Prediction of reference evapotranspiration for irrigation scheduling using machine learning
Manikumari Nagappan, Vinodhini Gopalakrishnan, Murugappan Alagappan
Abstract
Forecasting of irrigation demand is important for decision-making, and reference evapotranspiration (ETo) is a key determinant in evaluating water demand in advance. However, the precise determination of ETo is fairly difficult, and complex machine learning approaches are often used for this. This study, carried out in Veeranam tank, India, determines the multivariate analysis of correlated variables involved in the estimation and modelling of ETo from 1995 to 2016. A reduced-feature data model was constructed with the most significant variables of the model extracted by principal component analysis. This work also explores the effectiveness of a deep learning neural network (DLNN) with the reduced-feature model in predicting ETo in comparison with the conventional Food and Agriculture Organization of the United Nations (FAO-56) Penman-Monteith equation and the radial basis function neural network (RBFNN) as a baseline machine learning method. The input variable dimensionality was reduced from six to three most significant variables in ETo modelling. Among machine learning methods, DLNN proved to be effective in ETo prediction with the reduced-feature data model.