Organic pollutant migration numerical model substitution method based on multi-core extreme learning machine

Abstract

The invention relates to an organic pollutant migration numerical model substitution method based on a multi-core extreme learning machine, and the method comprises the following steps: building an underground water organic pollution multiphase flow migration numerical model according to observation data, determining pollution source characteristics, aquifer parameters and the value range of each variable, which have relatively high contribution degree to the spatial and temporal distribution of pollutants, in the model; preparing a training sample set; training a single-kernel extreme learning machine substitution model of the multiphase flow numerical model; establishing a nonlinear programming optimization model of kernel function key parameters and kernel function combination weights; and solving the optimization model by using a genetic algorithm, identifying an optimal kernel function parameter and a combination weight, and constructing a genetic evolution multi-kernel extreme learning machine intelligent substitution model of the numerical model. The problem of substitution of the underground water organic pollution multiphase flow numerical model is solved, the calculation efficiency of pollutant transport simulation prediction is improved, and an efficient solution is provided for underground water pollution source characteristic and pollutant transport parameter inversion identification.

Description

technical field [0001] The invention relates to the combined application field of artificial intelligence and groundwater numerical simulation, in particular to a method for replacing numerical models of organic pollutant migration in multi-core extreme learning machines. Background technique [0002] Organic pollutants have the characteristics of low water solubility, high toxicity, and high interfacial tension. After entering the groundwater system, they will accumulate and stay at the top or bottom of the aquifer (the location of the accumulation depends on the density of the pollutants being less than or greater than water). During the process, it is continuously dissolved and released into the water, causing serious and long-lasting pollution. Therefore, effective identification of pollution source characteristics and aquifer parameters, and accurate simulation and prediction of pollutant spatio-temporal distribution are crucial to achieving reliable pollution risk asse...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide an alternative method for the numerical model of organic pollutant migration based on multi-core extreme learning machine, which can be used to identify and approximate the characteristics of pollution sources, aquifer parameters and pollutants in the multiphase flow numerical model of organic pollution in groundwater The complex nonlinear relationship between concentration distributions can establish a substitute model for numerical models, greatly improve the calculation efficiency of pollutant migration simulation and prediction, and provide efficient solutions to computationally cumbersome problems such as the characteristics of groundwater pollution sources and the inversion and identification of pollutant migration parameters. way

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