Abstract
Debris flow disasters have plagued Taiwan in recent decades, and caused casualties and destruction of property. Several methods, including the numerical method, statistical method, and experimental method, have been adopted in recent years to predict debris flow, and more recently, the neural network (NN) and the genetic algorithm (GA) methods have been introduced to simulate the occurrence of debris flows. This study proposes using the GA to weigh seven important variables according to principles similar to natural selection. The study then simultaneously inputs these variables into a NN model to predict debris flow occurrences based on relevant factors. There were 154 potential cases of debris flow collected from eastern Taiwan and fed into the model for testing. The average ratio of successful prediction reached 94.94%, which demonstrates that the proposed model can provide stable and reliable results for predicting debris flow in hazard mitigation and guard systems.
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