Abstract
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.
Highlights
Due to the complexity of the concept, distinctions are made among reference crop evapotranspiration (ETo), crop evapotranspiration under standard conditions (ETc) and crop evapotranspiration under non-standard conditions (ETc-adj)
I, with a pronounced dry season from November to April, and wet during the rest of the Figure 1 shows the weather stations in Region IV-A, a region in the Philippines conyear, which includes the stations in UPLB, Sangley Point, and Ambulong; Type III, with a sidered in this study
Accurate estimates of ETo are important in irrigation planning and design, irrigation scheduling, reservoir management among other applications
Summary
The simultaneous occurrence of, and the difficulty of separately measuring, evaporation and transpirations gives rise to the concept of evapotranspiration [1]. Both evaporation and transpiration are influenced by factors such as weather parameters, crop characteristics, management, and environmental factors. The concept of ETo was introduced to study the evaporative demand of the atmosphere independently of crop type, crop development, and management practices. ETo is one of the most important agrometeorological inputs in the estimation of the irrigation water requirements needed for irrigation system planning, design, and operation [2–4]. Knowledge of ETo is needed in the development of hydrologic models for streamflow estimation and flood inundation [5–7], reservoir design and operation [8,9], climate change studies [10–12], and drought severity analyses [13,14]. The importance of accurate methods for estimating ETo can never be overemphasized
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