Machine learning-based inflight food waste prediction for sustainable aviation

OpenAlex İngilizce

The study delves into the utilization of machine learning to predict and reduce inflight food waste, improving sustainability in aviation logistics. Inflight food waste, a major environmental problem, is determined by passenger choices, flight parameters, and catering services. The research presents two efficient machine learning algorithms, that are, Multiple Linear and Random Forest Regression to perform food waste prediction during the flights. The models are trained using a synthetically created dataset of 10,000 records and 15 features, which include factors such as meal type, waste weight, and passenger number. The study undertakes considerable feature engineering, including the development of new features such as "Waste per Passenger" and "Meal Efficiency" to increase forecast accuracy. A correlation analysis is also used to determine the most influential characteristics. The models' performance is assessed in a Python-based computational environment, with MLR concentrating on linear links between food waste and predictors and RFR on non-linear interactions. The results show that both models can effectively forecast inflight food waste, with RFR being more adaptable to complicated patterns. The research concludes with recommendations for airline managers to apply data-driven waste reduction techniques that correspond with overall sustainability goals in aviation logistics. The models created are a useful tool for optimizing inflight food, lowering environmental impact, and contributing to the industry's sustainability initiatives.