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11 results for “disaster management”
Rapid Landslide Risk Zoning toward Multi-Slope Units of the Neikuihui Tribe for Preliminary Disaster Management repository
<p> Taiwan features steep terrain and a fragile geology environment accompanied by frequent earthquakes and typhoons annually. Meanwhile, with the booming economy and rapid population growth, activities pivot from metropolises to the Taiwan's suburban and mountain areas. However, for example, the Neikuihui tribe in northern Taiwan evolves landslide disasters during extreme rainfall events. To rapidly examine landslide risk in the tribe area for preliminary disaster management, the well-known principle of Risk, which comprises Hazard, Exposure, and Vulnerability, was carefully adapted to scrutinize 14 slope units around the Neikuihui tribe region. The framework of risk zoning is improved based on the previous quantified findings regarding the inventory of the deep-seated landslides in southern Taiwan. Moreover, the proposed procedures comprehensively assess susceptibility, activity, exposure, and vulnerability of each slope unit. The rapid risk zoning analysis of multi-slope units delivers a sloping unit with a high level of landslide risk, and this slope unit did suffer from landslide disasters in the 2016 typhoon event. This study preliminarily proves that the proposed framework and details of rapid risk zoning can help identify a relatively high-risk slope unit around a tribal region and address pre-countermeasures for disaster management.</p>
Respondents' perspectives on the impact of digital data-based health services on disaster risk management in Indonesia.
<p>This data contains respondents' perspectives on the impact of digital data-based health services on disaster risk management. Digital health services are the implementation of digital, information, and communication technologies in the context of health services. Digital health services include: mHealth, Health Information Technology, Wearable Devices, Telehealth and Telemedicine, and Personalized Medicine. </p> <p>Data was collected and processed as part of the ODDEA (Overcoming Digital Divide Between Europe and Southeast Asia) EU research project (Project ID: HORIZON MSCA-SE 101086381) would be advisable.</p>
Datasets for input and output of INFORM Severity-based SMAA study of resource allocation in humanitarian aid and disaster management under climatic losses and damages
<p>The landscape of climate change and extreme events will remain a wicked problem for equitable and forward-looking resource prioritisation. The question of how to couple climate and multi-risk information remains. IPCC has considered that multi-criteria decision analysis (MCDA) can help.</p> <p>We use stochastic multi-attribute analysis (SMAA), a variant of MCDA, to compute prioritisations of climatic losses & damages (l&d) for fragile countries with a humanitarian response plan. SMAA is combined with the INFORM Severity index, measuring the status of crises and disasters, and preferences gathered from stakeholders (e.g., United Nations, European Union, World Bank, the research and public sector, civil society).</p> <ul> <li><strong>Dataset S1. </strong>XLS-file with all the input data compiled from sources, concurrent data manipulation, and descriptions of steps taken until ready for the SMAA.</li> <li><strong>Dataset S2.</strong> XLS-file with results of the SMAA for all weight schemes and concurrent analysis, such as sensitivity heat mapping, correlations, regressions, and Tukey mean-difference plot.</li> </ul>
A dataset of community perspectives on living conditions and disaster risk management in informal settlements: A case study in KwaZulu-Natal Province, South Africa
<p>This article describes a dataset of community perspectives on living conditions and disaster risk management in Khan Road, a non-serviced informal settlement, located in Pietermaritzburg, the capital of KwaZulu-Natal province in South Africa. The data were collected by local community researchers via a structured questionnaire of 159 participants conducted between August and September 2022, using mobile phones via KoboToolbox. The dataset was analysed using exploratory data analysis (EDA) techniques. This household survey is part of a research project aiming to develop an evidence base of opportunities, risks and vulnerabilities related to housing construction and resource management in incremental upgrading of informal settlements in South Africa. This dataset can be used by local practitioners and policymakers involved in decision-making for informal settlement upgrading and help them prioritise resources and upgrading interventions based on what informal dwellers need. Furthermore, this cleaned dataset could support the analysis of further South African data guiding the development of digital platforms as a real-time resource management tool or guide the enhancement of existing theoretical frameworks in the field of participatory design and co-production used by academic scholars. </p>
