[논문]An artificial neural network model to predict debris-flow volumes caused by extreme rainfall in the central region of South Korea

Lee, Deuk-Hwan; Cheon, Enok; Lim, Hwan-Hui; Choi, Shin-Kyu; Kim, Yun-Tae; Lee, Seung-Rae

doi:10.1016/j.enggeo.2020.105979

[해외논문] An artificial neural network model to predict debris-flow volumes caused by extreme rainfall in the central region of South Korea

Engineering geology, v.281, 2021년, pp.105979 -

Lee, Deuk-Hwan (Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST)) , Cheon, Enok (Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST)) , Lim, Hwan-Hui (Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST)) , Choi, Shin-Kyu (Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST)) , Kim, Yun-Tae (Department of Ocean Engineering, Pukyong National University) , Lee, Seung-Rae (Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology (KAIST))

Abstract ▼ AI-Helper

Abstract In South Korea, the risk of debris-flow is relatively high due to the country's vast mountainous topographical features and intense continuous rainfall during the summer. Debris-flows can result in the loss of human life and severe property damage, which can be made worse due to the poor spatiotemporal predictability of such hazards. Therefore, it is essential to research the preemptive prediction and mitigation of debris-flow hazards. For this purpose, this study developed an ANN model to predict the debris-flow volume based on 63 historical events. By considering the morphology, rainfall, and geology characteristics of the studied area in central South Korea, the data of 15 debris-flow predisposing factors were obtained. Among these data, four predisposing factors (watershed area, channel length, watershed relief, and rainfall data) were selected based on Pearson's correlation analysis to check for significant correlations with the debris-flow volume. To determine the best performing ANN model, a validation testing was carried out involving ten-fold cross-validation with MSE and R 2 using both training and validation datasets, which were randomly split into a 7:3 ratio. The model performance validation results showed that an ANN model with two hidden neurons (4×2×1 architecture) had the highest R 2 value (0.828) and the lowest MSE (0.022). In addition, in a comparative study with other existing regression models, the ANN model showed better results in terms of adjusted R 2 value (0.911) using all datasets. Furthermore, 94% of the observed debris-flow volumes from the ANN model were within 1:2 and 2:1 lines of the predicted volumes. The results of this study have shown the potentiality of the developed ANN model to be a useful resource for decision-making and designing barriers in areas prone to debris-flows in South Korea. Highlights A prediction model for debris-flow volume based on an artificial neural network (ANN) is proposed and verified. The systematic procedure of modeling an ANN model for predicting the debris-flow volume is described. The developed ANN model was compared to other existing regression models. The ANN model showed better results in terms of predicting the debris-flow volume than the existing regression models.

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[해외논문] An artificial neural network model to predict debris-flow volumes caused by extreme rainfall in the central region of South Korea

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[해외논문] An artificial neural network model to predict debris-flow volumes caused by extreme rainfall in the central region of South Korea

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