Predicting the impacts of climate change on nonpoint source pollutant loadsfrom agricultural small watershed using artificial neural network
Eunjeong Lee , Chounghyun Seong , Hakkwan Kim , Seungwoo Park , Moonseong Kang
DOI:
Received October 02, 2009,Revised January 06, 2010, Accepted , Available online
Volume 22,2010,Pages 840-845
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This study described the development and validation of an artificial neural network (ANN) for the purpose of analyzing the e ects of
climate change on nonpoint source (NPS) pollutant loads from agricultural small watershed. The runo discharge was estimated using
ANN algorithm. The performance of ANN model was examined using observed data from study watershed. The simulation results
agreed well with observed values during calibration and validation periods. NPS pollutant loads were calculated from load-discharge
relationship driven by long-term monitoring data. LARS-WG (Long Ashton Research Station-Weather Generator) model was used
to generate rainfall data. The calibrated ANN model and load-discharge relationship with the generated data from LARS-WG were
applied to analyze the e ects of climate change on NPS pollutant loads from the agricultural small watershed. The results showed that
the ANN model provided valuable approach in estimating future runo discharge, and the NPS pollutant loads.
