Smart Wastage in Python

Abstract:

This article 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 article is on the problem of detection (i.e., binary classification) of an emptying of a recycling container using sensor measurements. Numerous data-driven methods for solving the problem were investigated in a realistic setting where most of the events were not actual emptyings. The investigated methods included the existing manually engineered model and its modification as well as conventional machines learning algorithms. The use of machine learning allowed improving the classification accuracy and recall of the existing manually engineered model from 86:8 % and 47:9 % to 99:1 % and 98:2 % respectively when using the best performing solution. This solution used 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 improved the quality of forecasts for emptying time of recycling containers.