AI ASSISTED WASTE MANAGEMENT SYSTEMS: A COMPREHENSIVE REVIEW OF TECHNOLOGIES, CASE STUDIES, AND SUSTAINABILITY IMPACTS

Metilda Stella Rani G.*, Mythili S.,  Durga Sri R.

ABSTRACT

The effects of urba
nization, population increase, and industrialization have also contributed to massive production
of complex wastes that are overstressing the conventional methods used in the management of such wastes. The
conventional methods for waste management, which i nvolve manual processes dependent on time, may not cope
well with issues regarding inadequacies in resource management, overflowed bins, contaminated water as well as
environment, and health aspects. This report highlights the latest innovations in using A I for the support of waste
management in the context of real time generation and cleanup of wastes, use of predictive analytics, design of
sensor networks and implementation of the internet of things, and intelligent automation. The implementation of
senso rs in AI helps in real time generation and cleanup of wastes through ultrasonic sensors, load cell sensors, and
GPS tracking devices. These are in turn used again for the implementation of AI algorithms for cleanup route
optimizations in the vehicles, wast ages in cleanup transports, preventing overflows, and operation maximization.
The predictive analytics module, developed through machine learning algorithms, helps in precise forecasting of
future generation of wastes based on historical inputs and season changes. The literature also offers evaluation
analysis concerning the application of Deep Learning as well as Computer Vision.

Keywords: Artificial Intelligence, Waste Management, Machine Learning, Deep Learning, Predictive Analytics, Environmental Monitoring.