Urban Fire Response Optimization in Karachi Through GIS and AI: A Conceptual Proposal

Amber Naz

No 4c9yk_v1, OSF Preprints from Center for Open Science

Abstract: Karachi, Pakistan’s largest city, faces critical fire-response challenges due to its unplanned dense urbanization, limited infrastructure, and scarce firefighting resources. This conceptual study proposes an integrated framework leveraging Artificial Intelligence (AI), Machine Learning (ML), and Geographic Information Systems (GIS) for proactive fire-response planning in Karachi. The framework consists of three components: AI-driven accessibility mapping for optimizing emergency navigation through congested areas; ML-enhanced service area analysis to improve fire station and hydrant coverage; and ML-GIS–based vulnerability mapping to predict and prioritize high-risk zones for strategic intervention. By utilizing open-source tools such as QGIS and ML libraries, the framework offers a low-cost, scalable approach tailored to resource-constrained settings. This study outlines the framework’s structure, discusses its implications for urban resilience, and proposes directions for empirical validation, offering a transferable blueprint for improving fire-response efficiency in Karachi and other megacities facing similar challenges.

Date: 2025-05-01
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Persistent link: https://EconPapers.repec.org/RePEc:osf:osfxxx:4c9yk_v1

DOI: 10.31219/osf.io/4c9yk_v1

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