Flight Data of Airplane for Wind Forecasting

Understanding and predicting weather behavior is vital for informing pilots about changing flight conditions. This paper presents a new approach towards forecasting one component of weather information, wind speed, from data captured by airplanes in flight. We compare two datasets for prediction suitability, and a collinearity analysis between these datasets reveals a better model performance with smaller test error with one of them. We then apply machine learning and a genetic algorithm to process this data further and arrive at a competitive error rate. Finally, we create an offline software for wind prediction using the best performing classifier.