Association Rule Mining For South West Monsoon Rainfall Prediction and Estimation over Mumbai Station
R. Varahasamy1, S. Meganathan2, Durga Karthik3
1R. Varahasamy, Department of Computer Science, Srinivasa Ramanujan Centre, SASTRA, Kumbakonam, Tamil Nadu, India.
2S. Meganathan Department of Computer Science, Srinivasa Ramanujan Centre, SASTRA, Kumbakonam, Tamil Nadu, India.
3Durga Karthik, Department of Computer Science, Srinivasa Ramanujan Centre, SASTRA, Kumbakonam, Tamil Nadu, India.
Manuscript received on 20 August 2019. | Revised Manuscript received on 26 August 2019. | Manuscript published on 30 September 2019. | PP: 4490-4493 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6809098319/2019©BEIESP | DOI: 10.35940/ijrte.C6809.098319
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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: Rainfall is important for agricultural yield and hence early prediction is required. It has a vital role in the improving the economy of a country. Accurate and timely weather prediction for rainfall forecasting has been one of the most challenging problems around the world as it changes the physical characteristics of the hydrologic system. Rainfall prediction model involves observation of weather data, deriving knowledge from it and implementing using computer models. The proposed work observed rainfall during south-west monsoon months of Mumbai (Latitude 19.0760°N / Longitude 72.8777°E) city. Predictive Apriori Algorithm was used to derive association rules for spot prediction, 24 hours ahead prediction and 48 hours ahead prediction, also to estimate a no rain day, moderate rain day and heavy rain day.
Keywords— Southwest Monsoon, Rainfall, Prediction, Estimation, Predictive Apriori Algorithm
Scope of the Article: Regression and Prediction