Monthly evapotranspiration estimation using optimal climatic parameters: efficacy of hybrid support vector regression integrated with whale optimization algorithm

• Published in: Environmental monitoring and assessment Vol. 192; no. 11; p. 696
• Main Authors: Tikhamarine, Yazid, Malik, Anurag, Pandey, Kusum, Sammen, Saad Shauket, Souag-Gamane, Doudja, Heddam, Salim, Kisi, Ozgur
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01-11-2020; Springer Nature B.V
• Subjects: Air temperature; Algorithms; Atmospheric Protection/Air Quality Control/Air Pollution; Coefficient of variation; Correlation coefficient; Correlation coefficients; Earth and Environmental Science; Ecology; Ecotoxicology; Environment; Environmental Management; Environmental monitoring; Environmental science; Error analysis; Evapotranspiration; Evapotranspiration estimates; Irrigation scheduling; Irrigation water; Mathematical models; Model accuracy; Monitoring/Environmental Analysis; Monthly; Optimization; Optimization algorithms; Parameter estimation; Parameters; Radar; Relative humidity; Root-mean-square errors; Solar radiation; Support vector machines; Water resources; Water resources management; Weather stations; Wind speed; Reference evapotranspiration; Algeria; Hybrid SVR models; Nature-inspired algorithms
• ISSN: 0167-6369; 1573-2959
• DOI: 10.1007/s10661-020-08659-7

For effective planning of irrigation scheduling, water budgeting, crop simulation, and water resources management, the accurate estimation of reference evapotranspiration (ET
o
) is essential. In the current study, the hybrid support vector regression (SVR) coupled with Whale Optimization Algorithm (SVR-WOA) was employed to estimate the monthly ET
o
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 (
R
s
), wind speed (
U
s
), relative humidity (RH), and maximum and minimum air temperatures (
T
max
and
T
min
) 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), root-mean-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 SVR-WOA 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.,
T
min,
T
max,
RH,
U
s
,
R
s
: 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 ET
o
in the study region.