Performance evaluation of numerical and machine learning methods in estimating reference evapotranspiration in a Brazili
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ORIGINAL PAPER
Performance evaluation of numerical and machine learning methods in estimating reference evapotranspiration in a Brazilian agricultural frontier Diego Bispo dos Santos Farias 1
&
Daniel Althoff 1 & Lineu Neiva Rodrigues 1,2 & Roberto Filgueiras 1
Received: 25 March 2020 / Accepted: 13 September 2020 / Published online: 24 September 2020 # Springer-Verlag GmbH Austria, part of Springer Nature 2020
Abstract The reference evapotranspiration (ET0) estimates is important for water resources and irrigation management. The PenmanMonteith equation is known for its accuracy but requires a high number of climatic parameters that are not always available. Thus, this study aimed to evaluate the performance of machine learning techniques (cubist regression, artificial neural network with Bayesian regularization, support vector machine with linear kernel function) and stepwise multiple linear regression method to estimate daily ET0 with limited weather data in a Brazilian agricultural frontier (MATOPIBA). Climatic data from 2000 to 2016 obtained from 23 weather stations were used. Five data scenarios were evaluated: (i) all variables, (ii) radiation and temperature, (iii) temperature and relative humidity, (iv) wind speed and temperature, and (v) temperature. The results showed that the machine learning methods are robust in estimating ET0, even in the absence of some variables. Among the methods evaluated using only temperature data, the cubist regression showed better performance. When estimating water demand for soybean and maize crops using only temperature, the cubist regression and calibrated Hargreaves-Samani equation showed the smallest errors.
1 Introduction The intensification of agriculture, that is, increasing production per unit of planted area combined with the reduction of environmental impacts, is the most appropriate strategy to increase food production in a sustainable manner (Pradhan et al. 2015). The intensification of agriculture, in turn, will increasingly depend on irrigation, which is the main user of water resources in Brazil and worldwide (ANA 2017; FAO 2015). Increasing the irrigated area may intensify conflicts over the use of water, especially in hydrographic basins where there is already a compromised water availability. In order to have water security in those basins, it is important that water is used * Diego Bispo dos Santos Farias [email protected] 1
Department of Agricultural Engineering, Federal University of Viçosa (UFV), Av. Peter Henry Rolfs, s.n, Viçosa, Minas Gerais 36570-900, Brazil
2
Brazilian Agricultural Research Corporation, Embrapa Cerrados, BR-020, Km 18, Planaltina, DF 73310-970, Brazil
in a sustainable manner. Therefore, it is necessary to improve the management and efficiency of use of irrigation water (Fishman et al. 2015) Obtaining reliable estimates of crop evapotranspiration (ETc) is essential for the development of irrigation management strategies. In addition, these estimates for remote rural areas, with little information, which are prevalent in B
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