Open Access

ARTICLE

Application of Time Serial Model in Water Quality Predicting

Jiang Wu¹, Jianjun Zhang¹, Wenwu Tan¹, Hao Lan^1,*, Sirao Zhang¹, Ke Xiao², Li Wang², Haijun Lin¹, Guang Sun³, Peng Guo⁴

1 College of Engineering and Design, Hunan Normal University, Changsha, 410081, China
2 Hunan Institute of Metrology and Test, Changsha, 410014, China
3 Big Data Institute, Hunan University of Finance and Economics, Changsha, 410205, China
4 University Malaysia Sabah, Sabah, 88400, Malaysia

* Corresponding Author: Hao Lan. Email:

Computers, Materials & Continua 2023, 74(1), 67-82. https://doi.org/10.32604/cmc.2023.030703

Received 31 March 2022; Accepted 15 June 2022; Issue published 22 September 2022

Abstract

Water resources are an indispensable and valuable resource for human survival and development. Water quality predicting plays an important role in the protection and development of water resources. It is difficult to predict water quality due to its random and trend changes. Therefore, a method of predicting water quality which combines Auto Regressive Integrated Moving Average (ARIMA) and clustering model was proposed in this paper. By taking the water quality monitoring data of a certain river basin as a sample, the water quality Total Phosphorus (TP) index was selected as the prediction object. Firstly, the sample data was cleaned, stationary analyzed, and white noise analyzed. Secondly, the appropriate parameters were selected according to the Bayesian Information Criterion (BIC) principle, and the trend component characteristics were obtained by using ARIMA to conduct water quality predicting. Thirdly, the relationship between the precipitation and the TP index in the monitoring water field was analyzed by the K-means clustering method, and the random incremental characteristics of precipitation on water quality changes were calculated. Finally, by combining with the trend component characteristics and the random incremental characteristics, the water quality prediction results were calculated. Compared with the ARIMA water quality prediction method, experiments showed that the proposed method has higher accuracy, and its Mean Absolute Error (MAE), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE) were respectively reduced by 44.6%, 56.8%, and 45.8%.

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Keywords

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