Prediction method of cyanobacteria blooms in lakes and reservoirs based on expert system and time series model of cyanobacteria growth mechanism

Abstract

The invention discloses a method for predicting cyanobacteria blooms in lakes and reservoirs based on an expert system and a time series model of cyanobacteria growth mechanism, belonging to the technical field of water environment prediction. The prediction method includes determination of key influencing factors, determination of threshold value, expert system modeling prediction based on key environmental factors, mechanism time series modeling prediction and comprehensive prediction based on key water quality factors. The present invention proposes that based on the environmental factors that affect the formation of cyanobacteria blooms in lakes and reservoirs, the expert system method is used to model and predict the formation process of cyanobacteria blooms in lakes and reservoirs, and by constructing an adaptive neuro-fuzzy reasoning expert system model, the prediction of environmental factors according to future moments can be realized. Changes predict changes in the formation process of cyanobacterial blooms, making the results of bloom modeling predictions more accurate. The description of the water bloom formation process in the invention is more realistic, makes the results of water bloom modeling and prediction more accurate, and improves the adaptability of the cyanobacteria water bloom modeling and prediction method.

Description

technical field [0001] The invention relates to a method for predicting cyanobacteria blooms in lakes and reservoirs, which belongs to the technical field of water environment prediction. Method modeling, and through the absolute threshold and relative threshold switching methods, two modeling prediction methods are used for comprehensive prediction, which improves the prediction accuracy of the lake cyanobacteria bloom prediction method. Background technique [0002] Water bloom refers to a typical manifestation of algae explosive growth and accumulation and reaching a certain concentration in the eutrophication of water body. Since the critical factors and mechanism of cyanobacteria blooms are still unclear, generally speaking, there is still a lack of technologies and means that can effectively prevent and control cyanobacteria blooms in a short period of time. Before the cyanobacteria bloom is effectively controlled, accurate prediction of the occurrence of cyanobacteri...

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Problems solved by technology

[0006] The present invention studies the prediction method of cyanobacteria blooms in lakes and reservoirs, and aims to solve the problems that the existing prediction accuracy of cyanobacteria blooms in lakes and reservoirs is not high, and the influence of environmental factors on the formation of cyanobacteria blooms cannot be considered in real time. The measured data of the formation process of cyanobacterial blooms under the influence of various factors of water quality factors were modeled based on the expert system method based on environmental factors, and the mechanism and time series method was used to model based on water quality factors, and the cyanobacterial blooms at different times in the actual lake reservoir were considered. When the dominant influencing factors formed are different, the absolute threshold and relative threshold methods are used to make threshold switching comprehensive predictions for the two modeling prediction methods, thereby improving the accuracy of algal bloom prediction, providing an effective reference for the environmental protection department, and improving the water quality of lakes and reservoirs. The protection and improvement of the environment play an important role in the prevention and control of

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