Reference evapotranspiration forecasting by artificial neural networks

Abstract

Evapotranspiration (ET) is the main component of water balance in agricultural systems and the most active variable of the hydrological cycle. In the literature, few studies have used the forecast the day before via Artificial Neural Networks (ANNs) for the northern region of São Paulo state, Brazil. Therefore, this aimed to predict the reference evapotranspiration for Jaboticabal, the major sugarcane-producing region of São Paulo state. We used a historical series of data on average air temperature, wind speed, net radiation, soil heat flux, and daily relative humidity from 2002 to 2012, for Jaboticabal, SP (Brazil). ET was estimated by Penman-Monteith method. To forecast reference evapotranspiration, we used a feed-forward Multi-Layer Perceptron (MLP), which is a traditional Artificial Neural Network. Numerous topologies and variations were tested between neurons in intermediate and outer layers until the most accurate were obtained. We separated 75% from data for network training (2002 to 2010) and 25% for testing (2011 to 2013). The criteria for assessing the ANN performance were accuracy, precision, and trend. ET could be accurately estimated with a day to spare at any time of the year, by means of artificial neural networks, and using only air temperature data as an input variable.