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 neural network (ANN), which has been proved to be an 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 neural network (BPNN) 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.
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Acknowledgements
This study was part of “Estimating the Environmental Cost of regional agricultural production based on Agroecosystem Carbon/Nitrogen Cycling Model” project (30270220), funded by the National Natural Science Foundation of China (NSFC) and “Technology demonstration and security system of ecological agriculture” funded by national key scientific and technological project. The authors wish to acknowledge endeavors of the government of Huantai County for organizing groundwater sampling. We are grateful to the Huantai Land Resource Bureau, the Huantai Water Conservation Bureau, and the Huantai Agricultural Bureau for providing the land-use information and site-specific hydrogeologic data. We also thank participating colleagues and farmers for their assistance and cooperation during water sampling and data collection.
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Wang, M.X., Liu, G.D., Wu, W.L. et al. Prediction of agriculture derived groundwater nitrate distribution in North China Plain with GIS-based BPNN. Environ Geol 50, 637–644 (2006). https://doi.org/10.1007/s00254-006-0237-x
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DOI: https://doi.org/10.1007/s00254-006-0237-x