Urban lake and reservoir cyanobacterial bloom multivariate predication method based on fuzzy support vector machine

Abstract

The invention discloses an urban lake and reservoir cyanobacterial bloom multivariate predication method based on a fuzzy support vector machine. The urban lake and reservoir cyanobacterial bloom multivariate predication method comprises the steps of: step 1, selecting key influencing factors in modeling of urban lake and reservoir cyanobacterial bloom multivariate predication; step 2, reconstructing a phase space of a urban lake and reservoir cyanobacterial bloom multivariate time sequence; step 3, optimizing and determining nearest neighbor points; step 4, and acquiring an urban lake and reservoir cyanobacterial bloom multivariate fuzzy support vector machine prediction model, and predicting urban lake and reservoir cyanobacterial bloom. The urban lake and reservoir cyanobacterial bloom multivariate predication method proposes the definition of similarity coefficient analysis for selecting the key influencing factors of lake and reservoir cyanobacterial bloom generation, and takes the consistency of time sequence variation trend and the similarity of time domain features into account, so as to determine the degree of similarity between the influencing factors and characterization factors, extract complete strong correlation information, reduce redundant information and improve the robustness and generalization capacity of prediction.

Description

technical field [0001] The invention relates to a method for predicting cyanobacteria blooms in urban lakes and belongs to the technical field of environmental engineering. Specifically, on the basis of in-depth research on the multiple influencing factors and chaotic attributes in the process of cyanobacterial blooms, the key influencing factors of cyanobacterial blooms are selected optimally, and then determined based on the time series data of multiple variables measured. The optimal delay time and embedding dimension of multivariate time series are reconstructed in phase space, and then combined with the idea of ​​nearest neighbor point optimization in chaotic time series forecasting and the strong nonlinear simulation of fuzzy support vector machine (Fuzzy Support Vector Machine, FSVM) To build a multi-variable prediction algorithm for cyanobacterial blooms in urban lakes and explore effective methods to improve the prediction accuracy of cyanobacterial blooms. Backgrou...

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Problems solved by technology

[0007] The purpose of the present invention is to solve the problem that most of the existing predictions of cyanobacteria blooms in urban lakes and reservoirs use a single characterization factor to predict the prediction accuracy is not high

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