Integration of dynamic rainfall data with environmental factors to forecast debris flow using an improved GMDH model

The objective of this study was to apply an improved Group Method of Data Handling (GMDH) network model for prediction of debris flow by integrating dynamic rainfall data and environmental factors. The rainfall data were collected from weather information, and the environmental data were extracted from RS, GIS, drilling data, and geophysical data. The input variables used in the SAGA-GMDH model were derived from six variables acquired by Kernel Linear Discriminant Analysis (KLDA). The results showed that the GMDH for prediction of debris flow performed well using the training, validation, and testing sets (R2 above 0.80 and ARE below 3.54%). The SAGA-GMDH model was subsequently compared with a back-propagation (BP) neural network model and adaptive network fuzzy interference system (ANFIS). The accuracies of the SAGA-GMDH model prediction were slightly better than those of other two models, which demonstrated that the SAGA-GMDH model was more suitable for prediction of debris flow.