Estimation of Reference Evapotranspiration Using Backpropagation Neural Network Model
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초록
Evapotranspiration (ET) of vegetation is one of the major components of the hydrologic cycle, and its accurate estimation is important for hydrologicwater balance, irrigation management, crop yield simulation, and water resources planning and management. For agricultural crops, ET is oftencalculated in terms of a short or tall crop reference, such as well-watered, clipped grass (reference crop evapotranspiration, ETo). The Penman-Monteithequation recommended by FAO (FAO 56-PM) has been accepted by researchers and practitioners, as the sole ETo method. However, its accuracy iscontingent on high quality measurements of four meteorological variables, and its use has been limited by incomplete and/or inaccurate input data. Therefore, this study evaluated the applicability of Backpropagation Neural Network (BPNN) model for estimating ETo from less meteorological datathan required by the FAO 56-PM. A total of six meteorological inputs, minimum temperature, average temperature, maximum temperature, relativehumidity, wind speed and solar radiation, were divided into a series of input groups (a combination of one, two, three, four, five and six variables)and each combination of different meteorological dataset was evaluated for its level of accuracy in estimating ETo. The overall findings of this studyindicated that ETo could be reasonably estimated using less than all six meteorological data using BPNN. In addition, it was shown that the properchoice of neural network architecture could not only minimize the computational error, but also maximize the relationship between dependent andindependent variables. The findings of this study would be of use in instances where data availability and/or accuracy are limited.
Abstract
Evapotranspiration (ET) of vegetation is one of the major components of the hydrologic cycle, and its accurate estimation is important for hydrologicwater balance, irrigation management, crop yield simulation, and water resources planning and management. For agricultural crops, ET is oftencalculated in terms of a short or tall crop reference, such as well-watered, clipped grass (reference crop evapotranspiration, ETo). The Penman-Monteithequation recommended by FAO (FAO 56-PM) has been accepted by researchers and practitioners, as the sole ETo method. However, its accuracy iscontingent on high quality measurements of four meteorological variables, and its use has been limited by incomplete and/or inaccurate input data. Therefore, this study evaluated the applicability of Backpropagation Neural Network (BPNN) model for estimating ETo from less meteorological datathan required by the FAO 56-PM. A total of six meteorological inputs, minimum temperature, average temperature, maximum temperature, relativehumidity, wind speed and solar radiation, were divided into a series of input groups (a combination of one, two, three, four, five and six variables)and each combination of different meteorological dataset was evaluated for its level of accuracy in estimating ETo. The overall findings of this studyindicated that ETo could be reasonably estimated using less than all six meteorological data using BPNN. In addition, it was shown that the properchoice of neural network architecture could not only minimize the computational error, but also maximize the relationship between dependent andindependent variables. The findings of this study would be of use in instances where data availability and/or accuracy are limited.
참고문헌 (35)
- Abedi-Koupai, J., "Comparison of artificial neural network and physically-based models for estimating of reference evapotranspiration in greenhouse", Australian Journal of Basic and Applied Sciences, 3(3), pp.2528~2535 (2009)
- Crop evapotranspiration-Guidelines for computing crop water requirements, "Allen, R. G.", FAO, ~, pp.FAO Irrigation and Drainage Paper, No 56 (1998)
- Allen, R. G., "A recommendation on standardized surface resistance for hourly calculation of reference ETo by the FAO 56 Penman-Monteith method", Agricultural Water Management, 81(1-2), pp.1~22 (2006)
- Allen, R. G., "Evapotranspiration information reporting : II. Recommended documentation", Agricultural Water Management, 98, pp.921~929 (2011)
- Basheer I. A., "Artificial neural networks: Fundamentals, computing, design, and application", J. Microbiol. Methods, 43(1), pp.3~31 (2000)
- Benzaghta, M. A., "Prediction of evaporation from Algardabiya reservoir", Libyan Agriculture Research Center Journal International, 3, pp.120~128 (2012)[DOI]
- 최용훈, "Comparison of Artificial Neural Network and Empirical Models to Determine Daily Reference Evapotranspiration", 한국농공학회논문집, 60(6), pp.43~54 (2018), 한국농공학회[DOI]