Additonal material for the dissertation "An Accelerated Solution Method for Two-Stage Stochastic Models in Disaster Management": Data, MATLAB codes and results
<p>File "DataImport" contains a "ReadMe" file, raw data for all case studies in Excel and the MATLAB code "ImportData.m" importing Excel data into MATLAB</p> <p>File "LShaped" contains a "ReadMe" file, all data in the form of matrices and the MATLAB code "LShaped_MultiCut.m" solving all case studies via the standard or accelerated L-shaped method using a multi-cut approach</p> <p>File "Results" contains a "ReadMe" file, results of all case studies and computation time required by Gurobi, der standard L-shaped method and accelerated L-shaped method</p>
AIS and START Grade With Films Transferring in Disaster Management
ClinicalTrials.gov study NCT05358418. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
National Conference on Geo-spatial Intelligence for Environmental Hazards and Disaster Management
<p>National conference </p>
The Effect of Two Different Learning Methods on Disaster Perception and Management in Nursing Students
ClinicalTrials.gov study NCT06817317. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
The Effect of a Mixed Intervention Structured According to the Integrated Disaster Management System
ClinicalTrials.gov study NCT07302529. IPD Sharing: NO. Countries: 1. Publications: 0.
The Impact of Crisis Management Training on Disaster Nursing Competencies
ClinicalTrials.gov study NCT06898619. IPD Sharing: NO. Countries: 1. Publications: 0.
SMDRM - Social Media for Disaster Risk Management
<p><strong>SMDRM - Social Media for Disaster Risk Management</strong></p> <p>Social media has been described as a form of distributed cognition, a mechanism for understanding a situation using information spread across many minds. The interactions among people in social media are a form of collective intelligence, as they allow people to make sense of a developing event collectively. Social media users can contribute to creating a "sensor" for citizen-generated data that modelling or monitoring systems can assimilate during a crisis. Gaining situational awareness in a disaster is critical and time-sensitive. Social media presents the possibilities of a growing data source to help improve response in the early hours and days of a crisis. However, social media platforms may not provide the functionality of summarising the information that is useful for crisis responders.SMDRM is a software platform that streamlines the processing of text and images extracted from Twitter in near real-time during a specific event. The data is collected using a combination of keywords and locations based on daily forecasts from the early warnings systems of the Copernicus Emergency Management Service such as EFAS, GloFAS and EFFIS (emergency.copernicus.eu) or triggered manually in case of earthquakes or not-forecasted events. Text is automatically "annotated" using a binary multilingual classifier trained on 12 languages and extended with multilingual embeddings. Simultaneously, a multi-class convolutional neural network labels relevant images for floods, storms, earthquakes and fires. The information that doesn't embed coordinates is geolocated in a two-step algorithm where location candidates are first selected using a multilingual named-entity recognition tool and then searched on available gazetteers. The last step of the SMDRM data processing is the aggregation of relevant information in spatial (administrative areas) and temporal (daily) units. Social media activity about an event can finally be distributed as a data map and visualised on a map server and made available to users.SMDRM could offer timely information useful for reducing the hazard models' uncertainty and providing added-value information such as reports or descriptions of the situation on the ground or in the vicinity. Other stakeholders, such as research groups could access new data to complement the ones extracted from traditional sensors or earth observation. The platform can adapt to cope with the varying workload as it uses scalable software containers. If the number of tweets is higher during an impactful event, the platform can use more containers to annotate them. SMDR code, together with the tens of thousands of annotated social media messages used for training its models, will be released as an open-source platform whose modules can be adapted to serve other research projects. We describe the platform's architecture and implementation details, and two use cases where images and text were used as a use-case to test the system's modules.</p> <p>Source https://ui.adsabs.harvard.edu/abs/2021EGUGA..2315012L/abstract</p>
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OpenNeuro
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