River channel roughness inversion method based on dynamic programming successive approximation method

Abstract

The invention belongs to the field of channel flow and sediment scour and deposit computation, and particularly relates to a dynamic planning successive approximation method-based channel roughness inversion method. Firstly a one-dimensional hydrodynamic model is adopted for simulating open channel and channel flow changes, and by taking computation of a minimum water level error square sum of channel segments in a computation cycle as a goal, a channel roughness inversion model is built; and secondly a dynamic planning successive approximation method is adopted for performing channel roughness inversion, the roughness under different flow levels is inverted and decomposed into a multi-stage optimization problem, only the roughness of the current stage is optimized during each computation, and roughness values of other stages are kept unchanged. The influence of the aftereffect does not need to be considered and the changes, with the flow, of the roughness are considered to obtain the roughness values under different flow. The method can avoid the problem of relatively great influence of an initial value, easy prematurity or falling into local optimum in other methods, and has the characteristics that the dimensions are reduced, the computation amount is reduced, the computation convergence speed is relatively high and the like.

Description

Technical field [0001] The invention belongs to the calculation field of river water flow and sediment erosion and deposition, and particularly relates to a river roughness inversion method based on a dynamic programming successive approximation method. Background technique [0002] Roughness is a very important and sensitive key parameter in hydraulic calculation, which has a great influence on the calculation results of river flow and its erosion and deposition changes. At the same time, the influencing factors of the roughness are many and complicated, not only related to the roughness of the perimeter wall, but also affected by the shape of the river and the characteristics of the water flow. The more commonly used methods for estimating the roughness are mainly divided into two major methods: traditional methods and intelligent inversion algorithms class. [0003] Traditional methods mainly include empirical method, semi-empirical method and trial and error method. The empir...

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Problems solved by technology

Since the 1960s, scholars at home and abroad have proposed some roughness inversion algorithms. Generally, the roughness of each channel is regarded as a fixed value, and the change of roughness with flow rate is not considered. In fact, the roughness of each channel under different flow rates is different

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