Red tide grade prediction method

Abstract

The invention discloses a red tide grade prediction method. The method comprises: prediction algorithm optimization, prediction model construction and prediction result analysis. According to optimization of a prediction algorithm, a C4.5 decision tree classification contribution optimal feature selection method is adopted, the problem that input parameters of the BP neural network are difficult to select is solved, and the problem that the number of nodes of a hidden layer of the BP neural network is difficult to determine is solved by adopting a binary segmentation algorithm. A prediction model construction comprises: constructing a red tide level prediction model by adopting the optimized BP neural network, training the model by using historical case data, and ending the training when the prediction error is within an allowable range or the network training reaches the maximum iteration times. And the prediction result is analyzed, the trained model is used to predict the red tide level, the root-mean-square error of the prediction result is smaller than the prediction result of the traditional BP neural network before optimization, and the prediction precision is higher. The method can provide a new solution for red tide grade prediction.

Description

[0001] The invention relates to a new method for predicting red tide grades. It learns and trains meteorological data in red tide-prone sea areas based on the good self-learning mode, generalization and fault tolerance capabilities of the neural network, and finally obtains the grade of red tide occurrence, which belongs to red tide Grade prediction technology field. Background technique [0002] In recent years, frequent outbreaks of red tides have disrupted the balance of the normal marine ecosystem, seriously endangered marine fishery resources, marine fishing, mariculture and human health, and caused huge economic losses. Therefore, it is of great significance and practical application value to carry out the research on the prediction of red tide level. At present, the scientific prediction methods for red tide include multiple regression, correlation analysis, time series, SVM, gray model, etc., including: using quantitative red tide ecological dynamics model, red tide ...

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to provide a method for predicting red tide grades, which has the characteristics of accurate prediction results and solves the problems of low precision and large errors in traditional red tide grade prediction

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