AI-DRIVEN DECISION SUPPORT SYSTEM FOR OPTIMIZED MUNICIPAL SOLID WASTE MANAGEMENT: A MACHINE LEARNING AND ROUTE OPTIMIZATION FRAMEWORK FOR SMART CITIES

Abstract

The task to manage the municipal solid waste (MSW) in the urban localities specifically in the cities which are growing faster is very challenging as the city administration need to cope up with the pace at which today’s cities are growing which is almost impossible without incorporating the latest technologies like Artificial Intelligence (AI). The static waste collection system which is being used conventionally currently facing issues like inadequate allocation of the resources required which in turn raises the overall costs of the operations and ultimately harm the environment, too. To overcome this, the present study introduces an innovative approach of combining Decision Support System (DSS) with machine learning driven waste generation forecasts. This study attempts to optimize the vehicle routing to improve the MSW collection efficacy in the city of Surat, India. The framework proposed here combines Long Short-Term Memory (LSTM) neural networks for MSW prediction with Capacitated Vehicle Routing Problem (CVRP) algorithms for the optimization of routes. We have developed our LSTM model using the dataset of past 16 years (2009-2024) obtained from Surat Municipal Corporation which achieved considerable forecasting accuracy with RMSE of 0.1095 and R² value of 0.93. The integrated DSS demonstrated considerable benefits in operations, cutting the overall collecting distance by 50.31% and the use of vehicles by 24% compared to traditional static routing methods. The technology uses predictive analytics to dynamically allocate the required resources helping the waste management authorities make decisions in real time. This study enhances sustainable urban development by offering a data-driven methodology that corresponds with the objectives of the Smart Cities Mission and Sustainable Development Goals (SDGs) 11, 12, and 13. The suggested architecture provides scalable methods for implementing a circular economy and making municipal waste management systems more environmentally friendly.