Feedforward backpropagation, genetic algorithm approaches for predicting reference evapotranspiration

Abstract

Water scarcity is a global concern, as the demand for water is increasing tremendously and poor management of water resources will accelerates dramatically the depletion of available water. The precise prediction of evapotranspiration (ET), that consumes almost 100% of the supplied irrigation water, is one of the goals that should be adopted in order to avoid more squandering of water especially in arid and semiarid regions. The capabilities of feedforward backpropagation neural networks (FFBP) in predicting reference evapotranspiration (ET<inf>0</inf>) are evaluated in this paper in comparison with the empirical FAO Penman-Monteith (P-M) equation, later a model of FFBP+Genetic Algorithm (GA) is implemented for the same evaluation purpose. The study location is the main station in Iraq, namely Baghdad Station. Records of weather variables from the related meteorological station, including monthly mean records of maximum air temperature (T<inf>max</inf>), minimum air temperature (T<inf>min</inf>), sunshine hours (R<inf>n</inf>), relative humidity (R<inf>h</inf>) and wind speed (U<inf>2</inf>), from the related meteorological station are used in the prediction of ET<inf>0</inf> values. The performance of both simulation models were evaluated using statistical coefficients such as the root of mean squared error (RMSE), mean absolute error (MAE) and coefficient of determination (R2). The results of both models are promising, however the hybrid model shows higher efficiency in predicting ET<inf>0</inf> and could be recommended for modeling of ET<inf>0</inf> in arid and semiarid regions.