Jiang Wu¹, Jianjun Zhang¹, Wenwu Tan¹, Hao Lan^1,*, 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

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… More >

Wenwu Tan¹, Jianjun Zhang^1,*, Jiang Wu¹, Hao Lan¹, Xing Liu¹, Ke Xiao², Li Wang², Haijun Lin¹, Guang Sun³, Peng Guo⁴

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1943-1958, 2022, DOI:10.32604/iasc.2022.029660

Abstract Water resources are an indispensable precious resource for human survival and development. Water quality prediction plays a vital role in protecting and enhancing water resources. Changes in water quality are influenced by many factors, both long-term and short-term. Therefore, according to water quality changes’ periodic and nonlinear characteristics, this paper considered dissolved oxygen as the research object and constructed a neural network model combining convolutional neural network (CNN) and long short-term memory network (LSTM) to predict dissolved oxygen index in water quality. Firstly, we preprocessed the water quality data set obtained from the water quality monitoring platform. Secondly, we used… More >