An ANN-based Decision Support Tool for the Sustainable Performance Prediction of the Waste Management Systems

Assessment and prediction of environmental and economic performance of waste management systems should be jointly considered in a sustainable planning of such a system. Complexity of the economic and the environmental evaluation of waste management system limits the adoption of analytical tools by policy makers. Consistently, the authors propose an Artificial Neural Network (ANN)-based decision support tool for the prediction of the optimized sustainable performance of an integrated waste management system. It stands as a userfriendly dashboard designed for a local-policy maker who seeks to have insights into potential effects of different waste management policy mainly on greenhouse gases (GHGs) emissions and monetary savings. Indeed, starting from both demographic and urban fabric features, the tool predicts the most suitable collection configuration, the flows managed as well as the amount of CO2eq emitted/avoided and relative financial flows. The ANN developed is trained through data deriving from an analytical optimization model. The modeling code is developed by Alyuda NeuroIntelligenceTM. Results show the good reliability of the ANN in the prediction of the optimized sustainable performance for the waste management system