A multiobjective evolutionary optimization method based critical rainfall thresholds for debris flows initiation |
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| Institution: | Key Laboratory of High-Speed Railway Engineering, MOE/School of Civil Engineering, Southwest Jiaotong University,Chengdu 610031, China;Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731,China;Key Laboratory of Mountain Surface Process and Hazards/Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China;School of Soil and Water Conservation, Beijing Forestry University, Beijing 100083, China;Key Laboratory of High-Speed Railway Engineering, MOE/School of Civil Engineering, Southwest Jiaotong University,Chengdu 610031, China;Sichuan Institute of Land and Space Ecological Restoration and Geological Hazard Prevention, Chengdu 610081, China |
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| Abstract: | At present,most researches on the critical rainfall threshold of debris flow initiation use a linear model obtained through regression.With relatively weak fault tolerance,this method not only ignores nonlinear effects but also is susceptible to singular noise samples,which makes it difficult to characterize the true quantization relationship of the rainfall threshold.Besides,the early warning threshold determined by statistical parameters is susceptible to negative samples(samples where no debris flow has occurred),which leads to uncertainty in the reliability of the early warning results by the regression curve.To overcome the above limitations,this study develops a data-driven multiobjective evolutionary optimization method that combines an artificial neural network(ANN) and a multiobjective evolutionary optimization implemented by particle swarm optimization(PSO).Firstly,the Pareto optimality method is used to represent the nonlinear and conflicting critical thresholds for the rainfall intensity I and the rainfall duration D.An ANN is used to construct a dual-target(dual-task) predictive surrogate model,and then a PSO-based multiobjective evolutionary optimization algorithm is applied to train the ANN and stochastically search the trained ANN for obtaining the Pareto front of the I-D surrogate prediction model,which is intended to overcome the limitations of the existing linear regression-based threshold methods.Finally,a double early warning curve model that can effectively control the false alarm rate and negative alarm rate of hazard warnings are proposed based on the decision space and target space maps.This study provides theoretical guidance for the early warning and forecasting of debris flows and has strong applicability. |
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