Spatio-Temporal Analysis of Water Quality Based on Gray Wolf Optimization and Support Vector Machines (GWO-SVM): A Case Study from Dongting Lake, China

Water quality assessment is an essential part of water resource management. This paper presents a new GWO-SVM method for water quality assessment. Through comparison with GS-SVM and GA-SVM, we found that GWO-SVM model has the highest accuracy, the highest squared correlation and the lowest mean squared error when testing the three indexes of PI, TN and TP.The GWO-SVM model proposed in this paper was used to analyze the water quality data of Dongting Lake from 1993 to 2012. The results showed that: As far As the PI is concerned, the water quality is mainly in grade I and II, with the best water quality in autumn and 2008-2012.As far As TN is concerned, the water quality is mainly of grade IV and V,With the best water quality in autumn and the worst in spring, water quality was the best from 2003 to 2007 and the worst from 2008 to 2012.In terms of TP, the water quality is mainly of grade III,IV and V, with the best inlet water quality, the worst water quality In the south dongting lake, the best water quality In summer,The best water quality between 1993 and 1997, and the worst water quality between 2003 and 2007.