An Automated Machine Learning Approach for Smart Waste Management Systems

This paper presents the use of automated machine learning for solving a practical problem of a real-life Smart Waste Management system. In particular, the focus of the paper is on the problem of detection (i.e., binary classification) of emptying of a recycling container using sensor measurements. Numerous data-driven methods for solving the problem are investigated in a realistic setting where most of the events are not actual emptying. The investigated methods include the existing manually engineered model and its modification as well as conventional machines learning algorithms. The use of machine learning allows improving the classification accuracy and recall of the existing manually engineered model from <inline-formula><tex-math notation="LaTeX">$86.8\%$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$47.9\%$</tex-math></inline-formula> to <inline-formula><tex-math notation="LaTeX">$99.1\%$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$98.2\%$</tex-math></inline-formula>, respectively, when using the best performing solution. This solution uses a Random Forest classifier on a set of features based on the filling level at different given time spans. Finally, compared to the baseline existing manually engineered model, the best performing solution also improves the quality of forecasts for emptying time of recycling containers.