Weather Prediction System Using KNN Classification Algorithm



Abstract Views
659

Downloads
1159

Citations

Share

##plugins.themes.bootstrap3.article.sidebar##

Submitted Dec 28, 2021
Published Feb 10, 2022
10.24018/compute.2022.2.1.44

Abstract viewed = 659 times

Table of Contents

##plugins.themes.bootstrap3.article.main##

Data Mining is a technology that facilitates extracting relevant and which have factors in common from the set of data. It is the process of analysis data from different perspectives and discovering problems, patterns, and correlations in data sets that are useful for predicting outcomes that help you make a correct decision.  Weather Prediction is a field of meteorology that is created by collecting dynamic data related to the current state of the weather such as temperature, humidity, rainfall, wind. In this paper, we designed a system using a classification method by k-Nearest Neighbors algorithm for predict whether through previous data to determine the expected temperature and humidity the prediction results were compared with real results, the comparison was good and acceptable.

Keywords: Correlations, classification, data Mining, discovering problems, k-Nearest neighbors algorithm, patterns

References

  1. Introduction to Data Mining and Knowledge Discovery. Two Crowds Corporation, [Internet] 2009. Available from: http://www.twocrows.com/introdm.pdf
     Google Scholar
  2. Olaiya F, Barnabas A. Applications of Data Mining Techniques in weather prediction and climate change studies. MECS. 2012.
     Google Scholar
  3. Shu-Hsien L, Pei-Hui C, Pei-Yuan H. Data mining techniques and applications – A decade review from 2000 to 2011. 2012
     Google Scholar
  4. Folorunsho O. Application of Data Mining Techniques in Weather Prediction and Climate Change Studies. I.J. Information Engineering and Electronic Business. 2012; 1: 51-59.
     Google Scholar
  5. Dickey DA, N. Carolina State U., Raleigh NC. Introduction to Predictive Modeling with Examples. Statistics and Data Analysis. 2012
     Google Scholar
  6. Kapoor P, Singh Bedi S. Weather Forecasting Using Sliding Window Algorithm. ISRN Signal Processing. 2013.
     Google Scholar
  7. Jayavani K. Statistical classification in machine intellegent. ISR Journals and Publications. 2014;1.
     Google Scholar
  8. Fan C, Xiao F, Wang S. Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques. 2014.
     Google Scholar
  9. Gupta D, Ghose U. A Comparative Study of Classification Algorithms for Forecasting Rainfall. IEEE. 2015.
     Google Scholar
  10. Hayati M, Mohebi Z. Temperature Forecasting Based on Neural Network Approch. World Applied Science Journal. 2007; 2(6): 613-620.
     Google Scholar
  11. Dunne S, Ghosh B. Weather adaptive traffic prediction using neuro-wavelet models. WCTR. 2013.
     Google Scholar
  12. Wang J, Su X. An improved K-Means clustering algorithm. IEEE. 2014.
     Google Scholar
  13. Hand, Manilla, Smyth, Principles of Data Mining. The MIT Press. 2001
     Google Scholar
  14. Han J, Kamber M, Pei J. Data Mining: Concepts and Techniques. 2012.
     Google Scholar
  15. Witten IH, Frank E. Data Mining Practical Machine Learning Tools and Techniques. 2015.
     Google Scholar