Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Beijing 100875, China.
Beijing Key Laboratory of Urban Hydrological Cycle and Sponge City Technology, College of Water Sciences, Beijing Normal University, Beijing 100875, China.
Sci Total Environ. 2019 Nov 25;693:133440. doi: 10.1016/j.scitotenv.2019.07.246. Epub 2019 Jul 17.
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 conclusion, the artificial intelligence scheme described here can be applied to aquatic systems.
点源是污染物进入河流的重要途径。识别水污染物的特征并追溯其工业污染源至关重要。在本研究中，使用人工智能系统（集成长短时记忆网络 LSTM）结合交叉相关和关联规则（Apriori）来识别水污染物的特征并追溯工业点源污染物。使用中国山东省的水质监测数据，通过交叉相关方法开发水质交叉相关图来验证人工智能系统的适用性。该图用于识别对水质有高度影响的相关污染物，然后使用关联规则（Apriori）追踪到研究区域常见的工业。将高度相关的水污染物和相关工业作为 LSTM 的输入，以确定 LSTM 追踪水污染物来源的效果。结果表明：（1）不同行业和污染物点源的不同分布和生产周期对水质变化的影响方式不同；（2）水质相关图可用于识别点源污染物排放的规则和异常波动，通过识别水质特征的变化和水质指标中的频繁项集，可以追踪对水质影响最大的行业；（3）LSTM 可以准确追踪未来水质变化的点源。总之，这里描述的人工智能方案可以应用于水生系统。
