Machine learning and conventional method for reference evapotranspiration estimation using limited climatic data scenarios

Abstract

Abstract Reference evapotranspiration (ET0) is one important agrometeorological parameter for hydrological studies and agricultural water management. The ET0 calculated by the Penman-Monteith - FAO method requires several input data. However, in the Minas Gerais region, the meteorological data are limited. The aim of this study was to evaluate the performance of Artificial Neural Network (ANN), Random Forest (RF), Support Vector Machine (SVM) and Multiple Linear Regression (MLR) to estimate the monthly mean ET0 with different input data combinations and scenarios. Three scenarios were evaluated: at the state level, where all climatological stations were used (Scenario I - SI) ; and at regional level, where the Minas Gerais state was divided according to the climatic classification of Thornthwaite (Scenario II - SII) and by Köppen (Scenario III - SIII). ANN and RF performed better in ET0 estimating among the models evaluated in the SI, SII and SIII scenarios with the following data combination: i) latitude, longitude, altitude, month, mean, maximum and minimum temperature, and relative humidity; and ii) latitude, longitude, altitude, month, mean temperature, and relative humidity. Also, the SVM and MLR models are recommended for all scenarios in situations with limited climatic data, where only air temperature and relative humidity data are available. Although dividing into scenarios results in less input data for models training, SII and SIII showed a slightly better result in the southern areas of the Minas Gerais state.