An artificial neural network model to predict debris-flow volumes caused by extreme rainfall in the central region of South Korea
| Autor: | Hwan-Hui Lim, Shin-Kyu Choi, Enok Cheon, Seung-Rae Lee, Deuk-Hwan Lee, Yun-Tae Kim |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
geography
Watershed geography.geographical_feature_category Watershed area 0211 other engineering and technologies Training (meteorology) Geology Regression analysis 02 engineering and technology 010502 geochemistry & geophysics Geotechnical Engineering and Engineering Geology 01 natural sciences Central region Debris flow Statistics Predictability Channel (geography) 021101 geological & geomatics engineering 0105 earth and related environmental sciences |
| Zdroj: | Engineering Geology. 281:105979 |
| ISSN: | 0013-7952 |
| DOI: | 10.1016/j.enggeo.2020.105979 |
| Popis: | 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 R2 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 R2 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 R2 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. |
| Databáze: | OpenAIRE |
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