Paper Info
Title
Predictive capacity of meteorological data: Will it rain tomorrow?
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.
Year
DOI
Venue
2014
10.1109/SAI.2015.7237145
2015 Science and Information Conference (SAI)
Keywords
Field
DocType
Predictive Analytics,Data Mining,Big Data,Data Modelling,Naïve Bayes,Weka,Random Forests,J48,IB1
Data mining,Data modeling,Names of the days of the week,Naive Bayes classifier,Digital sensors,Computer science,Predictive analytics,Predictive modelling,Random forest,Big data
Journal
Volume
Citations 
PageRank 
abs/1409.5079
0
0.34
References 
Authors
0
1
Name
Order
Citations
PageRank
Bilal A. Ahmed16117.20