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Reference Evapotranspiration Prediction Using Neural Networks and Optimum Time Lags

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  • Milan Gocić

    (University of Nis)

  • Mohammad Arab Amiri

Abstract

The reference evapotranspiration (ET0) plays a significant role especially in agricultural water management and water resources planning for irrigation. It can be calculated using different empirical equations and forecasted by applying various artificial intelligence techniques. The simulation result of a machine learning technique is a function of its structure and model inputs. The purpose of this study is to investigate the effect of using the optimum set of time lags for model inputs on the prediction accuracy of monthly ET0 using an artificial neural network (ANN). For this, the weather data time-series i.e. minimum and maximum air temperatures, vapour pressure, sunshine hours, and wind speed were collected from six meteorological stations in Serbia for the period 1980–2010. Three ANN models were applied to monthly ET0 time-series to study the impacts of using the optimum time lags for input time-series on the performance of ANN model. Achieved results of goodness–of–fit statistics approved the results obtained by scatterplots of testing sets - using more time lags that are selected based on their correlation to the dataset is more efficient for monthly ET0 prediction. It was realized that all the developed models showed the best performances at Loznica and Vranje stations and the worst performances at Nis station. Simultaneous assessment of the impact of using a different number of time lags and the set of time lags that show a stronger correlation to the dataset for input time-series, on the performance of ANN model in monthly ET0 prediction in Serbia is the novelty of this study.

Suggested Citation

  • Milan Gocić & Mohammad Arab Amiri, 2021. "Reference Evapotranspiration Prediction Using Neural Networks and Optimum Time Lags," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1913-1926, April.
    • Handle: RePEc:spr:waterr:v:35:y:2021:i:6:d:10.1007_s11269-021-02820-8
    DOI: 10.1007/s11269-021-02820-8
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    References listed on IDEAS

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    1. Yassin, Mohamed A. & Alazba, A.A. & Mattar, Mohamed A., 2016. "Artificial neural networks versus gene expression programming for estimating reference evapotranspiration in arid climate," Agricultural Water Management, Elsevier, vol. 163(C), pages 110-124.
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    5. Mohammadi, Babak & Mehdizadeh, Saeid, 2020. "Modeling daily reference evapotranspiration via a novel approach based on support vector regression coupled with whale optimization algorithm," Agricultural Water Management, Elsevier, vol. 237(C).
    6. Mohd Khairul Idlan Muhammad & Mohamed Salem Nashwan & Shamsuddin Shahid & Tarmizi bin Ismail & Young Hoon Song & Eun-Sung Chung, 2019. "Evaluation of Empirical Reference Evapotranspiration Models Using Compromise Programming: A Case Study of Peninsular Malaysia," Sustainability, MDPI, vol. 11(16), pages 1-19, August.
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    Cited by:

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    2. Hadeel E. Khairan & Salah L. Zubaidi & Syed Fawad Raza & Maysoun Hameed & Nadhir Al-Ansari & Hussein Mohammed Ridha, 2023. "Examination of Single- and Hybrid-Based Metaheuristic Algorithms in ANN Reference Evapotranspiration Estimating," Sustainability, MDPI, vol. 15(19), pages 1-22, September.
    3. Dilip Kumar Roy & Kowshik Kumar Saha & Mohammad Kamruzzaman & Sujit Kumar Biswas & Mohammad Anower Hossain, 2021. "Hierarchical Fuzzy Systems Integrated with Particle Swarm Optimization for Daily Reference Evapotranspiration Prediction: a Novel Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(15), pages 5383-5407, December.
    4. Xiufen Gu & HongGuang Sun & Yong Zhang & Shujun Zhang & Chengpeng Lu, 2022. "Partial Wavelet Coherence to Evaluate Scale-dependent Relationships Between Precipitation/Surface Water and Groundwater Levels in a Groundwater System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2509-2522, May.
    5. Long Zhao & Liwen Xing & Yuhang Wang & Ningbo Cui & Hanmi Zhou & Yi Shi & Sudan Chen & Xinbo Zhao & Zhe Li, 2023. "Prediction Model for Reference Crop Evapotranspiration Based on the Back-propagation Algorithm with Limited Factors," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(3), pages 1207-1222, February.
    6. Mojtaba Kadkhodazadeh & Mahdi Valikhan Anaraki & Amirreza Morshed-Bozorgdel & Saeed Farzin, 2022. "A New Methodology for Reference Evapotranspiration Prediction and Uncertainty Analysis under Climate Change Conditions Based on Machine Learning, Multi Criteria Decision Making and Monte Carlo Methods," Sustainability, MDPI, vol. 14(5), pages 1-37, February.
    7. 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.
    8. Dilip Kumar Roy & Tapash Kumar Sarkar & Sujit Kumar Biswas & Bithin Datta, 2023. "Generalized Daily Reference Evapotranspiration Models Based on a Hybrid Optimization Algorithm Tuned Fuzzy Tree Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 193-218, January.
    9. Hadeel E. Khairan & Salah L. Zubaidi & Mustafa Al-Mukhtar & Anmar Dulaimi & Hussein Al-Bugharbee & Furat A. Al-Faraj & Hussein Mohammed Ridha, 2023. "Assessing the Potential of Hybrid-Based Metaheuristic Algorithms Integrated with ANNs for Accurate Reference Evapotranspiration Forecasting," Sustainability, MDPI, vol. 15(19), pages 1-19, September.

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