A Stochastic Bayesian Artificial Intelligence Framework to Assess Climatological Water Balance under Missing Variables for Evapotranspiration Estimates

The sustainable use of water resources is of utmost importance given climatological changes and water scarcity, alongside the many socioeconomic factors that rely on clean water availability, such as food security. In this context, developing tools to minimize water waste in irrigation is paramount for sustainable food production. The evapotranspiration estimate is a tool to evaluate the water volume required to achieve optimal crop yield with the least amount of water waste. The Penman-Monteith equation is the gold standard for this task, despite it becoming inapplicable if any of its required climatological variables are missing. In this paper, we present a stochastic Bayesian framework to model the non-linear and non-stationary time series for the evapotranspiration estimate via Bayesian regression. We also leverage Bayesian networks and Bayesian inference to provide estimates for missing climatological data. Our obtained Bayesian regression equation achieves 0.087 mm · day−1 for the RMSE metric, compared to the expected time series, with wind speed and net incident solar radiation as the main components. Lastly, we show that the evapotranspiration time series, with missing climatological data inferred by the Bayesian network, achieves an RMSE metric ranging from 0.074 to 0.286 mm · day−1.