Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/61886
Título
Prediction of daily ambient temperature and its hourly estimation using artificial neural networks in an agrometeorological station in Castile and León, Spain
Autor
Año del Documento
2022
Editorial
MDPI
Descripción
Producción Científica
Documento Fuente
Sensors, 2022, Vol. 22, Nº. 13, 4850
Resumo
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.
Materias (normalizadas)
Ambient temperature
Evapotranspiration
Climate
Clima
Evaporación (Meteorología) - España
Meteorology, Agricultural
Meteorología agrícola
Redes neuronales (Informática)
Neural networks (Computer science)
Artificial intelligence
Precision farming
Agricultura - Innovaciones tecnológicas
Prediction
Castilla y León - Clima
Materias Unesco
3102 Ingeniería Agrícola
2502 Climatología
2509 Meteorología
1203.04 Inteligencia Artificial
ISSN
1424-8220
Revisión por pares
SI
Patrocinador
Unión Europea - (project H2020-FNR-2020-1/CE-FNR-07-2020)
Version del Editor
Propietario de los Derechos
© 2022 The Authors
Idioma
eng
Tipo de versión
info:eu-repo/semantics/publishedVersion
Derechos
openAccess
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