Modeling Rainfall Prediction Using Data Mining Method: A Bayesian Approach

Abstract

Weather forecasting has been one of the most scientifically and technologically challenging problem around the world. Weather data is one of the meteorological data that is rich with important information, which can be used for weather prediction We extract knowledge from weather historical data collected from Indian Meteorological Department (IMD) Pune. From the collected weather data comprising of 36 attributes, only 7 attributes are most relevant to rainfall prediction. We made data preprocessing and data transformation on raw weather data set, so that it shall be possible to work on Bayesian, the data mining, prediction model used for rainfall prediction. The model is trained using the training data set and has been tested for accuracy on available test data. The meteorological centers uses high performance computing and supercomputing power to run weather prediction model. To address the issue of compute intensive rainfall prediction model, we proposed and implemented data intensive model using data mining technique. Our model works with good accuracy and takes moderate compute resources to predict the rainfall. We have used Bayesian approach to prove our model for rainfall prediction, and found to be working well with good accuracy.