Study On Groundwater Environment Prediction Methods In Plain Regions

Groundwater environment prediction,which is a very important field in groundwater resources planning management, contribute to sustained utilization of groundwater resources extremely. Groundwater environment prediction methods,especially the establishment of prediction model and its utilization in reality,has increasingly been attached importance to and studied in recent years.In this paper,firstly,the problems existing in the present groundwater environment and their contributing factors were simply analyzed, the principle and the general procedure of groundwater environment prediction were then put forward,and the methods most in use in water environment macroscopic prediction were briefly introduced.Grey system theory whose research object is Grey system of which information is imperfect,contrasts with White system theory and Black system theory.Obviously, groundwater resources system is a grey system,so it is feasible for us to utilize Grey system theory to predict groundwater quality. In this paper,on the basis of systems analysis on physicochemical properties of groundwater,the establishment method of groundwater quality grey system prediction model was probed into,and the model was examined. Then, the author used the model to predict the development tendency of CL+, SO42-,HCO3-,,Na+,K*,Mg2+ and hardness of groundwater of some planning area,and analyzed the prediction results.Groundwater level prediction is also a very important field in groundwater environment prediction.Land subsidence,encroachment of seawater and deterioration of water quality and so on by artificial development have relation to sustained drop of groundwater level.After groundwater level change mechanism was analyzed, the establishment method of groundwater level prediction regression analysis model was explored.and the groundwater level development trend of some planning region with the model was predicted according to its change characters of groundwater level,wath's more, the prediction results was analyzed.