Precipitation Prediction for South West Monsoon Over Karnataka using Supervised Learning Technique
S. Meganathan1, Michael Raj.T.F.2, RajaKumar.B3, Raghuraman.K4, N. Rajesh Kumar5
1S. Meganathan, Faculty of Computer Science and Engineering, SASTRA Deemed University, Kumbakonam, Tamil Nadu, India.
2Michael Raj.T.F, , Faculty of Computer Science and Engineering, SASTRA Deemed University, Kumbakonam, Tamil Nadu, India.
3RajaKumar.B, Faculty of Computer Science and Engineering, SASTRA Deemed University, Kumbakonam, Tamil Nadu, India.
4Raghuraman.K, Faculty of Computer Science and Engineering, SASTRA Deemed University, Kumbakonam, Tamil Nadu, India.
5N. Rajesh Kumar, Faculty of Computer Science and Engineering, SASTRA Deemed University, Kumbakonam, Tamil Nadu, India.
Manuscript received on 06 August 2019. | Revised Manuscript received on 12 August 2019. | Manuscript published on 30 September 2019. | PP: 4450-4454 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6800098319/2019©BEIESP | DOI: 10.35940/ijrte.C6800.098319
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: TWeather forecasting is a major field of study in the area of Meteorology. Data Scientists, meteorologists and weather forecasters are implementing the experimentation of weather forecasting base on numerical and statistical methods. Traditional models used the fluid and thermal dynamic strategies for grid-point time series prediction based on few inherited constraints, such as the adoption of incomplete boundary rules, model assumptions and numerical instabilities. The nominated work is focused on finding the south west monsoon months’ precipitation patterns over the specific stations of Karnataka State. A multi-dimensional data framework for climate database with implementation online based data analysis has been developed. This works is carried out on the basis of monsoons that have prevailed during a year for the past 10 years. The proposed model emphasis the implementation of the association rules which has been extracted by the supervised classifier approach of data mining algorithms. The data mining technique of association rules emphasis the occurrence of the precipitation and will be helpful to take decisions in advance to the day to day operations in business, agriculture, water management and etc.
Keywords: Weather Forecasting, Naïve Bayes Classification, Data mining, Supervised Learning
Scope of the Article: Classification