Predicting the impacts of climate change on nonpoint source pollutant loads from agricultural small watershed using artificial neural network  

作　　者：Eunjeong Lee Chounghyun Seong Hakkwan Kim Seungwoo Park Moonseong Kang 

机构地区：[1]Department of Landscape Architecture and Rural Systems Engineering [2]Research Institute for Agricultural & Life Sciences,Seoul National University

出　　处：《Journal of Environmental Sciences》2010年第6期840-845,共6页环境科学学报（英文版）

基　　金：supported by a grant (code number 4-5-3) from Sustainable Water Resources Research Center of 21st Century Frontier Research Program (50%)and Han River Basin Environmental Office and Han River Environment Research Center, Ministry of Environment(50%)

摘　　要：This study described the development and validation of an artificial neural network （ANN） for the purpose of analyzing the effects of climate change on nonpoint source （NPS） pollutant loads from agricultural small watershed. The runoff 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 effects 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 runoff discharge, and the NPS pollutant loads.This study described the development and validation of an artificial neural network （ANN） for the purpose of analyzing the effects of climate change on nonpoint source （NPS） pollutant loads from agricultural small watershed. The runoff 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 effects 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 runoff discharge, and the NPS pollutant loads.

关 键 词：artificial neural network climate change LARS-WG nonpoint source pollution RUNOFF 

分 类 号：X52[环境科学与工程—环境工程]