Strategies for Crisis-Responsive Governance: Automated Anomaly Identification in Public Services

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Keywords:

311 services, classification, anomaly detection, supervised learning, disaster management

Abstract

This paper introduces a machine learning tool for service systems, focusing on accurate classification of service requests and swift anomaly detection, particularly crucial during emergencies. Employing a Support Vector Machine model, this tool automatically classifies service calls into predefined categories with high accuracy, while effectively detecting irregular requests that require specific attention from operators. This approach streamlines resource management by reducing the manual categorization workload and enables early detection of emerging service needs. Examining Orange County, Florida 311 System data, with a specific focus on the COVID-19 period, we illustrate the tool's success in automatic request categorization and anomaly detection. Overall, this tool presents an effective automation approach to help with efficient resource management of service systems and proactive assessment of public service needs, promising to revolutionize service request management during crises. Future work will explore additional classification models for enhanced accuracy and integrate automated alerts for proactive disaster management.

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Published

2024-05-22

How to Cite

Unveren, H., Lehyeh, A. A., Pamukcu, D., & Zobel, C. W. (2024). Strategies for Crisis-Responsive Governance: Automated Anomaly Identification in Public Services. ISCRAM Proceedings, 21. http://ojs.iscram.org/index.php/Proceedings/article/view/106

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