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ASSESSING PAN EVAPORATION TRENDS IN THE V-H RIVER BASIN, SLOVAK REPUBLIC, USING ARTIFICIAL INTELLIGENCE TECHNIQUES
Abstract
The combination of Long Short-Term Memory (LSTM) neural networks, comprehensive trend analysis, and standardized pan evaporation measurements creates the capability for understanding and predicting regional evaporation dynamics in the context of climate change in this study. Based on the thorough examination of this study in the Slovak Republic's Vah river basin the K-means clustering analysis revealed three different patterns of evaporation behaviour (1.5-3.2 mm/day); the seasonal analysis revealed that peak evaporation in July exceeded 4 mm and decreased to less than 2 mm in September; and the machine learning validation achieved remarkable results with an RMSE of 0.4. The three main analytical approaches employed in the study are succinctly described in the methodology section: Neural network training with convergence monitoring for model validation, seasonal analysis from May to September for temporal characterisation, and K-means clustering for spatial pattern detection.
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