Jiang Wu1, Jianjun Zhang1, Wenwu Tan1, Hao Lan1,*, Sirao Zhang1, Ke Xiao2, Li Wang2, Haijun Lin1, Guang Sun3, Peng Guo4
CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 67-82, 2023, DOI:10.32604/cmc.2023.030703
- 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… More >