Water quality evaluation prediction method based on fuzzy wavelet neural network

Abstract

The invention provides a water quality evaluation prediction method based on a fuzzy wavelet neural network and aims to solve problems of slow convergence speed during water quality prediction, poor approximation effect and inaccurate prediction result existing in a BP neural network in the prior art. According to the method, a fuzzy wavelet neural network prediction model is constructed through utilizing the known water quality analysis index quantity, prediction index quantity and fuzzy rule quantity, and the fuzzy wavelet neural network prediction model comprises an input layer, a subordinate layer, a fuzzy rule layer, a wavelet layer, an output layer and a defuzzication layer; a subordinate function parameter and a wavelet parameter of the wavelet layer are adjusted, a cost function is further defined, a BP algorithm based on a gradient descent method is utilized to carry out parameter adjustment, problems of low convergence speed, easy-to-generate concussion effects and local optimum can be avoided, model stability is improved, an initial parameter is optimized through employing an artificial bee colony algorithm, and the method is mainly applicable to water quality index prediction.

Description

technical field [0001] The invention relates to the field of hydrological evaluation and prediction, in particular to a water quality evaluation and prediction method based on fuzzy wavelet neural network. Background technique [0002] Water quality prediction is a technology to establish water functional areas in water pollution control units, and use the corresponding relationship between water quality indicators and corresponding pollution sources in land areas to obtain target water quality information. In the water environment and water pollution control at home and abroad, the research and application of water quality models have made great progress. Water quality prediction methods mainly include water quality simulation models, mathematical statistics models and artificial neural network models. The traditional BP neural network model method has made great progress in the application research of water quality prediction and evaluation, but there are slow convergence ...

AI Technical Summary

Problems solved by technology

[0003] For above-mentioned situation, in order to overcome the defective of prior art, the present invention provides a kind of water quality evaluation prediction method based on fuzzy wavelet neural network, the purpose is to solve BP neural network when carrying out water quality prediction, convergence speed is relatively slow, approximation effect is poor, and prediction The problem with inaccurate results

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