Fitting method of complex water level process

Abstract

The invention discloses a fitting method of a complex water level process, namely a layered transformation sieving and fitting method which organically integrates polynomial regression, stepwise regression, parameter mountain estimation, and the like and introduces progression transformation to systematically form a new method. Compared with the similar method, the fitting method has the core difference that the strong coupling function among the normal weak- influence factors is considered in the complex water level process; the fitting model error is further reduced by synthetically adoptingvarious methods, and the necessary progression transformation is introduced. The fitting method organically integrates the advantages of various theories and methods, is convenient to use and has thecommon applicability under similar complex fitting problems.

Description

technical field [0001] The invention relates to the fields of hydrology and water resources, in particular to a fitting method for complex water level processes. Background technique [0002] In water regime forecasting, it is necessary to establish an effective relationship model based on historical data, especially for rivers with complex water regimes, it is very difficult to establish a relationship model. [0003] Taking the Yellow River as an example, the internal relationship in the observation data of water and sediment in the flood season in the lower reaches of the Yellow River is very complicated. First, relatively less water and more sand. The Sanmenxia Hydrological Station in the middle reaches of the Yellow River has an annual average sediment concentration of 35kg / m 3 1. The amount of sand transported is about 1.6 billion tons. At the same time, the sediment particles of the Yellow River are very fine, and sometimes the river water is even in a muddy state; ...

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Problems solved by technology

Polynomial regression provides one of the options for the model structure, but the universality of the model in the application is often poor; stepwise regression can eliminate the items that are not significant in the regression to obtain the optimal regression model, but does not consider the strong coupling of the influencing factors; nonlinear regression The processing method of the existing nonlinear relationship items in the model is given, and the form of the nonlinear items cannot be given; when there are multiple correlations among the model components, the ridge estimation can provide more stable model parameters than the least squares estimation The variance of is also a smaller estimate, but also cannot give nonlinear terms of the form etc.

[0008] The inherent laws of many engineering problems are very complicated. When analyzing these laws, it is often difficult to achieve good results by using only one or two theories or methods

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