A comparative study of regional rainfall-induced landslide early warning models based on RF、CNN and MLP algorithms - Liu, Yanhui; Ma, Shiwei; Dong, Lihao; Xiao, Ruihua; Huang, Junbao; Zhou, Pinggen (Frontiers in Earth Science, 2024)
Landslide disasters, due to their widespread distribution and clustered occurrences, pose a significant threat to human society. Rainfall is considered a primary triggering factor, and the frequent clustering of landslides underscores the importance of early warning systems for regional landslide disasters in preventing and mitigating rainfall-induced landslides. Research on early warning models is crucial for accurately predicting rainfall-induced landslides. However, traditional models face challenges such as the complexity of landslide causes, insufficient data, and limited analysis methods, resulting in low accuracy and inadequate precision. This study focuses on Fujian Province, China, proposing a four-step process for building a regional landslide early warning model based on machine learning. The process includes data integration and cleaning, sample set construction, model training and validation, and practical application. By integrating and cleaning the latest and most detailed data, a training sample set (15,589 samples) for the regional landslide disaster early warning model is established. Three machine learning algorithms-random forest, multilayer perceptron, and convolutional neural network-are employed and compared, the evaluation results indicated that the RF-based warning model achieved an accuracy of 0.930 to 0.957 and an AUC value of 0.955. The CNN-based warning model demonstrated an accuracy of 0.945 to 0.948 with an AUC value of 0.940. The MLP-based warning model achieved an accuracy of 0.930 to 0.953 and an AUC value of 0.930. The results showed comparable accuracy metrics among the three models, with RF exhibiting a significant advantage in AUC values. Finally, the models are applied to the regional landslide disasters induced by heavy rainfall in Fujian Province on August 5, 2021. The results showed that in the binary classification warning strategy, the accuracy of the Random Forest and Convolutional Neural Network was 92.9%, while that of the Multilayer Perceptron was 85.8%, all performing well. In the multiclassification hierarchical warning strategy, the Random Forest excelled, while the performance of the Convolutional Neural Network and Multilayer Perceptron was relatively limited. The findings of this