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A multiobjective evolutionary optimization method based critical rainfall thresholds for debris flows initiation

Journal of Mountain Science 2020年第8期17卷 1860-1873页

作者： YAN Yan ZHANG Yu HU Wang GUO Xiao-jun MA Chao WANG Zi-ang ZHANG Qun Key Laboratory of High-Speed Railway Engineering MOE/School of Civil EngineeringSouthwest Jiao tong UniversityChengdu 610031China institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Key Laboratory of Mountain Surface Process and hazards/institute of Mountain hazards and Environment Chinese Academy of SciencesChengdu 610041China School of Soil and Water Conservation Beijing Forestry UniversityBeijing 100083China sichuan institute of land and space ecological restoration and geological hazard prevention Chengdu 610081China

At present,most researches on the critical rainfall threshold of debris flow initiation use a linear model obtained through *** 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 ***,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 *** 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 *** 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 ***,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 *** study provides theoretical guidance for the early warning and forecasting of debris flows and has strong applicability.

关键词： Debris flow Critical rainfall thresholds Multiobjective evolutionary optimization Artificial neural network Pareto optimality

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