Big Data Analytics in Disaster Management

Abstract

This study examines the role of big data analytics in disaster and crisis management, in light of the growing frequency and severity of natural disasters and humanitarian crises and the limitations of traditional, reactive response practices. The research problem addresses the extent to which big data analytics and predictive technologies can fundamentally transform the effectiveness of crisis management toward proactive modeling, and the main challenges and applied practices that govern this transformation. Drawing on recent field studies and international experiences, the study is organized into three sections. The first addresses the concept of big data and the role of predictive analytics in forecasting disasters. The second discusses methods of collecting big data from sources such as mobile phones, satellite imagery, the Internet of Things and crowdsourcing, along with the associated technical and ethical challenges. The third explores the role of big data in improving planning and coordination during crises, compares traditional and modern systems, reviews successful cases such as the Haiti earthquake, Hurricane Harvey and the Pakistan floods, and outlines future trends. The study found that unstructured data account for more than 80% of crisis data, making deep learning and natural language processing indispensable; that social media platforms act as effective human sensors for rapid impact assessment; and that predictive models can anticipate road flooding and loss of access to hospitals 4 to 24 hours in advance, while privacy, data security and the shortage of qualified personnel remain the main challenges. The study recommends building high-performance computing platforms, adopting clear policies to protect the privacy of affected people, activating open and humanitarian data-sharing initiatives, bridging the skills gap through specialized training, and relying on hybrid systems that combine machine speed with human expertise.