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Monthly evapotranspiration estimation using optimal climatic parameters: efficacy of hybrid support vector regression
Yazid Tikhamarine1,2, Anurag Malik3, Kusum Pandey4
1Department of Science and Technology, University of Tamanrasset, BP 10034 Sersouf, Tamanrasset, 11000, Algeria.
Environmental Monitoring and Assessment
|October 11, 2020
Summary
Accurate estimation of reference evapotranspiration (ETo) is crucial for water management. A hybrid Support Vector Regression with Whale Optimization Algorithm (SVR-WOA) model demonstrated superior performance in estimating monthly ETo in Algeria.
Area of Science:
- Hydrology and Agricultural Meteorology
- Computational Intelligence in Environmental Science
Background:
- Accurate estimation of reference evapotranspiration (ETo) is vital for irrigation scheduling, water budgeting, crop simulation, and water resources management.
- Algeria faces challenges in water resource management, necessitating precise ETo calculations for agricultural planning.
Purpose of the Study:
- To develop and evaluate a hybrid Support Vector Regression (SVR) model integrated with the Whale Optimization Algorithm (WOA) for estimating monthly ETo.
- To compare the performance of the SVR-WOA model against other hybrid models (SVR-MVO, SVR-ALO) using various meteorological input scenarios.
Main Methods:
- Utilized monthly climatic data (solar radiation, wind speed, relative humidity, max/min temperatures) from 2000-2013 for Algiers and Tlemcen, Algeria.
- Developed a hybrid Support Vector Regression coupled with Whale Optimization Algorithm (SVR-WOA) model.
- Evaluated model accuracy using statistical metrics (MAE, RMSE, NSE, PCC, IOA, IOS) and graphical analysis.
Main Results:
- The SVR-WOA model significantly outperformed SVR-MVO and SVR-ALO models in estimating monthly ETo at both meteorological stations.
- The SVR-WOA model with five inputs (Tmin, Tmax, RH, Us, Rs) achieved the lowest error (MAE, RMSE) and highest agreement (NSE, PCC, IOA) metrics.
- The proposed SVR-WOA model demonstrated high accuracy and efficiency for monthly ETo estimation in the study region.
Conclusions:
- The hybrid SVR-WOA model is a highly appropriate and efficient tool for estimating monthly reference evapotranspiration.
- This approach offers a reliable method for improving water resource management and agricultural planning in arid and semi-arid regions.
- The study highlights the potential of metaheuristic algorithms combined with machine learning for environmental modeling.
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