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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
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The objective of this study was to estimate surface runoff from rice paddy plots using an artificial neural network (ANN). A field experiment with three treatment levels was conducted in the NICS saemangum experimental field located in Iksan, Korea. The ANN model with the optimal network architectures, named Paddy1901 with 19 input nodes, 1 hidden layer with 16 neurons nodes, and 1output node, was adopted to predict surface runoff from the plots. The model consisted of 7 parameters of precipitation, irrigation rate, ponding depth, average temperature, relative humidity, wind speed, and solar radiation on the daily basis. Daily runoff, as the target simulation value, was computed using a water balance equation. The field data collected in 2011 were used for training and validation of the model. The model was trained based on the error back propagation algorithm with sigmoid activation function.
Simulation results for the independent training and testing data series showed that the model can perform well in simulating surface runoff from the study plots. The developed model has a main advantage that there is no requirement for any prior assumptions regarding the processes involved. ANN model thus can be a good tool to predict surface runoff from rice paddy fields.
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참고문헌 (17)
참고문헌 신청
Choi, J. K. / 2000 / (Theory of) Neural network and application to water resources / Jounal of Korean National Committee on Irrigation and Drainage 7 (2) : 248 ~ 258
Huynh, N. P. / 2000 / Neural networks for filtering and forecasting of daily and monthly streamflows / Water Resources Publications : 203 ~ 218
Kang, M. S. / 2001 / Forecasting long-term streamflow from a small watershed using artificial neural network / Journal of the Korean Society of Agricultural Engineers 43 (2)
Kang, M. S. / 2002 / Development of total maximum daily loads simulation system using artificial neural networks for satellite data analysis and nonpoint source pollution models / 박사
Kang, M. S. / 2006 / Application of grey model and artificial neural networks to flood forecasting / Journal of American Water Resources Association (JAWRA) 42 (2) : 473 ~ 48
Huynh, N. P. / 2000 / Neural networks for filtering and forecasting of daily and monthly streamflows / Water Resources Publications : 203 ~ 218
Kang, M. S. / 2001 / Forecasting long-term streamflow from a small watershed using artificial neural network / Journal of the Korean Society of Agricultural Engineers 43 (2)
Kang, M. S. / 2002 / Development of total maximum daily loads simulation system using artificial neural networks for satellite data analysis and nonpoint source pollution models / 박사
Kang, M. S. / 2006 / Application of grey model and artificial neural networks to flood forecasting / Journal of American Water Resources Association (JAWRA) 42 (2) : 473 ~ 48
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