Open AccessAssociation Rule Mining for South West Monsoon Rainfall Prediction and Estimation over Mumbai Station
Author(s) -
R. Varahasamy*,
S. Meganathan,
Durga Karthik
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c6809.098319
Subject(s) - longitude , meteorology , association rule learning , weather prediction , climatology , latitude , monsoon , environmental science , estimation , weather forecasting , predictive modelling , computer science , geography , data mining , machine learning , engineering , geology , geodesy , systems engineering
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.