Journal of The Korean Society of Agricultural Engineers (한국농공학회논문집)

The Korean Society of Agricultural Engineers (한국농공학회)
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Estimating Reference Crop Evapotranspiration Using Artificial Neural Network and Temperature-based Climatic Data

인공신경망모형을 이용한 기온기반 기준증발산량 산정

  • Lee, Sung-Hack (Department of Rural Systems Engineering, Seoul National University) ;
  • Kim, Maga (Department of Rural Systems Engineering, Seoul National University) ;
  • Choi, Jin-Yong (Department of Rural Systems Engineering, Research Institute of Agriculture and Life Sciences, Seoul National University) ;
  • Bang, Jehong (Department of Rural Systems Engineering, Seoul National University)
  • Received : 2017.04.10
  • Accepted : 2018.11.08
  • Published : 2019.01.31
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Abstract

Evapotranpiration (ET) is one of the important factor in Hydrological cycle and irrigation planning. In this study, temperature-based artificial neural network (ANN) model for daily reference crop ET estimation was developed and compared with reference crop evapotranpiration ($ET_0$) from FAO-56 Penman-Monteith method (FAO-56 PM) and parameter regionalized Hargreaves method. The ANN model was trained and tested for 10 weather stations (5 inland stations and 5 costal stations) and two input climate factors, maximum temperature ($T_{max}$), minimum temperature ($T_{min}$), and extraterrestrial radiation (RA) were used for training and validation of temperature-based ANN model. Monthly reference ET by the ANN model also compared with parameter regionalized Hargreaves method for ANN model applicability evaluation. The ANN model evapotranspiration demonstrated more accordance to FAO-56 PM evapotranspiration than the $ET_0$ from parameter regionalized Hargreaves method(R-Hargreaves). The results of this study proposed that daily reference crop ET estimated by the ANN model could be used in the condition of no sufficient climate data.

Keywords

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Fig. 1 Structure of typical multi-layer neural network

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Fig. 2 Scatter plots comparing calculated daily ET0 by FAO-56 PM method and simulated ET0 by artificial neural network method for training data

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Fig. 3 Scatter plots comparing calculated daily ET0 by FAO-56 PM method and simulated ET0 by artificial neural network method for validation data

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Fig. 4 Scatter plots comparing calculated monthly ET0 by FAO-56 PM method and simulated ET0 by artificial neural network method (2011∼2015)

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Fig. 5 Scatter plots comparing calculated ET0 by FAO-56 PM method and calculated ET0 by R-Hargreaves method (2011-2015)

Table 1 Description of weather stations used in this study

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Table 2 Number of nodes in hidden layer and calibration and validation statics for each stations(observation start time∼2010)

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Table 3 Statics of monthly evapotranspiration estimated by ANN and adjusted Hargreaves method for each stations during test period(2011∼2015)

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