Determining Disaster Risk Management Priorities through a Neural Network-Based Text Classifier

Canon, M.; Satuito, Arlene; Sy, Christian

doi:10.1109/is3c.2018.00067

Cited by 12 publications

(3 citation statements)

References 8 publications

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“…AI deals with computer-related activities concerned with building-related intelligent machines. Over the last decade, AI breakthroughs have considerably increased our capacity to forecast disasters and provide support throughout catastrophes [16][17][18]. AI development can be evident in disaster preparedness, crowdsourced information systems, rescue, and humanitarian distribution [15,19].…”

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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“…AI can revolutionize this first phase by analyzing large volumes of data using AI and ML. Based on insights from analyses, decision-makers can develop effective mitigation strategies (Gama et al , 2016), such as identifying management priorities (Canon et al , 2019) and developing contingency plans (Dou et al , 2014).…”

Section: Theoretical Developmentmentioning

confidence: 99%

Digital transformation to mitigate emergency situations: increasing opioid overdose survival rates through explainable artificial intelligence

Johnson¹,

Albizri²,

Harfouche

et al. 2021

IMDS

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PurposeThe global health crisis represents an unprecedented opportunity for the development of artificial intelligence (AI) solutions. This paper aims to integrate explainable AI into the decision-making process in emergency scenarios to help mitigate the high levels of complexity and uncertainty associated with these situations. An AI solution is designed to extract insights into opioid overdose (OD) that can help government agencies to improve their medical emergency response and reduce opioid-related deaths.Design/methodology/approachThis paper employs the design science research paradigm as an overarching framework. Open-access digital data and AI, two essential components within the digital transformation domain, are used to accurately predict OD survival rates.FindingsThe proposed AI solution has two primary implications for the advancement of informed emergency management. Results show that it can help not only local agencies plan their resources for timely response to OD incidents, thus improving survival rates, but also governments to identify geographical areas with lower survival rates and their primary contributing factor; hence, they can plan and allocate long-term resources to increase survival rates and help in developing effective emergency-related policies.Originality/valueThis paper illustrates that digital transformation, particularly open-access digital data and AI, can improve the emergency management framework (EMF). It also demonstrates that the AI models developed in this study can identify opioid OD trends and determine the significant factors improving survival rates.

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“…Disaster Risk Management Priorities can be determined in which public feedback can be used to make local communities better in times of disaster. In [16], a Bidirectional Recurrent Neural Network (BRNN) analyzes the collected textual data and sequential data. They divided the data corpus in which 85% of the training data and 15% of the testing data were used to construct a BRNN model that achieved an 81.67% accuracy rate.…”

Section: Literature Reviewmentioning

confidence: 99%

Using a Hybrid-Classification Method to Analyze Twitter Data During Critical Events

Alhashmi

Khedr²,

Arif

et al. 2021

IEEE Access

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In this paper, sentiment analysis of two critical events is presented using machine learning (ML) techniques. COVID-19 has put immense pressure across the globe and sentiment analysis of data from Twitter using ML techniques has become a hot topic. We extract the COVID-19 and Expo2020 data from twitter. First, we evaluate the Twitter data of these two significant events for sentiment analysis and then use the classification algorithm to find out the usefulness of the proposed methodology. A hybrid approach that uses supervised learning model Support Vector Machine (SVM) combined with Bayes Factor Tree Augmented Naive Bayes (BFTAN) technique is proposed to accurately classify the input tweet while keeping in mind the different challenges of sentiment analysis. Our study has four main contributions: a) hybrid classification techniques are thoroughly explored for sentiment analysis, b) a novel hybrid classification approach is proposed for sentiment analysis, c) a new Twitter dataset related to COVID-19 that can be used for future research, d) empirical study to show that the hybrid-classification approach can achieve comparable performance in improving accuracy, identifying the polarity of comparative sentences, distinguishing the intensity of opinion words, considering negative words, and handling sarcasm as well. The experimental results show that the proposed approach is robust in producing correct classification results with the tradeoff of poor time efficiency. Also, the accuracy of the proposed model is comparable to other classifiers, which is encouraging. Class distribution of each dataset demonstrates that more than 60% of tweets are negative.

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Determining Disaster Risk Management Priorities through a Neural Network-Based Text Classifier

Cited by 12 publications

References 8 publications

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

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

Digital transformation to mitigate emergency situations: increasing opioid overdose survival rates through explainable artificial intelligence

Using a Hybrid-Classification Method to Analyze Twitter Data During Critical Events

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