A method for simulating the evolution law of river flood flow based on wolf pack algorithm

Abstract

The invention discloses a river way flood flow evolution rule simulating method based on a wolves algorithm. The river way flood flow evolution rule simulating method based on the wolves algorithm comprises the following steps: step 1, establishing a Muskingum model by taking minimum river way flood actual flow process error; step 2, calibrating parameters of the Muskingum model in the step 1 by using the wolves algorithm to obtain a flow routing coefficient when errors of a river way flood flow simulating process and an actual flow process are minimum; and step 3, substituting the flow routing coefficient obtained in the step 2 into the Muskingum model in the step 1 to obtain a calibrated Muskingum model objective function. According to the river way flood flow evolution rule simulating method based on the wolves algorithm, the wolves algorithm needs to be introduced to calibrate the parameters of the Muskingum model, effectiveness, feasibility and superiority of the wolves algorithmin calibration on parameters of a flood evolution model, and the river way flood flow evolution rule simulating method based on the wolves algorithm has important practical significance and application value on improvement of forecast accuracy of three elements and arriving time of flood and prevention of flood damage.

Description

technical field [0001] The invention belongs to the technical field of river flow evolution simulation methods, and in particular relates to a method for simulating the evolution law of river flood flow based on a wolf pack algorithm. Background technique [0002] Muskingum is a classic model of river flow evolution. The core problem in practical application is model parameter calibration, which is actually a nonlinear optimization problem. Traditional parameter determination methods mainly include trial-and-error method, least square method, nonlinear programming method, etc., but these methods are limited by the optimal estimation of the storage curve, resulting in a large difference between the calculation results and the actual ones. In recent years, swarm intelligence algorithms have been widely used for solving nonlinear problems accurately and efficiently. Many foreign scholars have carried out in-depth research on the parameter calibration of the Muskingum model by ...

AI Technical Summary

Problems solved by technology

[0003] Since most intelligent algorithms do not need to consider the gradient of the objective function when solving problems, they are especially suitable for large-scale and complex optimization problems that cannot be solved by traditional methods, but there are also problems such as slow convergence speed, local optimum, unstable results, and low accuracy of results. and other ills

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