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Artificial neural network for modeling reference evapotranspiration complex process in Sudano-Sahelian zone

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  • Traore, Seydou
  • Wang, Yu-Min
  • Kerh, Tienfuan

Abstract

The major problem when dealing with modeling evapotranspiration process is its nonlinear dynamic high complexity. Researchers developed reference evapotranspiration (ET-ref) estimation models in rich and poor data situations. Thus, the well-known Penman-Monteith (PM) model always performs the highest accuracy results of ET-ref from a rich data situation. Its application in many areas particularly in developing countries such as Burkina Faso has been limited by the unavailability of the enormous climatic data required. In such circumstances, simple empirical Hargreaves (HARG) equation is often used despite of its non-universal suitability. The present study assesses the artificial neural network (ANN) performance in ET-ref modeling based on temperature data in Bobo-Dioulasso region, located in the Sudano-Sahelian zone of Burkina Faso. The models of feed forward backpropagation neural network (BPNN) algorithm type ANN and Hargreaves (HARG) were employed to study their performance by comparing with the true PM. From the statistical results, BPNN temperature-based models perform better than HARG. Beside, when wind speed is introduced into the neural network models, the coefficient of determination (r2) increases significantly up to 9.52%. While, sunshine duration and relative humidity might cause only 3.51 and 6.69% of difference, respectively. Wind is found to be the most effective variable extremely required for modeling with high accuracy the nonlinear complex process of ET-ref in the Sudano-Sahelian zone of Burkina Faso.

Suggested Citation

  • Traore, Seydou & Wang, Yu-Min & Kerh, Tienfuan, 2010. "Artificial neural network for modeling reference evapotranspiration complex process in Sudano-Sahelian zone," Agricultural Water Management, Elsevier, vol. 97(5), pages 707-714, May.
    • Handle: RePEc:eee:agiwat:v:97:y:2010:i:5:p:707-714
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    7. Yufeng Luo & Seydou Traore & Xinwei Lyu & Weiguang Wang & Ying Wang & Yongyu Xie & Xiyun Jiao & Guy Fipps, 2015. "Medium Range Daily Reference Evapotranspiration Forecasting by Using ANN and Public Weather Forecasts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3863-3876, August.
    8. Erdem Küçüktopcu & Emirhan Cemek & Bilal Cemek & Halis Simsek, 2023. "Hybrid Statistical and Machine Learning Methods for Daily Evapotranspiration Modeling," Sustainability, MDPI, vol. 15(7), pages 1-15, March.
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    11. Junzeng Xu & Junmei Wang & Qi Wei & Yanhua Wang, 2016. "Symbolic Regression Equations for Calculating Daily Reference Evapotranspiration with the Same Input to Hargreaves-Samani in Arid China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(6), pages 2055-2073, April.
    12. Saeedeh Shirin Manesh & Hossein Ahani & Mehdi Rezaeian-Zadeh, 2014. "ANN-based mapping of monthly reference crop evapotranspiration by using altitude, latitude and longitude data in Fars province, Iran," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 16(1), pages 103-122, February.
    13. Traore, Seydou & Luo, Yufeng & Fipps, Guy, 2016. "Deployment of artificial neural network for short-term forecasting of evapotranspiration using public weather forecast restricted messages," Agricultural Water Management, Elsevier, vol. 163(C), pages 363-379.
    14. Chen, Han & Huang, Jinhui Jeanne & McBean, Edward, 2020. "Partitioning of daily evapotranspiration using a modified shuttleworth-wallace model, random Forest and support vector regression, for a cabbage farmland," Agricultural Water Management, Elsevier, vol. 228(C).
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    16. Yamaç, Sevim Seda & Todorovic, Mladen, 2020. "Estimation of daily potato crop evapotranspiration using three different machine learning algorithms and four scenarios of available meteorological data," Agricultural Water Management, Elsevier, vol. 228(C).

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