Precipitation prediction for south west monsoon over karnataka using supervised learning technique

Abstract

Weather 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.