Global climate change is likely to influence evapotranspiration (ET); as a result, many ET calculation methods may not give accurate results under different climatic conditions. The main objective of this study is to verify the suitability of machine learning (ML) models as calculation methods for pan evaporation modeling on the macro-regional scale. The most significant PE changes in the different agroclimatic zones of the Slovak Republic were compared, and their considerable impacts were analyzed. On the basis of the agroclimatic zones, 35 meteorological stations distributed across Slovakia were classified into six macro-regions. For each of the meteorological stations, 11 variables were applied during the vegetation period in the years from 2010 to 2020 with a daily time step. The performance of eight different ML models—the neural network (NN) model, the autoneural network (AN) model, the decision tree (DT) model, the Dmine regression (DR) model, the DM neural network (DM NN) model, the gradient boosting (GB) model, the least angle regression (LARS) model, and the ensemble model (EM)—was employed to predict PE. It was found that the different models had diverse prediction accuracies in various geographical locations. In this study, the results of the values predicted by the individual models are compared.

Water stress is an indicator that plant are suffering from drought. For optimization of the crop production is necessary to indicate physiological changes, those can be represented by stem expansion or shrinkage and sap flow rate. We used the Dynagage SGA5-WS (Dynamax) sensors for the sap flow...

The modeling of pan evaporation (Ep) trends in Slovak river sub-basins was conducted using advanced artificial intelligence (AI) techniques algorithms to accurately calculate evaporation rates based on daily climate data from 2010 to 2023 across eight sub-basins in the Slovak Republic. The AI modeling results reveal that the Bodrog, Hornád, Ipeľ, Morava, Slaná, and Váh river basins are experiencing increases in evaporation, while the Dunaj and Hron rivers show declining trends. This divergence may indicate varying ecological factors influencing the evaporation dynamics of each river. A comprehensive set of 28 machine learning (ML) and deep learning (DL) models was employed, including ML techniques such as linear regression, tree-based, support vector machines (both with and without kernels), ensemble, and Gaussian process methods; as well as DL approaches like neural networks (narrow, medium, wide, bilayered, and trilayered). Among these, stepwise linear regression provided the most optimal fit. The minimum redundancy maximum relevance (mRMR) method was utilized for feature selection to balance relevance and redundancy effectively. The results suggest that emphasizing relative humidity (RH) and minimum temperature (t_min) significantly enhances accuracy, highlighting the critical roles of these factors in modeling pan evaporation trends. The results offer precise evaporation analyses to improve water management and lessen scarcity.