AI-DRIVEN WASTE MANAGEMENT SYSTEMS: A COMPARATIVE REVIEW OF INNOVATIONS IN THE USA AND AFRICA

Abstract

The burgeoning challenges of waste management have propelled the integration of artificial intelligence (AI) into waste management systems, aiming to enhance efficiency, sustainability, and environmental impact. This abstract delves into the comparative review of AI-driven waste management innovations in the USA and Africa, illuminating the divergent strategies employed to address distinct contextual demands. In the USA, where waste management infrastructures are relatively advanced, AI technologies are leveraged to optimize waste collection routes, automate sorting processes, and enhance recycling efficiency. Machine learning algorithms analyze historical data to predict waste generation patterns, enabling municipalities to allocate resources more effectively. Additionally, robotic sorting systems equipped with computer vision contribute to the accurate segregation of recyclables, reducing contamination and promoting a circular economy. Conversely, in Africa, where waste management infrastructures may be less developed, AI applications prioritize scalable and adaptable solutions. Mobile applications powered by AI facilitate crowd-sourced waste reporting, enabling citizens to actively participate in waste management efforts. Furthermore, sensor-equipped smart bins optimize collection routes in real-time, improving resource utilization. The emphasis on community engagement and decentralized solutions reflects the unique challenges and opportunities present in African waste management contexts. Despite these regional disparities, common themes emerge, such as the role of data analytics, automation, and community involvement in shaping effective waste management systems. The comparative analysis underscores the importance of tailoring AI-driven innovations to the specific socio-economic and infrastructural landscapes of each region. Ultimately, understanding the nuanced approaches in the USA and Africa can inform a more holistic and globally adaptable framework for AI-driven waste management systems.