A Systematic Review of Disaster Management Systems: Approaches, Challenges, and Future Directions

• Published in: Land (Basel) 2023-08, Vol.12 (8), p.1514
• Main Authors: Khan, Saad Mazhar, Shafi, Imran, Butt, Wasi Haider, Diez, Isabel de la Torre, Flores, Miguel Angel López, Galán, Juan Castanedo, Ashraf, Imran
• Format: Article
• Language: English
• Subjects: Analysis; Arson; Big Data; China; Cloud computing; Data analysis; Data management; Data storage; Decision analysis; Decision making; Disaster management; Disasters; Early warning systems; earthquake; Earthquakes; ecosystem; Emergency communications systems; Emergency management; Emergency preparedness; Flood control; Flood management; Flood predictions; Flood relief; Floods; Forest & brush fires; Geographic information systems; Geospatial data; Information storage and retrieval; Internet of Things; Knowledge management; Machine learning; Malaysia; Management systems; Natural disasters; Nitrogen dioxide; Prediction models; Remote sensing; Research methodology; Resource allocation; Risk assessment; Seismic activity; Seismology; Situational awareness; Software engineering; Spatial data; Systematic review; VOCs; Volatile organic compounds; Warning systems; wildfire; Wildfires
• ISSN: 2073-445X; 2073-445X
• DOI: 10.3390/land12081514

Disaster management is a critical area that requires efficient methods and techniques to address various challenges. This comprehensive assessment offers an in-depth overview of disaster management systems, methods, obstacles, and potential future paths. Specifically, it focuses on flood control, a significant and recurrent category of natural disasters. The analysis begins by exploring various types of natural catastrophes, including earthquakes, wildfires, and floods. It then delves into the different domains that collectively contribute to effective flood management. These domains encompass cutting-edge technologies such as big data analysis and cloud computing, providing scalable and reliable infrastructure for data storage, processing, and analysis. The study investigates the potential of the Internet of Things and sensor networks to gather real-time data from flood-prone areas, enhancing situational awareness and enabling prompt actions. Model-driven engineering is examined for its utility in developing and modeling flood scenarios, aiding in preparation and response planning. This study includes the Google Earth engine (GEE) and examines previous studies involving GEE. Moreover, we discuss remote sensing; remote sensing is undoubtedly a valuable tool for disaster management, and offers geographical data in various situations. We explore the application of Geographical Information System (GIS) and Spatial Data Management for visualizing and analyzing spatial data and facilitating informed decision-making and resource allocation during floods. In the final section, the focus shifts to the utilization of machine learning and data analytics in flood management. These methodologies offer predictive models and data-driven insights, enhancing early warning systems, risk assessment, and mitigation strategies. Through this in-depth analysis, the significance of incorporating these spheres into flood control procedures is highlighted, with the aim of improving disaster management techniques and enhancing resilience in flood-prone regions. The paper addresses existing challenges and provides future research directions, ultimately striving for a clearer and more coherent representation of disaster management techniques.