Optimal Prediction of Weather Condition Based on C4.5 Classification Technique
DOI:
https://doi.org/10.26438/ijcse/v6i10.621627Keywords:
C4.5, Temperature, Cloudcover, Vapor pressure, Relative humidity, Confusion matrixAbstract
In this world many of task is very challenged for researchers. By the way the accurate weather prediction is one of the disputes for the meteorologist. So this paper focuses the weather prediction for an implementing the classification technique of C4.5 Classification technique. This technique can be analyzed for the performance and accuracy of weather condition. Also this decision tree algorithm can be applied in weather prediction parameter of training data under the various regions. Such as, Tamil Nadu, Andra Pradesh, Gujarat and Odhisa states are taken from India for this research work. These states are mainly focus for the purpose of different monsoon seasons and climates vary from actual period of time. Finally, weather condition can be predicted on various monsoons seasonally on the respective class label of climate range
References
[1] Olaiya, Folorunsho, and Adesesan Barnabas Adeyemo. "Application of data mining techniques in weather prediction and climate change studies."International Journal of Information Engineering and Electronic Business (IJIEEB) 4.1 (2012): 51.
[2] Kantardzic, Mehmed. “Data mining: concepts, models, methods, and algorithms.” John Wiley & Sons, 2011.
[3] Berkhin, Pavel. "A survey of clustering data mining techniques." Groupingmultidimensional data.Springer Berlin Heidelberg, 2006.25-71.
[4] Allen, Richard G. "Crop evapotranspiration-Guidelines for computing crop water requirements-FAO Irrigation and drainage paper 56." FAO, Rome 300 (1998): 6541.
[5] Lawrence, Mark G. "The relationship between relative humidity and the dewpoint temperature in moist air: A simple conversion and applications." Bulletin of the American Meteorological Society 86.2 (2005): 225-233.
[6] Howarth, Edgar, and Michael S. Hoffman. "A multidimensional approach to the relationship between mood and weather." British Journal of Psychology 75.1 (1984): 15-23.
[7] Pasanen, A-L., et al. "Laboratory studies on the relationship between fungal growth and atmospheric temperature and humidity." Environment International17.4 (1991): 225-228.
[8] Burk, R. L., and M. Stuiver. "Oxygen isotope ratios in trees reflect mean annual temperature and humidity." Science 211.4489 (1981): 1417-1419.
[9] Zhang, Guang Jun, and Michael J. Mcphaden. "The relationship between sea surface temperature and latent heat flux in the equatorial Pacific." Journal of climate 8.3 (1995): 589-605.
[10] Campbell, A., et al. "Temperature requirements of some aphids and their parasites." Journal of applied ecology (1974): 431-438.
[11] Thornton, Peter E., Steven W. Running, and Michael A. White. "Generating surfaces of daily meteorological variables over large regions of complex terrain."Journal of Hydrology 190.3 (1997): 214-251.
[12] Solomon, M. E. "Control of humidity with potassium hydroxide, sulphuric acid, or other solutions." Bulletin of Entomological Research 42.03 (1951): 543-554
[13] Zhu, Xingquan, and Ian Davidson, eds. “Knowledge Discovery and Data Mining”. Challenges and Realities. Igi Global, 2007.
[14] Manish Verma, MaulySrivastava, NehaChack, Atul Kumar Diswar and Nidhi Gupta, “A Comparative Study of Various Clustering Algorithms in Data Mining”, International Journal of Engineering Research and Applications (IJERA) Vol. 2, Issue 3, May-Jun 2012, pp.1379
[15] Hall, Mark, et al. "The WEKA data mining software: an update." ACM SIGKDD Explorations Newsletter 11.1 (2009): 10-18.
[16] T.F. Gonzales. “Clustering to minimize the maximum inter cluster distance”. Theoretical Computer Science,1985,38(2-3):293-306.
[17] Kannan, M., S. Prabhakaran, and P. Ramachandran. "Rainfall forecasting using data mining technique."(2010)
[18] Arun K Pujari, “Data mining techniques”, University Press (India).2013
[19] Jiawei Han Mischeline Kamber, “Data Mining: Concepts and Techniques”, Morgan Kaufmann Publisher an imprint of Elsevier. 2006
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