Abstract
The Penman–Monteith equation (PM) is widely recommended by The Food and Agriculture Organization (FAO) as the method to calculate reference evapotranspiration (ET0). However, the detailed climatological data required by the PM are not often available. The present study aimed to develop bayesian regularized neural networks (BRNN)-based ET0 models and compare its results with the PM approach. Forteen weather stations were selected for this study,located in Juazeiro (BA) and Petrolina (PE) counties, Brazil. BRNN were trained with different parameters choices and obtained R² between 0.96 and 0.99 during training and between 0.95 and 0.98 with validation dataset. Root mean squared error (RMSE) less than 0.10 mm.day-1 for BRNN when compared to PM denoted the good performance of the network using only air temperature, solar radiation and wind speed at average daily scale as input variable. Epistemic and random uncertainties were evaluated and precipitation was identified as the variable with the greatest uncertainty, being therefore discarded for modeling.
Highlights
According to KUMAR et al (2002), evapotranspiration is a complex and nonlinear phenomenon, because it depends on the interaction of several climatic elements as solar radiation, wind speed, air humidity, and temperature, as well as on the type and growth stage of the crop
GAL and GHAHRAMANI (2016) showed that an artificial neural network (ANN) can be approximated to a Gaussian process and for this reason uncertainty estimates can be obtained by training a network with dropout and using dropout at test time too
bayesian regularized neural networks (BRNN) were trained with different parameters choices and obtained R2 between 0.96 and 0.99 during training and between 0.95 and 0.98 with test dataset, and root mean squared error (RMSE) less than 0.10 mm.day-1 compared to PM
Summary
According to KUMAR et al (2002), evapotranspiration is a complex and nonlinear phenomenon, because it depends on the interaction of several climatic elements as solar radiation, wind speed, air humidity, and temperature, as well as on the type and growth stage of the crop. According to PEREIRA et al (2002), the selection of a method for estimating the evapotranspiration depends on several factors. One of these factors is the availability of meteorological data, as the complex methods requiring a high number of variables have applicability only when all necessary data are available. A tool that can be used to estimate ETo is the artificial neural network (ANN)
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