Artificial Neural Networks’ Application in Weather Forecasting – Using RapidMiner

Weather forecasting is a crucial phenomenon in today’s world. Though weather prediction is completely automated, with the help of tools like Weather Research & Forecasting (WRF), Advanced Research WRF (ARW), Weather Processing System (WPS), it’s a ever challenging and a topic of interest because prediction is not an accurate always. Weather forecasting is a continuous, high dimensional, dynamic and complicated process because it involves many entities of the atmosphere. The parameters required to predict the weather are enormously complex such that there is uncertainty in prediction even for a short period. The property of artificial neural networks is that they not only analyze the historical data, but also learn from it for future predictions make them suitable / ideal for weather forecasting. Weather prediction can be simplified by using the artificial neural networks (ANN) with back propagation for supervised learning using the data collected at a particular station at a specified period. After training the model, they are used to predict the weather conditions. As an experimental method, the model is made known to predict the values as unknown values. The output is promising and motivates us to work more towards this goal. KeywordsData Mining, Predictive analytics, ANN, Regression Techniques, Machine Learning techniques