Water quality prediction method based on Gaussian cloud transformation and fuzzy time sequence

Abstract

The invention relates to a water quality prediction method based on Gaussian cloud transformation and a fuzzy time sequence and belongs to the technical field of artificial intelligence and water environment monitoring. The method mainly comprises the following steps: 1) discretizing historical observation values of water quality parameters into different granular layers through Gaussian cloud transformation; 2) calculating an approximate period length of water quality parameter historical data; 3) constructing a training set according to the approximate period length; and 4) calculating a prediction value by applying a fuzzy time sequence model. The water quality prediction method based on Gaussian cloud transformation and the fuzzy time sequence provided by the invention can be used for effectively processing a water quality data set with an uncertainty characteristic through Gaussian cloud transformation and prediction by the fuzzy time sequence. A prediction result is better in robustness; By blending the water quality data approximate period, noise data is removed by virtue of inherent characteristics of the water quality data, so that the prediction precision is prevented from being affected by the noise data, and the model is relatively high in self-adaptability to the data.

Description

technical field [0001] The invention belongs to the technical field of artificial intelligence and water environment monitoring, and relates to a water quality prediction method based on Gaussian cloud transformation and fuzzy time series. Background technique [0002] Water is not only an indispensable natural resource for the survival and development of human society, but also an important part of the ecological environment. In recent years, with the frequent occurrence of water pollution incidents, water resources management and water pollution control have gradually become the focus of environmental management departments around the world. As an important method and means of water resources management, water quality parameter prediction can provide scientific basis and decision-making support for relevant departments to grasp the development trend of water quality changes in a timely manner. At present, many statistical analysis models and artificial intelligence method...

AI Technical Summary

Problems solved by technology

The artificial neural network has good predictive ability, but it has problems such as slow network convergence and easy to fall into local minima, and the generalization ability of the model is not strong

The prediction accuracy of the support vector machine is highly dependent on the selection of the parameters of the model itself, and the existing parameter optimization methods (genetic algorithm and particle swarm algorithm, etc.) have high time complexity

[0004] The high-precision predictions of the above methods are all based on deterministic water quality time series data sets. When the collected water quality data has uncertain characteristics such as inaccuracy or missing due to inaccurate instruments or sensor problems, the current existing models cannot show relatively high accuracy. good predictive performance

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