Title :
Predictive capacity of meteorological data: Will it rain tomorrow?
Author_Institution :
Dept. of Comput. & Inf. Syst., Univ. of Melbourne, Melbourne, VIC, Australia
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;
Conference_Titel :
Science and Information Conference (SAI), 2015
Conference_Location :
London
DOI :
10.1109/SAI.2015.7237145