Authors : Maragathavalli P.; Selvam Marilyn; Dhinesh S.; Muthukumaran A. S.

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/2fa5vwpd

Scribd : https://tinyurl.com/snn4w2rx

DOI : https://doi.org/10.38124/ijisrt/26apr1288

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Traditional urban management systems often operate through isolated, reactive, and passive channels that struggle to address complex, multi-domain civic issues in real time. While digital reporting platforms exist, they frequently lack intelligent mechanisms to validate data, eliminate duplicates, or prioritize issues based on urgency and resource availability. This paper proposes the predictive and priority-driven response system for urban road damage, waste overflow, and streetlight failures using ML techniques. The framework utilizes a multimodal AI pipeline, employing Convolutional Neural Networks (CNN) for image-based issue classification and Natural Language Processing (NLP) for textual validation. To enhance operational efficiency, spatial clustering algorithms are implemented to consolidate duplicate reports, while time-series forecasting models analyze historical patterns to predict future problem hotspots. Furthermore, a mathematical prioritization model incorporating severity, location, and municipal resource constraint optimizes workforce allocation through linear programming. Experimental evaluations indicate that the system achieves high classification accuracy and a significant reduction in duplicate report processing. By shifting from manual complaint logging to automated, data-driven department routing, the system demonstrates measurable improvements in municipal response efficiency and resource utilization across road, waste, and lighting domains. Ultimately, this work promotes sustainable urban governance by fostering transparency and accountability. The scalable architecture provides a robust foundation for developing smarter, citizen-centric cities capable of proactive maintenance and timely decision-making.

Keywords : Machine Learning, CNN, NLP, Urban Management, Duplicates, Priority Scoring, Predictive Analytics.

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