Exploring the application of artificial intelligence technology for identification of water pollution characteristics and tracing the source of water quality pollutants

Point sources are important routes through which pollutants enter rivers. It is important to identify the characteristics of and trace the origins of water pollutants. In this study, an artificial intelligence system called the integrated long short-term memory network (LSTM), using cross-correlation and association rules (Apriori), was used to identify the characteristics of water pollutants and trace industrial point sources of pollutants. Water quality monitoring data from Shandong Province, China, were used to verify the applicability of the artificial intelligence system using a cross-correlation method to develop a water quality cross-correlation map. The map was used to identify highly correlated pollutants affecting water quality, then the association rules (Apriori) were used to track the pollutants to industries common in the study area. The highly correlated water pollutants and relevant industries were used as inputs for the LSTM to determine how well the LSTM traced sources of water pollutants. The results showed that (1) changes in water quality were affected in different ways by different industries and different distributions and production cycles of the pollutant point sources; (2) water quality correlation maps can be used to identify regular and abnormal fluctuations in point source pollutant emissions by identifying changes in water quality characteristics and frequent itemsets in water quality indices can be used to trace the industries that most strongly affect water quality; and (3) the LSTM accurately traced point sources of future changes in water quality. In condusion, the artificial intelligence scheme described here can be applied to aquatic systems. (C) 2019 Elsevier B.V. All rights reserved.