Prediction of Potential Evapotranspiration Using Temperature-Based Heuristic Approaches

Rana Muhammad Adnan, Salim Heddam, Zaher Mundher Yaseen, Shamsuddin Shahid, Ozgur Kisi and Binquan Li
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Rana Muhammad Adnan: State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China
Salim Heddam: Faculty of Science, Agronomy Department, Hydraulics Division, University of Skikda, Skikda 21000, Algeria
Zaher Mundher Yaseen: Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
Shamsuddin Shahid: Faculty of Engineering, School of Civil Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia
Ozgur Kisi: Faculty of Engineering, School of Civil Engineering, Universiti Teknologi Malaysia (UTM), Johor Bahru 81310, Malaysia
Binquan Li: State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China

Sustainability, 2020, vol. 13, issue 1, 1-21

Abstract: The potential or reference evapotranspiration ( ET 0 ) is considered as one of the fundamental variables for irrigation management, agricultural planning, and modeling different hydrological pr°Cesses, and therefore, its accurate prediction is highly essential. The study validates the feasibility of new temperature based heuristic models (i.e., group method of data handling neural network (GMDHNN), multivariate adaptive regression spline (MARS), and M5 model tree (M5Tree)) for estimating monthly ET 0 . The outcomes of the newly developed models are compared with empirical formulations including Hargreaves-Samani (HS), calibrated HS, and Stephens-Stewart (SS) models based on mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutcliffe efficiency. Monthly maximum and minimum temperatures ( T max and T min ) observed at two stations in Turkey are utilized as inputs for model development. In the applications, three data division scenarios are utilized and the effect of periodicity component (PC) on models’ accuracies are also examined. By importing PC into the model inputs, the RMSE accuracy of GMDHNN, MARS, and M5Tree models increased by 1.4%, 8%, and 6% in one station, respectively. The GMDHNN model with periodic input provides a superior performance to the other alternatives in both stations. The recommended model reduced the average error of MARS, M5Tree, HS, CHS, and SS models with respect to RMSE by 3.7–6.4%, 10.7–3.9%, 76–75%, 10–35%, and 0.8–17% in estimating monthly ET 0 , respectively. The HS model provides the worst accuracy while the calibrated version significantly improves its accuracy. The GMDHNN, MARS, M5Tree, SS, and CHS models are also compared in estimating monthly mean ET 0 . The GMDHNN generally gave the best accuracy while the CHS provides considerably over/under-estimations. The study indicated that the only one data splitting scenario may mislead the modeler and for better validation of the heuristic methods, more data splitting scenarios should be applied.

Keywords: potential evapotranspiration; heuristic models; empirical formulation; hydrological processes; water management and sustainability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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