Hydrological model parameter calibration method based on PSO (particle swarm optimization)-GA (genetic algorithm) mixed algorithm

Abstract

The invention provides a hydrological model parameter calibration method based on a PSO (particle swarm optimization)-GA (genetic algorithm) mixed algorithm, and belongs to the field of hydrological model parameter calibration. The method includes inputting rainfall, all parameters of a hydrological model, and maximum and minimum values of each parameter in an initialization phase of the hydrological model,; executing a hydrological model algorithm program to acquire an output forecasting runoff value; comparing the calculated forecasting runoff value with an actual value to acquire a certainty coefficient, and performing judgment; when the certainty coefficient is larger than or equal to 0.2, indicating that an error between the forecasting runoff value and the actual value is larger than or equal to 20%, and performing parameter calibration according to the PSO-GA mixed algorithm. The method has the advantages that optimizing characteristics of GA and PSO algorithms are combined, premature convergence is avoided, and searching accuracy is improved.

Description

technical field [0001] The invention belongs to the field of parameter calibration in hydrological models, and specifically relates to a hydrological model parameter calibration method combined with PSO particle swarm algorithm and GA genetic algorithm. Background technique [0002] The hydrological phenomenon in nature is a very complex phenomenon, which is affected by many factors such as rainfall characteristics, the underlying surface of the watershed, and human activities. Before it is difficult to understand the law of hydrological phenomena, it is an effective way to simulate (test) the hydrological process by establishing a model, which is a hydrological model. Hydrological model parameters can be divided into two categories: one category of parameters has clear physical meanings, which can be determined according to actual conditions, such as the proportion of impervious areas; , Soil Midflow Sunrise Flow Coefficient, these parameters need to be calibrated accordin...

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Problems solved by technology

In the traditional automatic parameter calibration methods, there are steepest descent method, step size acceleration method, mode method, etc., but these algorithms may only work for a certain type of method, and the requirements for the model are relatively strict, such as requiring the model to be continuous, derivable , unimodal and other characteristics; and some emerging optimization algorithms show their advantages here. Among them, the GA genetic algorithm is a computational model that simulates biological evolution stimulated by genetics, and searches for the optimal solution by simulating the natural evolution process. The method has strong global search ability, but weak local search ability, and the mutation is random, and some particles with low fitness will be lost during the selection operation, and these particles may also have good experience to learn. Due to the lack of an effective local area search mechanism, the GA algorithm converges slowly or even stops when it is close to the optimal solution. In the early stage of the algorithm, the model also has the disadvantages of low precision and easy divergence. In the case of convergence, because all particles are flying towards the optimal direction, the particles tend to be identical, which makes the later convergence speed significantly slower. , resulting in the defect that it is easy to fall into local optimum

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