RTWPCAMARM: A dynamic real time weather prediction system with 8 neighborhood hybrid cellular automata and modified association rule mining

Nowadays predicting the weather is the most vital problem in the recent centaury. It is very difficult to predict the temperature variations, wind speed and amount of rainfall at any given location. In this research we propose a dynamic real time weather prediction system with non uniform cellular automata that use eight neighborhood to predict the normal and abnormal climatic variations. This prediction method is based on dynamic NUH-MAR based climatologically methods, united with discovery of knowledge. This research work is mostly helpful to the farmers and guides the disasters management department to take remedial actions in case of drought. We have used OGD and IMD datasets pertaining to East Godavari, West Godavari and Krishna districts where majority of the people dependent on cultivation. Extensive experimental results are performed to estimate the variations in temperature, speed of wind and amount of rainfall in these locations. We have compared our results with the standard and best techniques reported in the literature survey. The results shows the novelty and strength of our algorithm to predict temperature abnormalities with an accuracy of 92%, wind speed with 91.2% and amount of rainfall with 96%.