DATA-DRIVEN DISASTER MANAGEMENT SYSTEMS: INTEGRATING ENVIRONMENTAL SCIENCE AND COMPUTING

Abstract

Data-driven disaster management systems (DDMS) have become critical in minimizing the impacts of natural disasters by leveraging real-time data, advanced computing techniques, and environmental science. These systems offer solutions for efficient monitoring, prediction, and mitigation of disasters such as floods, earthquakes, and droughts. The integration of environmental science with computing allows for the development of sophisticated models capable of forecasting disaster events, optimizing resource allocation, and improving response times. This paper examines the role of data analytics, machine learning, and geographic information systems (GIS) in disaster management. We explore how environmental data, coupled with computational algorithms, can provide actionable insights for policymakers, emergency responders, and affected communities. The key objectives of this research are to present a framework for integrating environmental data with disaster management computing, highlight the benefits and challenges, and provide a roadmap for future innovations in this field.