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Evapotranspiration estimation using hybrid and intelligent methods

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Abstract

Accurate prediction of reference evapotranspiration (ET0) is very valuable since it directly affects the amount of agricultural water needed, the allocation of water consumption and the management of irrigation systems. This research consists of two parts; in the first part, ET0 was predicted by two machine learning models (artificial neural network (ANN)) and tree model (MT) with two empirical equations (Romanenko and Schendel). In the second part, a new preprocessing algorithm, ensemble empirical mode decomposition (EEMD), was used to improve the prediction accuracy of the MT model and eliminate time-series noises. This approach was applied to several study areas in northwest of Iran including Urmia, Mahabad, Khoy, Takab, and Sardasht stations so that daily reference evapotranspiration values were calculated based on FAO-Penman equation. The parameters of average temperature, average relative humidity, average vapor pressure and average wind speed were input parameters to the models. The results of the first part showed that the performance of MT (with RMSE: 0.58 mm/day, RSD: 0.18, NSE: 0.97) has better performance than the ANN model (with RMSE: 0.70 mm/day, RSD: 0.22, NSE: 0.95). In Urmia station, EEMD–MT enhanced the MT model about 19% reduction in RMSE. Among the investigated models, the EEMD–MT model had the highest ET0 prediction accuracy in all investigated stations. The results of the machine learning models showed a better performance than the empirical used equations. This research showed that using the new EEMD processing algorithm can improve the accuracy of hydrological forecasts and time series.

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Authors and Affiliations

  1. Department of Water Engineering, Urmia University, 11 Km Sero Road, Post Box: 165, Urmia, 5756151818, Iran

    Amin Amirashayeri, Javad Behmanesh & Vahid Rezaverdinejad

  2. Department of Water Engineering, Tabriz University, Tabriz, Iran

    Nasrin Fathollahzadeh Attar

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  1. Amin Amirashayeri
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  2. Javad Behmanesh
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  3. Vahid Rezaverdinejad
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  4. Nasrin Fathollahzadeh Attar
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Correspondence to Javad Behmanesh.

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Amirashayeri, A., Behmanesh, J., Rezaverdinejad, V. et al. Evapotranspiration estimation using hybrid and intelligent methods. Soft Comput 27, 9801–9821 (2023). https://doi.org/10.1007/s00500-023-07822-9

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