The prediction of airplane landing gravity using case-based reasoning

Most flight accidents occurring worldwide are due to the lack of an appropriate approach in the landing phase. Recent data mining developments have provided aviation insights into the landing phase. Among these methods, case-based reasoning is a potential approach that can be applied for predicting landing gravity. This research proposes a novel model construction method that consists of non-linear similarity functions and dynamic weighting mechanisms to optimize the prediction accuracy. We illustrate our approach with the data obtained directly from flight data recorders of Boeing 747-400 airplanes. This experiment also shows that non-linear similarity functions demonstrate better prediction accuracy over the results from other approaches. Significance: Flight safety problems are very important and rarely has work been done on landing gravity prediction. Case-based reasoning is a feasible approach that can be used for predicting landing gravity.