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.
Funder
Department of Science and Technology
Subject
Water Science and Technology,Aquatic Science,Geography, Planning and Development,Biochemistry
Reference62 articles.
1. FAO Irrigation and Drainage Paper No. 56 Crop Evapotranspiration (Guidelines for Computing Crop Water Requirements);Allen,1998
2. Crop evapotranspiration estimation with FAO56: Past and future
3. PAES 217: Determination of Irrigation Water Requirements,2017
4. A Review of Evapotranspiration Measurement Models, Techniques and Methods for Open and Closed Agricultural Field Applications
5. Simple hydrologic model for predicting streamflow in small watersheds for irrigation system planning;Ella;Int. Agric. Eng. J.,2016
Cited by
21 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献