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Related Experiment Video

Updated: Dec 20, 2025

Measurement of Leaf Hydraulic Conductance and Stomatal Conductance and Their Responses to Irradiance and Dehydration Using the Evaporative Flux Method EFM
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Artificial intelligence models versus empirical equations for modeling monthly reference evapotranspiration.

Yazid Tikhamarine1, Anurag Malik2, Doudja Souag-Gamane1

  • 1Leghyd Laboratory, Department of Civil Engineering, University of Sciences and Technology Houari Boumediene, BP 32 Al Alia, Babezzouar, Algiers, Algeria.

Environmental Science and Pollution Research International
|May 24, 2020
PubMed
Summary

Accurate reference evapotranspiration (ETo) estimation is vital for water management. A new hybrid artificial intelligence model, Support Vector Regression integrated with Grey Wolf Optimizer (SVR-GWO), shows superior performance in estimating ETo using climatic data.

Keywords:
AlgeriaEmpirical methodsHybrid AI modelsMetaheuristic algorithmsReference evapotranspiration

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Area of Science:

  • Hydrology and Water Resources Management
  • Artificial Intelligence in Environmental Science
  • Agricultural Meteorology

Background:

  • Accurate estimation of reference evapotranspiration (ETo) is critical for crop modeling, water resource management, and irrigation scheduling, accounting for significant global precipitation losses.
  • Traditional methods like the FAO-56 Penman-Monteith (FAO-56 PM) require extensive climatic data, which are often unavailable.
  • Artificial intelligence (AI) models offer a promising alternative for estimating ETo across various time scales.

Purpose of the Study:

  • To explore the potential of a novel hybrid AI model, Support Vector Regression integrated with Grey Wolf Optimizer (SVR-GWO), for estimating monthly ETo.
  • To evaluate the performance of the SVR-GWO model against other AI and empirical methods using key statistical metrics.
  • To assess the model's applicability in meteorological stations in northern Algeria.

Main Methods:

  • Developed and tested a hybrid SVR-GWO model for monthly ETo estimation.
  • Utilized five climatic variables: relative humidity (RH), maximum and minimum air temperatures (Tmax, Tmin), solar radiation (Rs), and wind speed (Us).
  • Compared SVR-GWO with SVR-Genetic Algorithm (SVR-GA), SVR-Particle Swarm Optimization (SVR-PSO), Artificial Neural Network (ANN), and empirical methods (Turc, Ritchie, Thornthwaite, Valiantzas).

Main Results:

  • The proposed SVR-GWO model demonstrated superior performance in estimating ETo at Algiers, Tlemcen, and Annaba stations.
  • Achieved excellent statistical indicators: RMSE (0.0776/0.0613/0.0374 mm), NSE (0.9953/0.9990/0.9995), PCC (0.9978/0.9995/0.9998), and WI (0.9988/0.9997/0.9999).
  • The SVR-GWO model consistently outperformed other data-driven and empirical methods evaluated.

Conclusions:

  • The hybrid SVR-GWO model is highly suitable and effective for estimating monthly ETo in the studied Algerian regions.
  • The model's accuracy and robustness suggest its potential for wider application in water resource management.
  • Future research should focus on testing and adapting the SVR-GWO model in diverse geographical locations globally.