- Paulin Coulibaly, "Artificial neural network modeling of water table depth fluctuations", Water Resources Research, 37(4), pp.885~896 (2001), American Geophysical Union (AGU)[DOI]
- Xiaoqin Dai, "Artificial neural network models for estimating regional reference evapotranspiration based on climate factors", Hydrological Processes, 23(3), pp.442~450 (2009), Wiley[DOI]
- Djaman K, "Variability of the Ratio of Alfalfa to Grass Reference Evapotranspiration under Semiarid Climate", Irrigation & Drainage Systems Engineering, 07(01), pp.1~6 (2018), OMICS Publishing Group[DOI]
- Judy Z. Drexler, "A review of models and micrometeorological methods used to estimate wetland evapotranspiration", Hydrological Processes, 18(11), pp.2071~2101 (2004), Wiley[DOI]
- George, B. A., "Decision support system for estimating reference evapotranspiration", Journal of Irrigation and Drainage Engineering, 128(1), pp.1~10 (2002)[DOI]
- Goel, A., "ANN based modeling for prediction of evaporation in reservoirs (Research Note)", International Journal of Engineering, Transactions A: Basics, 22(4), pp.351~358 (2009)
- Haykin, S., "Neural networks: A comprehensive foundation" (1998), Prentice-Hall
- Itenfisu, D., "Comparison of reference evapotranspiration calculations as part of the ASCE standardization effort", Journal of Irrigation and Drainage Engineering, 129(6), pp.440~448 (2003)[DOI]
- Sanjay K. Jain, "Impact of Declining Trend of Flow on Harike Wetland, India", Water Resources Management, 22(4), pp.409~421 (2008), Springer Science and Business Media LLC[DOI]
- Evaluation of reference evapotranspiration methodologies and AFSIRS crop water use simulation model, "Jennifer, M. J.", Final report, Division of Water Supply Management, St. Johns River Water Manag, ~ (2001)
- Crop and irrigation water requirements, "Jensen, M. E.", ASCE, ~, pp.Manual and Reports on Engineering Practice No. 70 (1990)
- Kecman, V., "Learning and soft computing" (2001), MIT press
- Ali Rahimi Khoob, "Comparative study of Hargreaves’s and artificial neural network’s methodologies in estimating reference evapotranspiration in a semiarid environment", Irrigation Science, 26(3), pp.253~259 (2008), Springer Science and Business Media LLC[DOI]
- Minyoung Kim, "Source tracking of microbial intrusion in water systems using artificial neural networks", Water Research, 42(4-5), pp.1308~1314 (2008), Elsevier BV[DOI]
- Laaboudi A, "Conceptual Reference Evapotranspiration Models for Different Time Steps", Journal of Petroleum & Environmental Biotechnology, 03(04), pp.1~8 (2012), OMICS Publishing Group[DOI]
- Landeras G., "Comparison of artificial neural network models and empirical and semi-empirical equations for daily reference evapotranspiration estimation in the Basque Country(Northern Spain)", Agricultural Water Management, 95, pp.553~565 (2008)[DOI]
- 이은정, "인공신경망 기법을 이용한 장래 잠재증발산량 산정", 한국농공학회논문집, 52(5), pp.1~9 (2010), 한국농공학회
- Lu, Y., "A simple method for estimating field crop evapotranspiration from Pot Experiments", Water, 10, pp.1~19 (2018)[DOI]
- Holger R. Maier, "Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications", Environmental Modelling & Software, 15(1), pp.101~124 (2000), Elsevier BV[DOI]
- Mia, M. M., "An algorithm for training multilayer perceptron (MLP) For image reconstruction using neural network without overfitting", IJSTR, 10, pp.271~275 (2015)
- Palayasoot, P., "Estimation of pan evaporation and potential evapotranspiration of rice in the central plain of Thailand by using various formulas based on climatological data", M. S., pp.1965
- Parisi, S., "Mini-lysimeters evapotranspiration measurements on suburban environment", Italian Journal of Agrometeorologoy, 3, pp.13~16 (2009)
- Report of the expert consultation on procedures for revision of FAO guidelines for prediction of crop water requirements, "Smith, M.", UN-FAO, 54~ (1992)
- Sudheer, K. P., "Radial basis function neural networks for modeling stage discharge relationship", J. Hydrol. Eng, 8(3), pp.161~164 (2003)
- Traoré, A., "Multivariate analyses on morphological traits in Burkina Faso goat", Arch Anim Breed, 51, pp.588~600 (2008)[DOI]
- Khyati Vyas, "Application of Artificial Neural Network Approach for Estimating Reference Evapotranspir", Current World Environment, 11(2), pp.637~647 (2016), Enviro Research Publishers[DOI]
- Zanetti, S. S., "Estimating evapotranspiration using artificial neural network and minimum climatological data", Journal of Irrigation and Drainage Engineering, 133(2), pp.83~89 (2007)[DOI]
- Wenyan Wu, "Protocol for developing ANN models and its application to the assessment of the quality of the ANN model development process in drinking water quality modelling", Environmental Modelling & Software, 54, pp.108~127 (2014), Elsevier BV[DOI]