RT info:eu-repo/semantics/article
T1 Virtual weather stations for meteorological data estimations
A1 Franco Ortellado, Blas Manuel
A1 Hernández Callejo, Luis
A1 Navas Gracia, Luis Manuel
K1 Machine learning
K1 Neural networks
K1 Temperature
K1 Relative humidity
K1 Evapotranspiration
K1 2509 Meteorología
K1 2509.01 Meteorología agrícola
K1 1203.04 Inteligencia Artificial
AB In this paper, the concept of Virtual Weather Stations (VWS) is introduced. A VWS is an integration of algorithms to download meteorological data, process and use them with the main objective of estimate data in nearby locations with no meteorological stations. To develop the VWS, the performances of different interpolation methods were evaluated to test the accuracy. Daily data from an automatic weather station network, such as precipitation (Precip), air temperature (Temp), air relative humidity, mean wind speed, total solar irradiation, and reference evapotranspiration were interpolated using artificial neural networks (ANNs) with the hardlim, sigmoid, hyperbolic tangent (tanh), softsign, and rectified linear unit (relu) activations functions were employed. To contrast the ANNs interpolations, alternatives methods such as inverse distance weighting, inverse-squared distance weighting, multilinear regression, and random forest regression were used. To validate the models, a randomly selected weather station was removed from the daily datasets, and the interpolated values were compared with the actual station records. Additionally, interpolations in the summer and winter months were performed to check the capability of the models during periods with more extreme phenomena. The results showed that the interpolation methods have an R2 up to 0.98 for variables such as temperatures for the period of 1 year. Meanwhile, during the summer and winter, the models presented lower accuracy. From a practical perspective, the methods here described could be useful to produce meteorological data with the VWS to record temperatures and dose the irrigation in crops.
PB Springer
SN 0941-0643
YR 2020
FD 2020
LK https://uvadoc.uva.es/handle/10324/70823
UL https://uvadoc.uva.es/handle/10324/70823
LA eng
NO Neural Computing and Applications, 2020, vol. 32, p. 12801-12812.
NO Producción Científica
DS UVaDOC
RD 19-oct-2024