CP-KNN: Seasonal to Inter – annual Climate Prediction using Data Mining KNN technique.
The impact of seasonal to inter – annual climate prediction on the society, business, agriculture and almost all aspect of human life enforce the scientist to give proper attention to the matter. The last few years show tremendous achievements in this field. All systems, techniques developed so far, use the Sea Surface Temperature (SST) as a main factor among other seasonal climatic attributes. The statistical and mathematical models are then used for further climate predictions. In this paper we are going to develop a system that uses the historical weather data (Rain, Wind speed, Dew point, temperature etc) of a region and applies data mining algorithm “k–nearest neighbor (KNN)” for classification of these historical data into specific time span, the k nearest time spans (K nearest Neighbors) are then taken to predict the weather a month in advance. The experiments show that the system generates accurate result within reasonable time for a month in advance.
Objectives
The motivation behind the research is to extend the application of Data Mining to the field of meteorology, oceanography and climatology. This will open a new era in the field of Data Mining and climate prediction. The main Objectives are
• Utilization of historical data
• Data Cleansing to convert the data in uniform format
• Concrete Model for Climate Prediction using Data Mining
• Prediction using Numerical Data
• Improvements in performance and accuracy of Climate Prediction
Hypothesis (Problem Solution)
The huge amount of climatic data is available for years. The data should be brought into a uniform format. If the data is noisy it can be cleanse using any of...
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Muhammad Abrar Lecturer in Agricultural University Peshawar Pakistan
This is synopsis for MS Degree that are recently approved by the concern Board of Studies
National Weather Service. National Oceanic and Atmospheric Association, 5 Jan. 2010. Web. 2 Feb. 2010. .
Didactic Reliance on Technology: Analysis of “August 2026: There Will Come Soft Rain” and “The Machine Stops”
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Wang, Risheng and Bin Wang. "Phase Space Representation and Characteristics of El Nino-La Nina". Journal of Atmospheric Sciences: vol. 57, No. 19, pp. 3315-3333.
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McMean, G. (2007, June). Artic climate impact assessment (C. Symon, Ed.). New York, NY: Cambridge Univercity Press. http://www.acia.uaf.edu/pages/scientific.html
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Web. The Web. The Web. March 26, 2014 Ncdc.noaa.gov. Web.