This paper presents 15 SVM models optimized by machine learning methods in water quality assessment. Through comparative analysis, we found that all the models reached reasonable accuracy during the training and testing process. In particular, SAA-SVM-PI, GWO-SVM-PI, GWO-SVM-TN, PSO-SVM-TP models have the highest accuracy, the highest squared correlation and the lowest mean squared error when testing the three indexes of PI, TN and TP. In order to study the spatial distribution characteristics of water quality in Dongting Lake, GWO-SVM-PI, GWO-SVM-TN and PSO-SVM-TP models were respectively use...
