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Paper ID:IJSREM63097

Delhi Water Watch: A Role-Based, Event-Driven Civic Intelligence Architecture for Urban Waterlogging Management

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM63097

Volume:

10 |

5 |

May 2026

Pages:

1-8

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM63097

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

ADITYA SHARMA

ADITYA SHARMA

Department of Computer Science

Bhagwan Parshuram Institute of Technology (BPIT), India

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RC

Ritesh Chauhan

Department of Computer Science

Bhagwan Parshuram Institute of Technology (BPIT), India

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HK

Harsh Kumar

Department of Computer Science

Bhagwan Parshuram Institute of Technology (BPIT), India

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DB

Dinesh Bhardwaj

Department of Computer Science

Bhagwan Parshuram Institute of Technology (BPIT), India

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Abstract

Urban waterlogging management in high-density cities remains constrained by delayed complaint intake, fragmented field coordination, and weak end-to-end traceability. This paper presents Delhi Water Watch, a role-based, event-driven civic platform designed to support complete lifecycle management of waterlogging incidents—from citizen reporting to administrative closure. The architecture introduces four bounded operational roles (citizen, ward administrator, field worker, and super administrator), each with isolated permissions, contextual views, and workflow responsibilities. The system integrates geotagged issue reporting, real-time status propagation, ward-specific triage, field assignment with completion proof, map-centric situational monitoring, and notification orchestration. A predictive intelligence layer further assists governance by generating AI-based hotspot alerts using weather signals and historical report trends, with human validation before activation. Implemented using React + TypeScript, Firebase Authentication, Firestore real-time listeners, and Leaflet-based geospatial visualization, the platform demonstrates improved operational clarity and accountability over linear grievance channels. Qualitative pilot observations show stronger role coordination, higher status transparency for citizens, and better city-level preparedness visibility. The proposed framework contributes a practical architecture for climate-resilient, digitally governed municipal response systems.

KEYWORDS

Keywords: urban waterlogging civic informatics event-driven architecture role-based access control AI-assisted prediction real-time dashboards municipal governance smart city resilience

Cite This Article

ADITYA SHARMA, Ritesh Chauhan, Harsh Kumar, Dinesh Bhardwaj, "Delhi Water Watch: A Role-Based, Event-Driven Civic Intelligence Architecture for Urban Waterlogging Management," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-8, May 2026. https://doi.org/10.55041/IJSREM63097

Full Article/Conclusion

Delhi Water Watch presents a deployable civic intelligence architecture for waterlogging governance in high-density urban environments. By combining role-bounded access control, event-driven operational state management, geospatial incident monitoring, and supervised AI-assisted forecasting, the platform substantially improves traceability, inter-role coordination, and proactive preparedness. The pilot results demonstrate a 75% reduction in average complaint resolution time, a 52% resolution rate within the pilot window, and successful AI hotspot activation prior to high-rainfall events. The framework is architecturally suitable for municipal pilots across Indian cities and is extensible toward city-scale, climate-resilient digital governance infrastructure with the planned additions of immutable audit ledgers, RAG-enhanced inference, and multi-ward collaborative operations. As urban flooding intensifies under climate change, platforms like Delhi Water Watch offer a replicable model for digitally governed, accountable, and anticipatory municipal response systems.

References

[1] Liu, N. F., et al. (2023). Lost in the Middle: How Language Models Use Long Contexts. Transactions of the Association for Computational Linguistics (TACL). [2] Wu, Q., et al. (2023). AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation. arXiv preprint arXiv:2308.08155. [3] Fowler, M. (2005). Event Sourcing. martinfowler.com. Retrieved from https://martinfowler.com/eaaDev/EventSourcing.html [4] Packer, C., et al. (2023). MemGPT: Towards LLMs as Operating Systems. arXiv preprint arXiv:2310.08560. [5] Firebase Documentation. (2024). Authentication and Cloud Firestore. Google LLC. https://firebase.google.com/docs [6] OpenStreetMap Contributors. (2024). OpenStreetMap Documentation. https://wiki.openstreetmap.org [7] India Meteorological Department (IMD). (2024). Delhi Monsoon Rainfall Bulletin, Monsoon Season 2024. Ministry of Earth Sciences, Government of India. [8] Ahuja, S., & Bhattacharya, P. (2022). Smart City Governance and Digital Civic Infrastructure: A Review of Municipal Complaint Management Systems in India. Journal of Urban Technology, 29(2), 45–68.

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Publication Date

5/12/2026

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Review Rounds: 2

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