Exploring the application of artificial intelligence technology for identification of water pollution characteristics and tracing the source of water quality pollutants Puze Wang a , Jiping Yao a , Guoqiang Wang a, ⁎, Fanghua Hao a , Sangam Shrestha b , Baolin Xue a , Gang Xie c , Yanbo Peng c a Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Beijing 100875, China b School of Engineering and Technology, Asian Institute of Technology, Thailand c Shandong Academy of Environmental Planning, Shandong 250101, China HIGHLIGHTS • Long short-term memory network was introduced into the field of water envi- ronment creatively. • An AI system for identifying and tracing point sources in water pollution indus- try is proposed. • Explore how the big data and AI tech- nology apply to the water environment • A multi-dimensional and multi-spatial water quality map is proposed. GRAPHICAL ABSTRACT abstract article info Article history: Received 6 June 2019 Received in revised form 16 July 2019 Accepted 16 July 2019 Available online 17 July 2019 Point sources are important routes through which pollutants enter rivers. It is important to identify the charac- teristics of and trace the origins of water pollutants. In this study, an artificial intelligence system called the inte- grated 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 in- telligence 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 in- dustries and different distributions and production cycles of the pollutant point sources; (2) water quality corre- lation 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 conclusion, the artificial intelligence scheme described here can be applied to aquatic systems. © 2019 Elsevier B.V. All rights reserved. Keywords: Aquatic environment Point source pollution Artificial intelligence Long short-term memory Cross-correlation Apriori Science of the Total Environment 693 (2019) 133440 ⁎ Corresponding author. E-mail address: wanggq@bnu.edu.cn (G. Wang). https://doi.org/10.1016/j.scitotenv.2019.07.246 0048-9697/© 2019 Elsevier B.V. All rights reserved. Contents lists available at ScienceDirect Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv