Reliable Model of Reservoir Water Quality Prediction Based on Improved ARIMA Method

Abstract

Abstract

With the frequent occurrence of water pollution, the safety of the surface water environment has become increasingly severe. Studying the changing trend of reservoir water quality and establishing a prediction and early warning system for water eutrophication is of great significance to the management and maintenance of water resources. Based on the time series ARIMA model, the Holt-Winters seasonal model was introduced for optimization, and a universal water quality prediction model with eutrophication indicator Total Phosphorus and Total Nitrogen as parameters was established. And through self-correction, the water quality prediction accuracy rate has been improved to 97.5%. Experiments showed that compared with the traditional water quality prediction model, this model is simpler and more convenient, and it has the advantages of high learning speed, high prediction accuracy, easy multi-dimensional analysis of data, and close connection with the development laws of things. Therefore, the model can be applied to the short-term prediction of different reservoirs, can significantly reduce the predicted cost of reservoir water quality, and provide methods for the study of dynamic changes of reservoir water quality parameters; thus, it will be a scientific basis and decision support for water quality improvement.