Open AccessComparison the ANN models of Penman-Monteith Potential Evapotranspiration with combination two input of climatological data in Surabaya
Author(s) -
D. A. D. Nusantara,
Feriza Nadiar
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1098/2/022018
Subject(s) - evapotranspiration , wind speed , relative humidity , artificial neural network , environmental science , data set , meteorology , humidity , computer science , machine learning , artificial intelligence , geography , ecology , biology
In Surabaya, as a part of the equatorial region, determine the rate of daily potential evapotranspiration (PET) turns into a requirement. During the scarcity season, the rate of PET significantly increases for certifying water availability. The PET model founded from several inputs of climatological data that are relative-humidity, wind speed, average daily temperature, and the duration of sun exposure. Modeling a PET through prolonged and complicated steps. To simplify the development of modeling PET, this research using Artificial Neural Network (ANN) based on data-driven modeling with fewer inputs. The PET-ANN model intends to match the PET estimated with Penman-Monteith (PM). This research purpose is learning what the best combination of two climatological data as input. The perform MSE and R on the validating process present how the different results come. The results show the best combination of two climatological data from the entire data set as an input. The conclusion is that the combination of relative humidity and wind speed as an input to the PET-ANN presents the best result than other combinations of climatological data. Besides, it approves that the relative humidity and wind speed as an undoubted input to the PET model even using ANN or not.