Monthly evapotranspiration estimation using optimal climatic parameters: efficacy of hybrid support vector regression in
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Monthly evapotranspiration estimation using optimal climatic parameters: efficacy of hybrid support vector regression integrated with whale optimization algorithm Yazid Tikhamarine & Anurag Malik & Kusum Pandey & Saad Shauket Sammen & Doudja Souag-Gamane & Salim Heddam & Ozgur Kisi
Received: 4 May 2020 / Accepted: 5 October 2020 # Springer Nature Switzerland AG 2020
Abstract For effective planning of irrigation scheduling, water budgeting, crop simulation, and water resources management, the accurate estimation of reference evapotranspiration (ETo) is essential. In the current study, the hybrid support vector regression (SVR) coupled with Whale Optimization Algorithm (SVR-WOA) was
Y. Tikhamarine Department of Science and Technology, University of Tamanrasset, BP 10034 Sersouf, Tamanrasset 11000, Algeria Y. Tikhamarine : D. Souag-Gamane Leghyd Laboratory, Department of Civil Engineering, University of Sciences and Technology Houari Boumediene , BP 32 Al Alia, BP 32, Bab Ezzouar, Algiers, Algeria A. Malik (*) Punjab Agricultural University, Regional Research Station, Bathinda 151001 Punjab, India e-mail: [email protected] K. Pandey Department of Soil and Water Engineering, Punjab Agricultural University, Ludhiana 141004 Punjab, India S. S. Sammen Department of Civil Engineering, College of Engineering, Diyala University, Baquba, Diyala 15 Governorate, Iraq S. Heddam Faculty of Science, Agronomy Department, Hydraulics Division, University 20 Août 1955, Route EL HADAIK, BP 26, Skikda, Algeria O. Kisi School of Technology, Ilia State University, Tbilisi, Georgia
employed to estimate the monthly ETo at Algiers and Tlemcen meteorological stations positioned in the north of Algeria under three different optimal input scenarios. Monthly climatic parameters, i.e., solar radiation (Rs), wind speed (Us), relative humidity (RH), and maximum and minimum air temperatures (Tmax and Tmin) of 14 years (2000–2013), were obtained from both stations. The accuracy of the hybrid SVR-WOA model was appraised against hybrid SVR-MVO (Multi-Verse Optimizer), and SVR-ALO (Ant Lion Optimizer) models through performance measures, i.e., mean absolute error (MAE), rootmean-square error (RMSE), index of scattering (IOS), index of agreement (IOA), Pearson correlation coefficient (PCC), Nash-Sutcliffe efficiency (NSE), and graphical interpretation (time-variation and scatter plots, radar chart, and Taylor diagram). The results showed that the SVRWOA model performed superior to the SVR-MVO and SVR-ALO models at both stations in all scenarios. The SVR-WOA-1 model with five inputs (i.e., Tmin, Tmax, RH, Us, Rs: scenario-1) had the lowest value of MAE = 0.0658/0.0489 mm/month, RMSE = 0.0808/0.0617 mm/ month, IOS = 0.0259/0.0165, and the highest value of NSE = 0.9949/0.9989, PCC = 0.9975/0.9995, and IOA = 0.9987/0.9997 for testing period at both stations, respectively. The proposed hybrid SVR-WOA model was found to be more appropriate and efficient in comparison to SVR-MVO and SVR-ALO models for estimating monthly ETo in the study re
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