceptibility prediction and model comparison, and accuracy evaluation and OOD generalization verification showed that the deep fully connected neural network model was superior to the other three models in terms of AUC (0.027 higher), Acc (0.02 higher), Recall (0.02 higher), MAE (0.003 lower), and OOD generalization (0.056 higher). The results show that deep fully connected neural networks are good at predicting debris–flow susceptibility, thereby improving the accuracy and adaptability of debris–flow evaluation and providing a new way to evaluate debris–flow susceptibility.