Identification of groundwater contaminant source information based on ensemble surrogate model and genetic algorithm
Identifying contaminant source information accurately is the premise for efficient remediation of groundwater pollution.In order to solve the problem of computational burden in the process of identifying contaminant source information using traditional simulation-optimization methods,this paper firstly establi-shes back propagation neural network(BPNN)model,support vector regression(SVR)model and kernel ex-treme learning machine(KELM)model to replace the groundwater flow and solute transport model in tradi-tional simulation-optimization methods.Then,the simple average method and genetic algorithm(GA)are used to calculate the weight values,and the ensemble surrogate models are established to further improve the accuracy of the model.Finally,the best-performing ensemble surrogate model based on GA is embedded in the optimization model for identifying contaminant source information.The calculation results show that com-pared with the traditional simulation-optimization model,the calculation time of the optimization model em-bedded with the ensemble surrogate model based on GA is shortened from 11 d to 39 min and the contaminant source information parameters obtained by the solution are close to the true value,which can be used to solve the contaminant source information identification problem.
万方数据
维普
