Open AccessModeling Reference Crop Evapotranspiration Using Support Vector Machine (SVM) and Extreme Learning Machine (ELM) in Region IV-A, Philippines
Author(s) -
Allan T. Tejada,
Victor B. Ella,
Rubenito M. Lampayan,
Consorcia E. Reaño
Publication year - 2022
Publication title -
water
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.718
H-Index - 55
ISSN - 2073-4441
DOI - 10.3390/w14050754
Subject(s) - support vector machine , irrigation scheduling , extreme learning machine , empirical modelling , evapotranspiration , machine learning , wind speed , irrigation , penman–monteith equation , computer science , artificial intelligence , environmental science , meteorology , simulation , artificial neural network , geography , ecology , biology
The need for accurate estimates of reference crop evapotranspiration (ETo) is important in irrigation planning and design, irrigation scheduling, reservoir management among other applications. ETo can be accurately determined using the internationally accepted FAO Penman–Monteith (FAO-56 PM) equation. However, this requires numerous observed data, including solar radiation, air temperature, relative humidity, and wind speed, which in most cases are unavailable, particularly in developing countries such as the Philippines. This study developed models based on Support Vector Machines (SVMs) and Extreme Learning Machines (ELMs) for the estimation of daily ETo using different input combinations of meteorological data in Region IV-A, Philippines. The performance of machine learning models was compared with the different established alternative empirical models for ETo. The results show that the SVM and ELM models, with at least Tmax, Tmin, and Rs as inputs, provide the best daily ETo estimates. The accuracy of machine learning models was also found to be superior compared to the empirical models given with same input requirements. In general, SVM and ELM models showed similar modeling performance, although the former showed lower run time than the latter.