• español
  • English
  • français
  • Deutsch
  • português (Brasil)
  • italiano
    • español
    • English
    • français
    • Deutsch
    • português (Brasil)
    • italiano
    • español
    • English
    • français
    • Deutsch
    • português (Brasil)
    • italiano
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Listar

    Todo UVaDOCComunidadesPor fecha de publicaciónAutoresMateriasTítulos

    Mi cuenta

    Acceder

    Estadísticas

    Ver Estadísticas de uso

    Compartir

    Ver ítem 
    •   UVaDOC Principal
    • PRODUCCIÓN CIENTÍFICA
    • Departamentos
    • Dpto. Ingeniería Agrícola y Forestal
    • DEP42 - Artículos de revista
    • Ver ítem
    •   UVaDOC Principal
    • PRODUCCIÓN CIENTÍFICA
    • Departamentos
    • Dpto. Ingeniería Agrícola y Forestal
    • DEP42 - Artículos de revista
    • Ver ítem
    • español
    • English
    • français
    • Deutsch
    • português (Brasil)
    • italiano

    Exportar

    RISMendeleyRefworksZotero
    • edm
    • marc
    • xoai
    • qdc
    • ore
    • ese
    • dim
    • uketd_dc
    • oai_dc
    • etdms
    • rdf
    • mods
    • mets
    • didl
    • premis

    Citas

    Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/70823

    Título
    Virtual weather stations for meteorological data estimations
    Autor
    Franco Ortellado, Blas ManuelAutoridad UVA
    Hernández Callejo, LuisAutoridad UVA Orcid
    Navas Gracia, Luis ManuelAutoridad UVA
    Año del Documento
    2020
    Editorial
    Springer
    Descripción
    Producción Científica
    Documento Fuente
    Neural Computing and Applications, 2020, vol. 32, p. 12801-12812.
    Resumen
    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.
    Materias Unesco
    2509 Meteorología
    2509.01 Meteorología agrícola
    1203.04 Inteligencia Artificial
    Palabras Clave
    Machine learning
    Neural networks
    Temperature
    Relative humidity
    Evapotranspiration
    ISSN
    0941-0643
    Revisión por pares
    SI
    DOI
    10.1007/s00521-020-04727-8
    Version del Editor
    https://link.springer.com/article/10.1007/s00521-020-04727-8
    Propietario de los Derechos
    © Springer
    Idioma
    eng
    URI
    https://uvadoc.uva.es/handle/10324/70823
    Tipo de versión
    info:eu-repo/semantics/publishedVersion
    Derechos
    restrictedAccess
    Aparece en las colecciones
    • DEP42 - Artículos de revista [291]
    Mostrar el registro completo del ítem
    Ficheros en el ítem
    Nombre:
    Virtual_weather_stations_for_meteorological_data_estimations.pdfEmbargado hasta: 9999-09-09
    Tamaño:
    792.8Kb
    Formato:
    Adobe PDF
    Visualizar/Abrir

    Universidad de Valladolid

    Powered by MIT's. DSpace software, Version 5.10