Reference evapotranspiration estimated by Penman-Monteith-Fao, Priestley-Taylor, Hargreaves and ANN - Santiago-Rodríguez, Sandra; Arteaga-Ramírez, Ramón; Sangerman-Jarquín, Dora Ma.; Cervantes-Osornio, Rocío; Navarro Bravo, Agustín (Revista Mexicana de Ciencias Agrícolas, 2018)
The irrigation water is a requirement of crops and is based on the estimation of reference evapotranspiration (ETo) of each particular area, are subject to the limitation of existing meteorological data. The objective was to calculate ETo with FAO-Penman-Monteith (FAO-PM), Hargreaves (H), Priestly-Taylor (PT) and artificial neural networks (ANN). We used data from the weather station of Chapingo for the period 2003-2009. In the H and PT methods, four climatic variables were used for their calculation and in ANN were constructed different scenarios to evaluate the performance of the network, by changing the input climatic variables and the number of neurons in the hidden layer. The results of the coefficient of determination (r2) and root mean square error (RMSE) of H and PT are: 0.5378, 0.8553 and 0.6977, 0.6501 respectively. For ANN was found that with the largest number of variables and neurons in the hidden layer was obtained an r2 0.9986, and RMSE 0.0297 and in the scenario with the least number of variables and neurons in the hidden layer were 0.7549 and 0.5555. If you count with all the climatic variables ANN is better because the RMSE results are close to zero and its r2 approaches to one. If ANN decreases the number of neurons in the hidden layer and the variables, gives the greatest error estimate of ETo, but lower than those obtained by H and PT.
Keywords: Math lab; estimation; forecast; irrigation; weather variables