RT info:eu-repo/semantics/article
T1 Prediction of daily ambient temperature and its hourly estimation using artificial neural networks in an agrometeorological station in Castile and León, Spain
A1 Diez, Francisco Javier
A1 Correa Guimaraes, Adriana
A1 Chico Santamarta, Leticia
A1 Martínez Rodríguez, Andrés
A1 Murcia Velasco, Diana Alexandra
A1 Andara, Renato
A1 Navas Gracia, Luis Manuel
K1 Ambient temperature
K1 Evapotranspiration
K1 Climate
K1 Clima
K1 Evaporación (Meteorología) - España
K1 Meteorology, Agricultural
K1 Meteorología agrícola
K1 Redes neuronales (Informática)
K1 Neural networks (Computer science)
K1 Artificial intelligence
K1 Precision farming
K1 Agricultura - Innovaciones tecnológicas
K1 Prediction
K1 Castilla y León - Clima
K1 3102 Ingeniería Agrícola
K1 2502 Climatología
K1 2509 Meteorología
K1 1203.04 Inteligencia Artificial
AB This study evaluates the predictive modeling of the daily ambient temperature (maximum, Tmax; average, Tave; and minimum, Tmin) and its hourly estimation (T0h, …, T23h) using artificial neural networks (ANNs) for agricultural applications. The data, 2004–2010, were used for training and 2011 for validation, recorded at the SIAR agrometeorological station of Mansilla Mayor (León). ANN models for daily prediction have three neurons in the output layer (Tmax(t + 1), Tave(t + 1), Tmin(t + 1)). Two models were evaluated: (1) with three entries (Tmax(t), Tave(t), Tmin(t)), and (2) adding the day of the year (J(t)). The inclusion of J(t) improves the predictions, with an RMSE for Tmax = 2.56, Tave = 1.65 and Tmin = 2.09 (°C), achieving better results than the classical statistical methods (typical year Tave = 3.64 °C; weighted moving mean Tmax = 2.76, Tave = 1.81 and Tmin = 2.52 (°C); linear regression Tave = 1.85 °C; and Fourier Tmax = 3.75, Tave = 2.67 and Tmin = 3.34 (°C)) for one year. The ANN models for hourly estimation have 24 neurons in the output layer (T0h(t), …, T23h(t)) corresponding to the mean hourly temperature. In this case, the inclusion of the day of the year (J(t)) does not significantly improve the estimations, with an RMSE = 1.25 °C, but it improves the results of the ASHRAE method, which obtains an RMSE = 2.36 °C for one week. The results obtained, with lower prediction errors than those achieved with the classical methods, confirm the interest in using the ANN models for predicting temperatures in agricultural applications.
PB MDPI
SN 1424-8220
YR 2022
FD 2022
LK https://uvadoc.uva.es/handle/10324/61886
UL https://uvadoc.uva.es/handle/10324/61886
LA eng
NO Sensors, 2022, Vol. 22, Nº. 13, 4850
NO Producción Científica
DS UVaDOC
RD 27-dic-2024