• DocumentCode
    2030005
  • Title

    Predictive capacity of meteorological data: Will it rain tomorrow?

  • Author

    Ahmed, Bilal

  • Author_Institution
    Dept. of Comput. & Inf. Syst., Univ. of Melbourne, Melbourne, VIC, Australia
  • fYear
    2015
  • fDate
    28-30 July 2015
  • Firstpage
    199
  • Lastpage
    205
  • Abstract
    With the availability of high precision digital sensors and cheap storage medium, it is not uncommon to find large amounts of data collected on almost all measurable attributes, both in nature and man-made habitats. Weather in particular has been an area of keen interest for researchers to develop more accurate and reliable prediction models. This paper presents a set of experiments which involve the use of prevalent machine learning techniques to build models to predict the day of the week given the weather data for that particular day i.e. temperature, wind, rain etc., and test their reliability across four cities in Australia {Brisbane, Adelaide, Perth, Hobart}. The results provide a comparison of accuracy of these machine learning techniques and their reliability to predict the day of the week by analysing the weather data. We then apply the models to predict weather conditions based on the available data.
  • Keywords
    data analysis; geophysics computing; learning (artificial intelligence); weather forecasting; Adelaide; Australia; Brisbane; Hobart; Perth; machine learning techniques; meteorological data; weather data analysis; weather prediction models; Accuracy; Classification algorithms; Rain; Sun; Wind forecasting; Big Data; Data Mining; Data Modelling; IB1; J48; Naïve Bayes; Predictive Analytics; Random Forests; Weka;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Science and Information Conference (SAI), 2015
  • Conference_Location
    London
  • Type

    conf

  • DOI
    10.1109/SAI.2015.7237145
  • Filename
    7237145