Wang,M.X., Liu,G.D., Wu,W.L., Bau,Y.H. and Liu,W.N.(2006): Prediction of agriculture derived groundwater nitrate distribution in North China Plain with GIS-based BPNN. Environ. Geol., 50, 637-644.

『GISに基づいたBPNNによる中国華北平原における農業由来の地下水硝酸塩の予測』


Abstract
 In recent years, nitrate contamination of groundwater has become a growing concern for people in rural areas in North China Plain (NCP) where groundwater is used as drinking water. The objective of this study was to simulate agriculture derived groundwater nitrate pollution patterns with artificial neutral network (ANN), which has been proved to be a effective tool for prediction in many branches of hydrology when data are not sufficient to understand the physical process of the systems but relative accurate predictions is needed. In our study, a back propagation neutral network (BONN) was developed to simulate spatial distribution of NO3-N concentrations in groundwater with land use information and site-specific hydrogeological properties in Huantai County, a typical agriculture dominated region of NCP. Geographic information system (GIS) tools were used in preparing and processing input-output vectors data for the BPNN. The circular buffer zones centered on the sampling wells were designated so as to consider the nitrate contamination of groundwater due to neighboring field. The result showed that the GIS-based BPNN simulated groundwater NO3-N concentration efficiently and captured the general trend of groundwater nitrate pollution patterns. The optimal result was obtained with a learning rate of 0.02, a 4-7-1 architecture and a buffer zone radius of 400 m. Nitrogen budget combined with GIS-based BPNN can serve as a cost-effective tool for prediction and management of groundwater nitrate pollution in an agriculture dominated regions in North China Plain.

Keywords: Nitrate; Groundwater; Artificial neutral network; Nitrogen budget; North China Plain』

Introduction
Methods and materials
 Study area and data source
 Groundwater sampling
 Data collection
 Back propagation neutral network development
 Conceptualization of groundwater nitrate pollution
 Preparation of training and validation data set
 Network architectures and efficiency evaluation
Results and discussion
 Land use and groundwater nitrate pollution
 Model training and verification
 Simulation of groundwater NO3-N concentration distribution
 Application in groundwater quality management
Conclusion
Acknowledgements
References


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