한국농공학회논문집 (Journal of The Korean Society of Agricultural Engineers)

  • 제54권4호
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  • Pages.65-71
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  • 2012
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  • 1738-3692(pISSN)
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  • 2093-7709(eISSN)
한국농공학회 (The Korean Society of Agricultural Engineers)
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인공신경망 기법을 이용한 논에서의 지표 유출량 산정

Estimation of Surface Runoff from Paddy Plots using an Artificial Neural Network

  • 안지현 (서울대학교 농업생명과학대학 생태조경.지역시스템공학부) ;
  • 강문성 (서울대학교 농업생명과학대학 조경.지역시스템공학부, 농업생명과학연구원) ;
  • 송인홍 (서울대학교 농업생명과학연구원) ;
  • 이경도 (농촌진흥청 국립식량과학원) ;
  • 송정헌 (서울대학교 농업생명과학대학 생태조경.지역시스템공학부) ;
  • 장정렬 (한국농어촌공사 농어촌연구원 새만금연구부)
  • 투고 : 2011.12.14
  • 심사 : 2012.06.13
  • 발행 : 2012.07.31
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초록

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 1 output 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

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