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

Abstract

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.

Applying Digital Technology to Understand Human Experiences of Climate Change Impacts on Food Security and Mental Health: Scoping Review

“…In many instances, communities that are vulnerable to adverse climate change events are also disproportionately affected by food insecurity [34,109,110]. Consistent with existing literature [40,[50][51][52][53], this review found that digital apps, particularly those which provide real-time alerts, information, and communication, can not only ensure that solutions are catered to communities' specific needs, but also foster a sense of empowerment among community members [83,84,92].…”

“…Capturing the intersection of these complex global health issues requires leveraging existing and emerging technology [50][51][52]. Digital applications (apps) have long been used to track changing weather patterns and climate events [40,53] but the increasing frequency and severity of climate change warrants citizen engagement and participation to better understand both specific impacts, as well as targeted solutions in the age of climate emergency [1,5,12]. Citizen science has a longstanding history of citizen engagement to capture local environmental impacts.…”

Novel Learning of Bathymetry from Landsat 9 Imagery Using Machine Learning, Feature Extraction and Meta-Heuristic Optimization in a Shallow Turbid Lagoon

“…The KTB, a new boosting ML model introduced in 2021, presented a high potential in learning and predicting complex data with similar performance to the CB model (R 2 = 0.86). Boosting was found to outperform the bagging and SVM groups in the retrieval of both classification and bio-physical parameters [27,50,51,61] with advancements in algorithm structures and the creative workflow in the decision making of the final model [62,63].…”

Optimizing the Numerical Simulation of Debris Flows: A New Exploration of the Hexagonal Cellular Automaton Method

“…In the field of debris flow disaster mitigation, numerical technologies play a crucial role in disaster prevention [13,14]. Many studies focus on numerically solving the Navier-Stokes (N-S) equation.…”