AI-Enabled Smart City Waste Management System

Abstract

Waste management consists of various activities such as recycling, reuse, reorganization of waste and implementation of other strategies to reduce wastes in the cities, which is caused due to population, industrialization and urbanization. Industrial growth and urbanization produce large quantities of waste. Monitoring and managing of this waste are challenging tasks. Smart cities' transformation towards green economy needs an engagement of industrial sectors, private sectors and government sectors along with a large section of people, including policy-makers, entrepreneurs, academics and international organizations irrespective of sector-wise categorizations to derive better waste management practices and approaches. The smart city facilitates seamless integration and sharing of data, use of smart technologies, incorporation of data analysis for decision-making, effective and efficient operation of the city, thereby helping to improve the life of its citizens. A smart city also enables the usage of smart technologies to provide sustainable waste management practices, auto-identification of waste, analysis of sources of waste and the infrastructure needed for waste management, along with analysis of relevant regulations, policies, and legal and institutional frameworks for automatically enhancing environmental qualities. Hence a smart city integrated with a key waste management infrastructure will ensure the systematic development of an integrated waste management system and cater to the needs of smart cities in order to handle waste. The research highlights smart city approaches towards key aspects of waste management such as waste generation, waste collection, waste transportation, waste recycling, waste disposal and waste removal using smart technologies. This research further examines and analyses smart city waste management resources, smart city waste management needs and smart infrastructure requirements for handling the waste using descriptive, prescriptive and predictive features of data analysis. The research also evaluates technology integration such as machine learning within a waste management ecosystem and helps to predict various waste management requirement predictions, including infrastructure predictions, resource predictions and waste capacity predictions. Finally, the research evaluates artificial intelligence infusion within the waste management framework to derive the process of decision-making and recommends key requirements of smart city waste management implementations.