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• W3039356875 endingPage "107333" @default.
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• W3039356875 abstract "Empirical-statistical models of debris-flow are challenging to implement in environments where sedimentary and hydrologic triggering processes change through time, such as after a large earthquake. The flexible and adaptive statistical methods provided by machine learning algorithms may improve the quality of debris flow predictions where triggering conditions and the nature of sediment that can bulk flows varies with time. We developed a hybrid machine-learning model of future debris-flow volumes using a dataset of measured debris-flow volumes from 60 catchments that generated post-Wenchuan Earthquake (Mw 7.9) debris flows. We input topographic variables (catchment area, topographic relief, channel length, distance from seismic fault, and average channel gradient) and the total volume of co-seismic landslide debris into the PSO-ELM_AdaBoost machine-learning model, created by combining Extreme learning machine (ELM), particle swarm optimization (PSO) and adaptive boosting machine learning algorithm (AdaBoost). The model was trained and tested using post-2008 Mw 7.9 Wenchuan Earthquake debris flows, then applied to understand potential volumes of post-earthquake debris flows associated with other regional earthquakes (2013 Mw 6.6 Lushan Earthquake, 2010 Mw 6.9 Yushu Earthquake). We compared the PSO-ELM_Adaboost method with different machine learning methods, including back-propagation neural network (BPNN), support vector machine (SVM), ELM, PSO-ELM. The Comparative analysis demonstrated that the PSO-ELM_Adaboost method has a higher statistical validity and prediction accuracy with a mean absolute percentage error (MAPE) less than 0.10. The prediction accuracy of debris-flow volumes trigged by other earthquakes decreases to 0.11–0.16 (absolute percentage error), suggesting that once calibrated for a region this method can be applied to other regional earthquakes. This model may be useful for engineering design to mitigate the risk of large post-earthquake debris flows." @default.
• W3039356875 created "2020-07-10" @default.
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• W3039356875 date "2020-10-01" @default.
• W3039356875 modified "2023-10-13" @default.
• W3039356875 title "A hybrid machine-learning model to estimate potential debris-flow volumes" @default.
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• W3039356875 doi "https://doi.org/10.1016/j.geomorph.2020.107333" @default.
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