Leveraging Big Data in Waste Management: A Knowledge-Based Approach

Abstract

The rapid urbanization of cities worldwide has led to a significant increase in waste production, posing challenges for traditional waste management systems. These systems, often reliant on fixed schedules and routes, struggle to adapt to dynamic demand and growing environmental concerns. This study explores how integrating big data analytics with systematic knowledge management can optimize waste management, enhance efficiency, and promote environmental sustainability. By leveraging GPS tracking, IoT sensors, and predictive analytics, cities can develop intelligent, responsive, and sustainable waste management systems.

Introduction

Rapid urbanization has increased waste generation, exposing inefficiencies in traditional fixed-route collection systems, such as missed pickups, redundant trips, and high operational costs. Big data analytics and knowledge management (KM) offer solutions by enabling real-time monitoring, predictive waste forecasting, and dynamic route optimization, improving efficiency while reducing environmental impact. IoT sensors in bins, GPS tracking of collection trucks, and environmental monitoring systems provide actionable data that supports smarter, demand-driven waste collection.

Integrating big data with KM allows cities to document best practices, share lessons learned, and continuously improve policies, creating a learning ecosystem for adaptive waste management. Case studies, such as Thrissur Corporation’s Harithamithram app, demonstrate practical benefits, providing real-time notifications, proper waste segregation guidance, and easier household participation in sustainable practices.

Additionally, artificial intelligence and robotics are increasingly applied in waste sorting, treatment, predictive modeling, and logistics, helping reduce landfill usage, illegal dumping, and operational inefficiencies. Globally, AI adoption in countries like Germany, Japan, Singapore, and the USA is enhancing sustainability and operational efficiency in urban waste management, making technology-driven solutions critical for modern cities.

Conclusion

Waste disposal is inefficient, leading to severe environmental pollution, high costs, and a lack of leadership in the disposal process. Waste management is a challenge for both developed and developing countries. However, artificial intelligence can improve treatment efficiency, reduce environmental damage, and provide computational solutions for smarter waste management. Artificial intelligence in waste management highlights the potential impact of artificial intelligence on waste management, with practical applications such as smart bin systems, waste-sorting robots, and predictive waste tracking models. Artificial intelligence can also assist in managing hazardous waste, reducing illegal dumping, and recovering valuable resources from the waste stream. Additionally, artificial intelligence can aid public health interventions, including medical waste disposal and pandemic response. The paper examines the impact of artificial intelligence on waste logistics and transportation, including reducing distance, cost, and collection time and improving collection efficiency. Despite these challenges, artificial intelligence can change how people deal with waste, leading to a more sustainable future with efficient, economic, ecological, and intelligent waste management systems. By leveraging GPS tracking, IoT sensors, and predictive analytics, cities like Thrissur which is the cultural capital of Kerala, God’s own country can become a good model for smart waste management and develop intelligent, responsive, and sustainable waste management systems.

References

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Copyright

Copyright © 2026 Soumya C, Kumutha V. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.