4.6 Article

Modeling Potential Evapotranspiration by Improved Machine Learning Methods Using Limited Climatic Data

Journal

WATER
Volume 15, Issue 3, Pages -

Publisher

MDPI
DOI: 10.3390/w15030486

Keywords

machine learning; modeling potential evapotranspiration; relevance vector machine (RVM); random vector functional link (RVFL) hybrid modeling; quantum-based avian navigation optimizer algorithm (QANA); artificial hummingbird algorithm (AHA)

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This study investigates the efficiency of two machine-learning methods, random vector functional link (RVFL) and relevance vector machine (RVM), improved with new metaheuristic algorithms, quantum-based avian navigation optimizer algorithm (QANA), and artificial hummingbird algorithm (AHA) in modeling potential evapotranspiration (ET0) using limited climatic data. The results showed that AHA and QANA significantly improved the efficiency of RVFL and RVM in modeling ET0.
Modeling potential evapotranspiration (ET0) is an important issue for water resources planning and management projects involving droughts and flood hazards. Evapotranspiration, one of the main components of the hydrological cycle, is highly effective in drought monitoring. This study investigates the efficiency of two machine-learning methods, random vector functional link (RVFL) and relevance vector machine (RVM), improved with new metaheuristic algorithms, quantum-based avian navigation optimizer algorithm (QANA), and artificial hummingbird algorithm (AHA) in modeling ET0 using limited climatic data, minimum temperature, maximum temperature, and extraterrestrial radiation. The outcomes of the hybrid RVFL-AHA, RVFL-QANA, RVM-AHA, and RVM-QANA models compared with single RVFL and RVM models. Various input combinations and three data split scenarios were employed. The results revealed that the AHA and QANA considerably improved the efficiency of RVFL and RVM methods in modeling ET0. Considering the periodicity component and extraterrestrial radiation as inputs improved the prediction accuracy of the applied methods.

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