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%.
Among the water quality indicators, permanganate and turbidity are important indicators to reflect the pollution status of water bodies. In order to study the correlation between the two, the water quality monitoring data of relevant water areas were obtained by designing a web crawler, and the water quality monitoring data set was constructed. After the data was cleaned, the correlation analysis was carried out. The experimental results show that there is a big difference in the correlation coefficient between the two indicators at different periods of the same monitoring point. The correlation between the two indicators in the abundant-water season is greater than that in the flat-water season, and the correlation between them in the flat-water season is greater than that in the poor-water season. Among them, there was a high positive correlation between the two indicators during the abundant-water season, and there is little correlation between them during the poor-water season.
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