Work

Comparative Analysis of Machine Learning Algorithms for Potential Evapotranspiration Estimation Using Limited Data at a High-Altitude Mediterranean Forest

Year: 2025

Type: article

Abstract: Accurate estimation of potential evapotranspiration (PET) is of paramount importance for water resource management, especially in Mediterranean mountainous environments that are often data-scarce and highly sensitive to climate variability. This study evaluates the performance of four machine learning (ML) regression algorithms—Support Vector Regression (SVR), Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and K-Nearest Neighbors (KNN)—in predicting daily PET using limited meteorological data from a high-altitude in Central Greece. The ML models were trained and tested using easily available meteorological inputs—temperature, relative humidity, and extraterrestrial solar radiation—on a dataset covering 11 years (2012–2023). Among the tested configurations, RFR showed the best performance (R2 = 0.917, RMSE = 0.468 mm/d, MAPE = 0.119 mm/d) when all the above-mentioned input variables were included, closely approximating FAO56–PM outputs. Results bring to light the potential of machine learning models to reliably estimate PET in data-scarce conditions, with RFR outperforming others, whereas the inclusion of the easily estimated extraterrestrial radiation parameter in the ML models training enhances PET prediction accuracy.

Source: Atmosphere

DOI: 10.3390/atmos16070851

Authors: Stefanos Stefanidis, Konstantinos Ioannou, Nikolaos Proutsos, Ilias Karmiris, Panagiotis Stefanidis

Institutions: Hellenic Agricultural Organization - DIMITRA, Aristotle University of Thessaloniki

Language: English

Topic: Hydrological Forecasting Using AI

Subfield: Environmental Engineering

Field: Environmental Science

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $2,896