Application of artificial neural network to karst groundwater pollution assessment in Southwest China
Karst groundwater is an important source of water for human life.Predicting the pollution level of karst groundwater is of great significance for the sustainable development and utilization of groundwater resources and the protection of the ecological environment in karst areas.In order to obtain more representative evaluation results of karst groundwater pollution,a total of 105 groundwater samples were collected from typical karst areas,southwestern China.On the basis of the quantitative results of Nemero Comprehensive Pollution Index,the BP(Back Propagation)neu-ral network,SVM(Support Vector Machine)and T-S(Takagi-Sugeno)fuzzy neural network were used to predict groundwater pollution level.Further,the reliabilities of three artificial neural net-work conducted in pollution evaluation and prediction were compared.In these processes,90 karst groundwater samples were applied to train artificial neural network,and remaining 15 karst groundwater samples were employed for predicting the pollution level.The results exhibited that the similarity between the prediction results of BP neural network model and the evaluation re-sults of Nemero comprehensive pollution index is 80%,the similarity of SVM is 73.3%,and the similarity of T-S fuzzy neural network is 66.7%.The prediction result of karst groundwater pol-lution level from BP neural network is better than that from the SVM,and also better than that from the T-S.Moreover,the obtained prediction result from BP neural network is similar to the result from the Nemero Comprehensive Pollution Index.Based on this,in the study of karst groundwater pollution evaluation in southwest China,it is suggested that BP artificial neural net-work should be preferred to predict the pollution level of karst groundwater.