A risk evaluation model for karst groundwater pollution based on geographic information system and artificial neural network applications

The risk analysis on karst groundwater pollution is a research hotspot in current international hydrogeological field as well as the premise of preventing and controlling groundwater pollution. According to the characteristics of groundwater pollution in the typical study area, the study selected main-control factors of risk evaluation on karst groundwater pollution in mountainous areas at first. Based on this, the research determines the method for quantifying the factors and established a risk evaluation index system for karst groundwater pollution. To overcome drawbacks of the method for determining weights of factors in traditional evaluation method, the study determines the structure of the artificial neural network model by combining the selected evaluation factors. And also, the weight coefficients of evaluation factors on each layer are calculated. On this basis, the model for evaluating the risk of karst groundwater pollution is established. Moreover, the risk zoning evaluation map of groundwater pollution in the typical study area is prepared after conducting the weighted stacking of various sub-layers using the geographic information system. The method applied in the study can comprehensively and objectively reflect that the groundwater pollution is controlled by multiple factors and reveal the nonlinear characteristic of the pollution process. Additionally, the evaluation result is institutive and visible, which can provide a certain basis and reference for relevant researches.