Επίπεδο εγγραφής:	Έργο ολοκληρωμένο
Χρονολογία:	2026
Θέμα:	Meteorology Μετεωρολογία
Δημιουργός:	Amanatidis, Petros Stefanidis, Stefanos Ioannou, Konstantinos Proutsos, Nikolaos Karmiris, Ilias Stefanidis, Panagiotis Karampatzakis, Dimitris Προύτσος, Νικόλαος
Τίτλος:	Transferable deep learning models for the estimation of daily potential evapotranspiration across altitudinal forest gradients in the Mediterranean
Γλώσσα:	Αγγλικά
Λέξεις-Κλειδιά:	Meteorology; Evapotranspiration; Deep Learning; Μετεωρολογία; Εξατμισοδιαπνοή
Προσδιοριστής:	DOI: https
Περίληψη:	The potential Evapotranspiration (PET) is a crucial hydrological variable for understanding the water balance and ecosystem dynamics, particularly in different altitudinal Mediterranean forest environments. Traditional empirical methods for estimating PET have frequently failed to generalize accurately to heterogeneous environments, including Mediterranean forests, due to climatic and topographic diversity. This study develops and evaluates bi-directional transferable Deep Learning (DL) models for PET estimation using meteorological and environmental data collected from Mediterranean forest areas, namely Pertouli and Taxiarchis, covering multiple altitudinal levels. The models were trained and evaluated in both directions: first trained on Pertouli and transferred to Taxiarchis, and second trained on Taxiarchis and transferred to Pertouli. These models were deployed and retrained using Transfer Learning (TL) for PET estimation to address the limited data availability at the target site. Several model architectures were implemented and evaluated in different Scenarios. The experimental findings show that the transferable DL models achieve R2 values between 0.7 and 0.87 and RMSE values from 0.55 to 0.75, according to the data availability of the target site and the meteorological data used for training. Among the evaluated model architectures, the RNN model achieved the highest performance with R2 = 0.767, MAE = 0.521, RMSE = 0.668, and sRPIQ = 0.755 when the training data were limited, while the LSTM obtained the highest accuracy under higher data availability conditions, namely, R2 = 0.873, MAE = 0.384, RMSE = 0.55, and sRPIQ = 0.831. This work demonstrates the feasibility of bi-directional model transfer across altitudinal forest gradients under Mediterranean conditions.
Εξειδίκευση τύπου :	Άρθρο
Τίτλος περιέχουσας πηγής:	Acta Geophysica
Άδεια χρήσης:	Αναφορά Μη Εμπορική Χρήση Παρόμοια Δανομή
Διάθεση ψηφιακού τεκμηρίου:	Δεν διατίθεται
Αρίθμηση περιέχουσας πηγής:	Vol.74, Αrticle nο.104
Εναλλακτικός δικτυακός τόπος:	https://link.springer.com/article/10.1007/s11600-026-01836-1
Εμφανίζεται στις Ομάδες Τεκμηρίων:	2.1.3. Κλιματολογία - Μετεωρολογία

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