Forecasting vapor pressure deficit for agricultural water management using machine learning in semi-arid environments

Precise evapotranspiration (ET) estimation is critical for agricultural water management, particularly in waterstressed developing countries. Vapor Pressure Deficit is one of the ET parameters that has a significant impact on its calculation (VPD). This paper forecasts VPD using ensemble learning-based modeling in eight different regions (Dakahliyah, Gharbiyah, Kafr Elsheikh, Dumyat, Port Said, Ismailia, Sharqiyah, and Qalubiyah) in Egypt. In this study, six machine learning algorithms were used: Linear Regression (LR), Additive regression trees (ART), Random SubSpace (RSS), Random Forest (RF), Reduced Error Pruning Tree (REPTree), and Quinlan's M5 algorithm (M5P). Monthly vapor pressure data were obtained from the Japanese 55-year Reanalysis JRA-55 from 1958 to 2021. The dateset has been divided into two segments: the training stage (1958-2005) and the testing stage (2006-2021). Five statistical measures were used to evaluate the model performances: Correlation Coefficient (CC), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative absolute error (RAE), and Root Relative Squared Error (RRSE), across both training and testing stages. RF model outperformed the rest of the models [CC = 0.9694; MAE = 0.0967; RMSE = 0.1252; RAE (%) = 21.7297 and RRSE (%) = 24.0356], followed closely by REPTree and RSS models. On the other hand, M5P model performance remained moderate and both LR and AR model were the worst. During the testing stage, RF outperformed the rest of the models in terms of (which statistic), followed closely by REPTree and RSS models. On the other hand, M5P performance remained moderate and both LR and AR models were the worst. This study recommended using the RF model for future hydro-climatological studies in general, and vapor pressure deficit modeling and prediction in particular. This study enables future magnitudes to be predicted, alerting the authorities and administrators involved to focus their policy-making on more specific pathways toward climate adaptation.