Assessing the Impact of Geographical and Meteorological Information on Machine Learning-Based Reproduction of FAO Penman-Monteith Reference Evapotranspiration
AGRONOMY-BASEL, cilt.16, sa.15, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 16 Sayı: 15
- Basım Tarihi: 2026
- Doi Numarası: 10.3390/agronomy16151409
- Dergi Adı: AGRONOMY-BASEL
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, CAB Abstracts, Geobase, Directory of Open Access Journals, Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest)
- Ondokuz Mayıs Üniversitesi Adresli: Evet
Özet
Reference evapotranspiration (ETo) is essential for irrigation scheduling, water resources management, and climate-related applications, but the FAO Penman-Monteith (FAO-PM) method is often constrained by limited meteorological data availability. This study evaluated four machine learning (ML) algorithms, Kernel Approximation Regression (KAR), Multilayer Perceptron (MLP), Extreme Gradient Boosting (XGB), and Random Forest (RF), for reproducing FAO-PM ETo under different levels of geographical and meteorological information availability in the Czech Republic. Daily observations from 59 meteorological stations (1980-2024) were used to develop eight input scenarios. Model performance was evaluated using a station-wise chronological train-test framework and station-based analyses. The results showed that predictor availability had a greater influence on model performance than model selection. The geographical-information scenario produced the lowest performance, whereas substantial improvements were achieved when meteorological variables were incorporated. Among the single-variable meteorological scenarios, relative humidity provided the greatest improvement in agreement with the FAO-PM ETo benchmark. Across all input scenarios and ML algorithms, testing performance ranged from R2 = 0.683 to 0.998 and RMSE = 0.076 to 0.939 mm d-1, indicating progressively improved agreement with FAO-PM ETo as additional meteorological information became available. The reduced-input scenarios therefore provide a practical approach for approximating FAO-PM ETo at stations represented during model development when some meteorological inputs are unavailable.