Investigation of Multilayer Perceptron Regression-based Models to Forecast Reference Evapotranspiration (ETo)

Abstract

Reference evapotranspiration (ETo) is a valuable factor in the hydrological process and its estimation is a sophisticated and nonlinear problem. In this study, the utility of multilayer perceptron regression is investigated to estimate ETo of Jodhpur city, India which has a hot arid climate. Four different multilayer perceptron regression-based models are created and compared in this study. Multilayer perceptron regression is a popular tool used to predict the results of sophisticated problems. Each created model has a different architecture, in which the size (neurons) of the input and hidden layers is decided by the maximal correlation relationship between meteorological attributes and observed ETo using the Food Agriculture Organization Penman-Monteith method (FAO-PM56). This study found that model with meteorology inputs (namely both high and low temperatures, solar radiation, wind speed at 2 m, and humidity) and nine neurons at the hidden layer achieved high predictive accuracy with mean absolute error (MAE) of 0.08, mean squared error (MSE) of 0.01, root mean squared error (RMSE) of 0.10, Pearson correlation (r) of 0.99, and coefficient of determination (r2) of 0.99. The finding of this study is that the multilayer perceptron regression-based models with at least three meteorological inputs (temperature, solar radiation, and wind speed) can effectively utilize to estimate ETo and may receive attention from agriculturists, engineers, and researchers for irrigation scheduling, water resource handling, crop production enhancement, draught area prediction, etc.