Cloud-Powered Predictive Analytics for Illegal Dumping Detection and Enforcement with Random Forest Algorithm

Efficient identification and enforcement measures are necessary to address the serious environmental and public health risks of illegal dumping. In this paper, a system for cloud-based predictive analytics that can identify and sanction unlawful dumping using the Random Forest method is presented. Scalable data processing and analysis are made possible by using cloud computing infrastructure. Using past data and environmental characteristics, the Random Forest algorithm forecasts which locations might be vulnerable to unlawful dumping because of its reliability and precision in classification tasks. To improve the precision and timeliness of detection, the framework incorporates real-time data streams from several sources, such as social media, geographic information systems (GIS), and remote sensing photography. The system uses enforcement optimization tactics, meaning regions with a greater probability of unlawful dumping are targeted more often. Results from a case study indicate that the proposed technique works in a city, outperforming more conventional approaches in terms of detection rates and enforcement efficiency. It introduces a new cloud-based approach to proactively reducing unlawful dumping, which may help with long-term environmental management and meeting regulatory requirements.