『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