Disaster management in real time simulation using machine learning

Khouj, Mohammed; López, César A.; Sarkaria, Sarbjit; Martí, José

doi:10.1109/ccece.2011.6030716

Cited by 16 publications

(8 citation statements)

References 3 publications

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“…People have recently enhanced their use of social media sites and others to broadcast a range of data such as stylistic messages, photographs, and films online during the outbreak of any natural or man-made calamity in the current era [34]. In previous natural and social crisis scenarios, such as flooding, social media data have shown to be a vital source of information, earthquakes, wildfires, nuclear disasters, and civil wars [35].…”

Section: Artificial Intelligence and Disaster Managementmentioning

confidence: 99%

Toward an Integrated Disaster Management Approach: How Artificial Intelligence Can Boost Disaster Management

et al. 2021

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Technical and methodological enhancement of hazards and disaster research is identified as a critical question in disaster management. Artificial intelligence (AI) applications, such as tracking and mapping, geospatial analysis, remote sensing techniques, robotics, drone technology, machine learning, telecom and network services, accident and hot spot analysis, smart city urban planning, transportation planning, and environmental impact analysis, are the technological components of societal change, having significant implications for research on the societal response to hazards and disasters. Social science researchers have used various technologies and methods to examine hazards and disasters through disciplinary, multidisciplinary, and interdisciplinary lenses. They have employed both quantitative and qualitative data collection and data analysis strategies. This study provides an overview of the current applications of AI in disaster management during its four phases and how AI is vital to all disaster management phases, leading to a faster, more concise, equipped response. Integrating a geographic information system (GIS) and remote sensing (RS) into disaster management enables higher planning, analysis, situational awareness, and recovery operations. GIS and RS are commonly recognized as key support tools for disaster management. Visualization capabilities, satellite images, and artificial intelligence analysis can assist governments in making quick decisions after natural disasters.

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Section: Artificial Intelligence and Disaster Managementmentioning

confidence: 99%

Toward an Integrated Disaster Management Approach: How Artificial Intelligence Can Boost Disaster Management

et al. 2021

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“…In past, large numbers of people died due to suffocation in crowded areas in various public gathering events. Better crowd management can be made in such events to avoid accidents [ 29 , 30 , 31 ].…”

Section: Applicationsmentioning

confidence: 99%

Advances and Trends in Real Time Visual Crowd Analysis

Khan

Albattah

Khan

et al. 2020

Sensors

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Real time crowd analysis represents an active area of research within the computer vision community in general and scene analysis in particular. Over the last 10 years, various methods for crowd management in real time scenario have received immense attention due to large scale applications in people counting, public events management, disaster management, safety monitoring an so on. Although many sophisticated algorithms have been developed to address the task; crowd management in real time conditions is still a challenging problem being completely solved, particularly in wild and unconstrained conditions. In the proposed paper, we present a detailed review of crowd analysis and management, focusing on state-of-the-art methods for both controlled and unconstrained conditions. The paper illustrates both the advantages and disadvantages of state-of-the-art methods. The methods presented comprise the seminal research works on crowd management, and monitoring and then culminating state-of-the-art methods of the newly introduced deep learning methods. Comparison of the previous methods is presented, with a detailed discussion of the direction for future research work. We believe this review article will contribute to various application domains and will also augment the knowledge of the crowd analysis within the research community.

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“…The emotion elicitation process they used was based on the Appraisal Theory (Lazarus, 1991). Khouj et al showed some prelaminar results of the modelling and simulation of an intelligent agent by using RL algorithms to assist a human emergency responder, with a goal of maximizing the number of patients discharged from hospitals or on-site emergency units (Khouj et al, 2011). During the simulations, the proposed agent, called DAARTS (Decision Assistant Agent in Real Time Simulation), was able to help the emergency responder, leading to a favorable outcome.…”

Section: Agent Behaviormentioning

confidence: 99%