# Water Quality Evaluation and Prediction Based on a Combined Model
###### Abstract  
Along with increasingly serious water pollution, water environmental problems have become major factors that hinder the sustainable development of our economy and society. Reliable evaluation of water quality and accurate prediction of water pollution indicators are the key links in water resource management and water pollution control. In this paper, the water quality data of Lanzhou Xincheng Bridge section in the Yellow River Basin and Sichuan Panzhihua Longdong section in the Yangtze River Basin were used to establish a water quality evaluation model and a prediction model. For the water quality evaluation model, we constructed the research samples by means of equal intervals and uniform distribution of interpolated water quality index data according to _Environmental Quality Standards for Surface Water_. The training samples were determined by a stratified sampling method, and the water quality evaluation model was established using a T-S fuzzy neural network. The experimental results show that the highest accuracy achieved by the evaluation model in water quality classification was 94.12%. With respect to the water quality prediction model, we propose ARIMA-WNN, which combines the autoregressive integrated moving average model (ARIMA) and a wavelet neural network (WNN) with the bat algorithm (BA) to determine the optimal weight of each individual model. The experimental results show that the highest prediction accuracy of ARIMA-WNN is 68.06% higher than that of the original model. water quality evaluation; water quality forecasting; T-S fuzzy neural network; combined model 202313128610 December 202230 December 202230 December 202230 December 202  
management have further worsened the water quality of small tributaries flowing into the major rivers of our country. Lakes are also heavily polluted. The water quality of nearly half of the 62 key lakes in the country is of grades IV and V or inferior. The three major lakes in China, Taihu Lake, Chaohu Lake and Dianchi Lake, are polluted to varying degrees, in states of mild, moderate and severe pollution, respectively, with total phosphorus and chemical oxygen demand representing the main pollutants. The groundwater quality situation is also worrying. In China's major cities, 27 percent of centralized drinking water sources do not meet official standards. Among the 5118 groundwater monitoring points in various provinces and cities across the country, the proportion of poor and extremely poor water quality is more than half, threatening people's daily water use [3; 4]. Water environmental problems have become a major factor hindering the sustainable development of China's economy and society, and the effective treatment of water pollution and the rational management of water resources are urgent problems to be solved. The accurate prediction of water quality indicators and the reliable evaluation of water quality grades are the basis for understanding the current water quality status and taking corresponding protection measures, so water quality prediction and evaluation have great practical significance. In this paper, we take the water quality of Lanzhou Xincheng Bridge section in Yellow River Basin and Longdong section in Yangtze River Basin as the research object, establish a water quality evaluation model and propose a new water quality prediction model. A T-S fuzzy neural network was used to establish the evaluation model combined with the relevant water quality information of the two basins. In the process of model training, an innovative method of interpolating water quality index data with equal intervals and uniform distribution was adopted to construct research samples, and the method of stratified sampling was used to construct training samples. The trained model was applied to water quality evaluation of Lanzhou Xincheng Bridge section in the Yellow River Basin and Longdong section in the Yangtze River Basin.