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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 accurate estimation of reference crop evapotranspiration (ETo) is essential in irrigation water management to assess the time-dependent status ofcrop water use and irrigation scheduling. The importance of ETo has resulted in many direct and indirect methods to approximate its value and includepan evaporation, meteorological-based estimations, lysimetry, soil moisture depletion, and soil water balance equations. Artificial neural networks (ANNs)have been intensively implemented for process-based hydrologic modeling due to their superior performance using nonlinear modeling, patternrecognition, and classification. This study adapted two well-known ANN algorithms, Backpropagation neural network (BPNN) and Generalizedregression neural network (GRNN), to evaluate their capability to accurately predict ETo using daily meteorological data. All data were obtained fromtwo automated weather stations (Chupungryeong and Jangsu) located in the Yeongdong-gun (2002-2017) and Jangsu-gun (1988-2017), respectively. Daily ETo was calculated using the Penman-Monteith equation as the benchmark method. These calculated values of ETo and correspondingmeteorological data were separated into training, validation and test datasets. The performance of each ANN algorithm was evaluated against ETocalculated from the benchmark method and multiple linear regression (MLR) model. The overall results showed that the BPNN algorithm performedbest followed by the MLR and GRNN in a statistical sense and this could contribute to provide valuable information to farmers, water managers andpolicy makers for effective agricultural water governance.
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참고문헌 (24)
참고문헌 신청
Allen, R. G. / 1994 / An update for the definition of reference evapotranspiration / ICID Bull 43(2):1~92
ALI AYTEK / 2009 / REPLY to Discussion of “An explicit neural network formulation for evapotranspiration” / Hydrological Sciences Journal 54(2):389~393
Ali Azadeh / 2014 / An Integrated Artificial Neural Network and System Dynamics Approach in Support of the Viable System Model to Enhance Industrial Intelligence: The Case of a Large Broiler Industry / Systems Research and Behavioral Science 31(2):236~257
Basheer I. A. / 2000 / Artificial neural networks: fundamentals, computing, design, and application / J. Microbiol. Methods 43(1):3~31
Basu, J. K. / 2010 / Use of artificial neural network in pattern recognition / International Journal of Software Engineering and its Applications 4(2):23~43
ALI AYTEK / 2009 / REPLY to Discussion of “An explicit neural network formulation for evapotranspiration” / Hydrological Sciences Journal 54(2):389~393
Ali Azadeh / 2014 / An Integrated Artificial Neural Network and System Dynamics Approach in Support of the Viable System Model to Enhance Industrial Intelligence: The Case of a Large Broiler Industry / Systems Research and Behavioral Science 31(2):236~257
Basheer I. A. / 2000 / Artificial neural networks: fundamentals, computing, design, and application / J. Microbiol. Methods 43(1):3~31
Basu, J. K. / 2010 / Use of artificial neural network in pattern recognition / International Journal of Software Engineering and its Applications 4(2):23~43
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