Development of a Temperature-Based Model Using Machine Learning Algorithms for the Projection of Evapotranspiration of Peninsular Malaysia

Abstract

Reliable projections of evapotranspiration (ET) are important for agricultural and water resources development, planning, and management. However, ET projections using well established empirical models suffer from uncertainty due to their dependency on many climatic variables. This study aimed to develop temperature-based empirical ET models using Gene Expression Programming (GEP) for the reliable estimation and projection of ET in peninsular Malaysia within the context of global warming. The efficiency of the GEP-generated equation was compared to the existing methods. Finally, the GEP ET formulas were used to project ET from the downscaled and projected temperature of nine global climate models (GCMs) for four Representative Concentration Pathways (RCPs), namely, RCP 2.6, 4.5, 6.0, and 8.5, at ten locations of peninsular Malaysia. The results revealed improved performance of GEP models in all standard statistics. Downscaled temperatures revealed a rise in minimum and maximum temperatures in the range of 2.47–3.30 °C and 2.79–3.24 °C, respectively, during 2010–2099. The ET projections in peninsular Malaysia showed changes from −4.35 to 7.06% for RCP2.6, −1.99 to 16.76% for RCP4.5, −1.66 to 22.14% for RCP6.0 and −0.91 to 39.7% for RCP8.5 during 2010−2099. A higher rise in ET was projected over the northern peninsula than in the other parts.