Multi-target optimal management method for remediation of underground water with uncertainty

Abstract

The invention discloses a multi-target optimal management method for remediation of underground water with uncertainty. According to the method, target function random evaluation, a random Pareto domination concept and a random ecological niche fitness sharing method are imported into a population evolution operation of an EMOTS on the basis of a multi-target random tabu search algorithm PEMOTS of an elitism selection strategy. The PEMOTS inherits the global search advantage of the EMOTS, Latin Hypercube Sampling LHS is imported into an elitism selection strategy adopted by the EMOTS so as to generate neighborhood solutions, so that non-interior solutions obtained through the algorithm can be convergent to true solutions and can be uniformly distributed along a trade-off curve. The core difference between the PEMOTS and similar methods is adopting sequential Gaussian simulation SGSIM to reduce the uncertainty of water-bearing system parameters; and meanwhile, the random multi-target evolution operation is imported, the variability of the search of Pareto optimal solutions is reduced. The method disclosed in the invention is coupled with an underground water flow program MODFLOW and a solute transport program MT3DMS, so that relatively strong reliability and robustness are provided in the process of solving a multi-target management model for the pollution abatement of the underground water with uncertainty.

Description

technical field [0001] The invention relates to a multi-objective optimal management method for uncertain groundwater restoration, belonging to the fields of hydrology and water resources. Background technique [0002] In the past 20 years, multi-objective evolutionary algorithm (Multi-objective evolutionary algorithm, MOEA) has become a research hotspot in the field of intelligent computing, and has been widely used in solving groundwater management models. With the in-depth application of MOEA in the field of water resources, the model needs to consider factors such as multi-variables, complex multi-objectives, and uncertainties in water-bearing systems. In the groundwater optimal management model, uncertain system parameters will lead to random changes in the output flow and solute transport state of the model, which in turn will cause uncertain changes in the value of the management objective function. Deterministic multi-objective evolutionary algorithms, such as multi...

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Problems solved by technology

[0003] In the past, the uncertain water resources optimization problem only considered the constraint conditions or the uncertainty of a single target, and the average method was used to evaluate individuals with the realization of a group of random variables, calculate the average value of individual objective functions, and perform population evolution operations. The method takes into account the uncertainty of the parameters, but it is relatively simple to deal with the uncertainty of the objective function, and the reliability of the optimal solution is not strong

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