Using Explainable Artificial Intelligence (XAI) to Predict the Influence of Weather on the Thermal Soaring Capabilities of Sailplanes for Smart City Applications

Abstract

Background: Drones, also known as unmanned aerial vehicles, could potentially be a key part of future smart cities by aiding traffic management, infrastructure inspection and maybe even last mile delivery. This paper contributes to the research on managing a fleet of soaring aircraft by gaining an understanding of the influence of the weather on soaring capabilities. To do so, machine learning algorithms were trained on flight data, which was recorded in the UK over the past ten years at selected gliding clubs (i.e., sailplanes). Methods: A random forest regressor was trained to predict the flight duration and a random forest (RF) classifier was used to predict whether at least one flight on a given day managed to soar in thermals. SHAP (SHapley Additive exPlanations), a form of explainable artificial intelligence (AI), was used to understand the predictions given by the models. Results: The best RF have a mean absolute error of 5.7 min (flight duration) and an accuracy of 81.2% (probability of soaring in a thermal on a given day). The explanations derived from SHAP are in line with the common knowledge about the effect of weather systems to predict soaring potential. However, the key conclusion of this study is the importance of combining human knowledge with machine learning to devise a holistic explanation of a machine learning model and to avoid misinterpretations.