We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
Reseach Article

A Weather Forecasting Model using the Data Mining Technique

by Rohit Kumar Yadav, Ravi Khatri
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 139 - Number 14
Year of Publication: 2016
Authors: Rohit Kumar Yadav, Ravi Khatri
10.5120/ijca2016908900

Rohit Kumar Yadav, Ravi Khatri . A Weather Forecasting Model using the Data Mining Technique. International Journal of Computer Applications. 139, 14 ( April 2016), 4-12. DOI=10.5120/ijca2016908900

@article{ 10.5120/ijca2016908900,
author = { Rohit Kumar Yadav, Ravi Khatri },
title = { A Weather Forecasting Model using the Data Mining Technique },
journal = { International Journal of Computer Applications },
issue_date = { April 2016 },
volume = { 139 },
number = { 14 },
month = { April },
year = { 2016 },
issn = { 0975-8887 },
pages = { 4-12 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume139/number14/24665-2016908900/ },
doi = { 10.5120/ijca2016908900 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:40:55.391942+05:30
%A Rohit Kumar Yadav
%A Ravi Khatri
%T A Weather Forecasting Model using the Data Mining Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 139
%N 14
%P 4-12
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The weather conditions are changing continuously and the entire world is suffers from the changing Clemet and their side effects. Therefore pattern on changing weather conditions are required to observe. With this aim the proposed work is intended to investigate about the weather condition pattern and their forecasting model. On the other hand data mining technique enables us to analyse the data and extract the valuable patterns from the data. Therefore in order to understand fluctuating patterns of the weather conditions the data mining based predictive model is reported in this work. The proposed data model analyse the historical weather data and identify the significant on the data. These identified patterns from the historical data enable us to approximate the upcoming weather conditions and their outcomes. To design and develop such an accurate data model a number of techniques are reviewed and most promising approaches are collected. Thus the proposed data model incorporates the Hidden Markov Model for prediction and for extraction of the weather condition observations the K-means clustering is used. For predicting the new or upcoming conditions the system need to accept the current scenarios of weather conditions. The implementation of the proposed technique is performed on the JAVA technology. Additionally for justification of the proposed model the comparative study with the traditional ID3 algorithm is used. To compare both the techniques the accuracy, error rate and the time and space complexity is estimated as the performance parameters. According to the obtained results the performance of the proposed technique is found enhanced as compared to available ID3 based technique.

References
  1. A. Ramos-Soto, A. Bugar´in, S. Barro, and J. Taboada, “Linguistic Descriptions for Automatic Generation of Textual Short-Term Weather Forecasts on Real Prediction Data”, 1063-6706 (c) 2013 IEEE
  2. Data Mining: What is Data Mining? http://www.anderson.ucla.edu/faculty/jason. frand/teacher/technologies/palace/datamining.htm
  3. Data Mining - Applications & Trends, http://www.tutorialspoint.com/data_mining/dm_applications_trends.htm
  4. Mahak Chowdhary, Shrutika Suri and Mansi Bhutani, “Comparative Study of Intrusion Detection System”, 2014, IJCSE All Rights Reserved, Volume-2, Issue-4
  5. Mrs. Pradnya Muley, Dr. Anniruddha Joshi, “Application of Data Mining Techniques for Customer Segmentation in Real Time Business Intelligence”, International Journal of Innovative Research in Advanced Engineering (IJIRAE) ISSN: 2349-2163, Issue 4, Volume 2 (April 2015)
  6. Ghazaleh Khodabandelou, Charlotte Hug, Rebecca Deneckere, Camille Salinesi, “Supervised vs. Unsupervised Learning for Intentional Process Model Discovery”, Business Process Modeling, Development, and Support (BPMDS), Jun 2014, Thessalonique, Greece. pp.1-15, 2014
  7. Importance of Predictive Analytics in Business, http://www.orchestrate.com/blog/importance-of-predictive-analytics-in-business.
  8. David A. Dickey, N. Carolina State U., Raleigh, NC, “Introduction to Predictive Modeling with Examples”, Statistics and Data Analysis, SAS Global Forum 2012
  9. Hand, Manilla, & Smyth, “Descriptive Modeling”, http://www.stat.columbia.edu/~madigan/DM08/descriptive.ppt.pdf
  10. K.Jayavani, “STATISTICAL CLASSIFICATION IN MACHINE INTELLEGENT”, ISRJournals and Publications, Volume: 1 Issue: 1 18-Jul-2014, I
  11. Folorunsho Olaiya, “Application of Data Mining Techniques in Weather Prediction and Climate Change Studies”, I.J. Information Engineering and Electronic Business, 2012, 1, 51-59
  12. M.S.B. PhridviRaja, C.V. GuruRaob, “Data mining – past, present and future – a typical survey on data streams”, The 7th International Conference Interdisciplinarity in Engineering (INTER-ENG 2013)
  13. Wei Fan, Albert Bifet, “Mining Big Data: Current Status, and Forecast to the Future”, SIGKDD Explorations Volume 14, Issue 2
  14. Andrew Kusiak, Xiupeng Wei, Anoop Prakash Verma, and Evan Roz, “Modeling and Prediction of Rainfall Using Radar Reflectivity Data: A Data-Mining Approach”, IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 51, NO. 4, APRIL 2013
  15. Cheng Fan, Fu Xiao, Shengwei Wang, “Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques”, 2014 Elsevier Ltd. All rights reserved.
  16. Shu-Hsien Liao, Pei-Hui Chu, Pei-Yuan Hsiao, “Data mining techniques and applications – A decade review from 2000 to 2011”, 2012 Elsevier Ltd. All rights reserved.
  17. Wu He, “Examining students’ online interaction in a live video streaming environment using data mining and text mining”, 2012 Elsevier Ltd. All rights reserved.
  18. Meik Schlechtingen, Ilmar Ferreira Santos, and Sofiane Achiche, “Using Data-Mining Approaches for Wind Turbine Power Curve Monitoring: A Comparative Study”, IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 4, NO. 3, JULY 2013
  19. Andrew Kusiak, and Anoop Verma, “A Data-Mining Approach to Monitoring Wind Turbines”, IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, VOL. 3, NO. 1, JANUARY 2012
  20. Stephen Dunne, Bidisha Ghosh, “WEATHER ADAPTIVE TRAFFIC PREDICTION USING NEURO-WAVELET MODELS”, WCTR 2013
  21. Juntao Wang, Xiaolong Su, “An improved K-Means clustering algorithm”, 978-1-61284-486-2/111$26.00 ©2011 IEEE
  22. Shweta Jaiswal, Atish Mishra, Praveen Bhanodia,” Grid Host Load Prediction Using GridSim Simulation and Hidden Markov Model”, International Journal of Emerging Technology and Advanced Engineering (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 7, July 2014)
Index Terms

Computer Science
Information Sciences

Keywords

Data mining classification supervised learning implementation performance study.