首页|New Findings from Anglia Ruskin University in the Area of Artificial Intelligenc e Described [Using Explainable Artificial Intelligence (XAI) to Predict the Influence of Weather on the Thermal Soaring Capabilities of Sailp lanes for Smart City ...]
New Findings from Anglia Ruskin University in the Area of Artificial Intelligenc e Described [Using Explainable Artificial Intelligence (XAI) to Predict the Influence of Weather on the Thermal Soaring Capabilities of Sailp lanes for Smart City ...]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on artificial intell igence have been published. According to news originating from Cambridge, United Kingdom, by NewsRx correspondents, research stated, "Drones, also known as unma nned aerial vehicles, could potentially be a key part of future smart cities by aiding traffic management, infrastructure inspection and maybe even last mile de livery." Our news editors obtained a quote from the research from Anglia Ruskin Universit y: "This paper contributes to the research on managing a fleet of soaring aircra ft by gaining an understanding of the influence of the weather on soaring capabi lities. 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). A random forest regressor was trained to predict the flight durati on 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 exPlan ations), a form of explainable artificial intelligence (AI), was used to underst and the predictions given by the models. 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 predic t soaring potential." According to the news editors, the research concluded: "However, the key conclus ion of this study is the importance of combining human knowledge with machine le arning to devise a holistic explanation of a machine learning model and to avoid misinterpretations."
Anglia Ruskin UniversityCambridgeUni ted KingdomEuropeArtificial IntelligenceCyborgsEmerging TechnologiesMa chine Learning
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