Utility of Artificial Neural Networks in Modeling Pan Evaporation in Hyper-Arid Climates

Evaporation is the major water-loss component of the hydrologic cycle and thus requires efficient management. This study aims to model daily pan evaporation rates in hyper-arid climates using artificial neural networks (ANNs). Hyper-arid climates are characterized by harsh environmental conditions where annual precipitation rates do not exceed 3% of annual evaporation rates. For the first time, ANNs were applied to model such climatic conditions in the State of Kuwait. Pan evaporation data from 1993-2015 were normalized to a 0-1 range to boost ANN performance and the ANN structure was optimized by testing various meteorological input combinations. Levenberg-Marquardt algorithms were used to train the ANN models. The proposed ANN was satisfactorily efficient in modeling pan evaporation in these hyper-arid climatic conditions. The Nash-Sutcliffe coefficients ranged from 0.405 to 0.755 over the validation period. Mean air temperatures and average wind speeds were identified as meteorological variables that most influenced the ANN performance. A sensitivity analysis showed that the number of hidden layers did not significantly impact the ANN performance. The ANN models demonstrated considerable bias in predicting high pan evaporation rates (>25 mm/day). The proposed modeling method may assist water managers in Kuwait and other hyper-arid regions in establishing resilient water-management plans.