Remote sensing for early warning of natural meteorological and hydrological disasters and provision of transportation safety over the Black Sea in Georgia

Kordzakhia, George; Shengelia, Larisa; Tvauri, Genadi; Tatishvili, Marika; Mkurnalidze, Irine

doi:10.1016/j.sbspro.2011.05.166

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…It is essential, however, to integrate remote sensing data and GIS data during flood management, particularly in disaster relief operations. In summary, remote sensing data find application in flood management for hazard assessment map preparation, hydrological model generation, quantitative soil assessment, flood risk mapping, and early warning [65].…”

Section: Floodsmentioning

confidence: 99%

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

Khan,

Shafi,

Butt

et al. 2023

Land

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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.

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Section: Floodsmentioning

confidence: 99%