scholarly journals Developing a framework to enhance early warning response capabilities of disaster resilience in the UAE

2015 ◽  
Author(s):  
A. Al Hmoudi
Keyword(s):  
Early Warning ◽  
Disaster Resilience ◽  
Warning Response

Early warning response of Posidonia oceanica epiphyte community to environmental alterations (Ligurian Sea, NW Mediterranean)

2010 ◽  
Vol 60 (7) ◽  
pp. 1031-1039 ◽  
Author(s):  
E. Giovannetti ◽  
M. Montefalcone ◽  
C. Morri ◽  
C.N. Bianchi ◽  
G. Albertelli
Keyword(s):  
Early Warning ◽  
Posidonia Oceanica ◽  
Ligurian Sea ◽  
Nw Mediterranean ◽  
Warning Response ◽  
Epiphyte Community

scholarly journals Comparison of the risk perception and warning response to air pollution on an individual level in China

2021 ◽  
Vol 252 ◽  
pp. 03063
Author(s):  
Fangping Wang ◽  
Fei Su
Keyword(s):  
Air Pollution ◽  
Risk Perception ◽  
Early Warning ◽  
Public Perception ◽  
Warning System ◽  
Pollution Level ◽  
Individual Level ◽  
The Public ◽  
Gender Education ◽  
Warning Response

Individual responses to China’s heavy air pollution early warning are poorly understood. This knowledge gap has hampered the evaluation and improvement of the early warning system in providing the targeted populations with effective protection guidance. In order to explore the public’s response to air pollution warning, field survey were conducted in three major cities of China in 2016. The results indicated that different levels of air pollution warnings were correctly understood in these three cities, but the warning response rate was low. Significant differences in the public’s risk perception were demonstrated. Public perception of the health impacts of air pollution (HEP) and knowledge of the warning index (AQI) were significantly higher in Beijing than in Shenzhen. The public perception of the pollution level (DEVIATION) was equal in Beijing and Shenzhen, but higher than that in Shanghai. Gender, education, and risk perception were crucial factors influencing the public’s willingness to respond to warnings. Early warning policymakers can use this research to optimize the design and dissemination of early warning information to improve the public’s health and quality of life in cities with air pollution.

Using internet GIS technology for early warning, response and controlling the quality of the public health sector

2004 ◽  
Vol 1 (1) ◽  
pp. 78 ◽  
Author(s):  
Dimitrios Ptochos ◽  
Dimitrios Panopoulos ◽  
Kostas Metaxiotis ◽  
Dimitrios Askounis ◽  
John Psarras
Keyword(s):  
Public Health ◽  
Early Warning ◽  
Health Sector ◽  
Gis Technology ◽  
The Public ◽  
Public Health Sector ◽  
Warning Response

scholarly journals Dissemination Application for Early Warning in a Community

2020 ◽  
pp. 67
Author(s):  
Tora Fahrudin ◽  
Kastaman Kastaman
Keyword(s):  
Real Time ◽  
Natural Disaster ◽  
Emergency Response ◽  
Forest Fire ◽  
Early Warning ◽  
Sensor Technology ◽  
Community Members ◽  
Warning Response

The development of sensor technology for early warning at an affordable price makes it possible to be implemented in the community. Therefore, an application is needed to send this early warning information to community members and warning response centers in real-time by considering the distance from the location of the incident. So, in this research, we proposed a dissemination application for early warning in a community (DissApps). The application was build using Code Igniter Framework, MySQL database, Mapquests, and Wablas API. This application can be used for disseminating emergency response situations such as thievery, a natural disaster such as earthquake, flood, forest fire.

scholarly journals MOWLAS: NIED observation network for earthquake, tsunami and volcano

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Shin Aoi ◽  
Youichi Asano ◽  
Takashi Kunugi ◽  
Takeshi Kimura ◽  
Kenji Uehira ◽  
...  
Keyword(s):  
Earthquake Engineering ◽  
Early Warning ◽  
Earthquake Early Warning ◽  
Tsunami Warning ◽  
Disaster Mitigation ◽  
Earthquake Forecasting ◽  
Disaster Resilience ◽  
The 2011 Tohoku Earthquake ◽  
Seafloor Observation ◽  
Observation Networks

Abstract National Research Institute for Earth Science and Disaster Resilience (NIED) integrated the land observation networks established since the 1995 Kobe earthquake with the seafloor observation networks established since the 2011 Tohoku earthquake and tsunami as MOWLAS (Monitoring of Waves on Land and Seafloor) in November 2017. The purpose of MOWLAS is to provide comprehensive, accurate, and rapid observation and monitoring of earthquake, tsunami, and volcano events throughout Japan and its offshore areas. MOWLAS data are widely utilized for long-term earthquake forecasting, the monitoring of current seismic activity, seismic and tsunami hazard assessments, earthquake early warning, tsunami warning, and earthquake engineering, as well as earthquake science. Ocean bottom observations provide an extension of observations to areas where no people are living and have the advantage of increasing lead time of earthquake early warning and tsunami warning. The application of recent technology advancements to real-time observations as well as the processing of MOWLAS data has contributed to the direct disaster mitigation of ongoing earthquakes. These observations are fundamental for both science and disaster resilience, and thus it is necessary to continue ceaseless operation and maintenance.

Hazard identification and early warning system based on stochastic forest algorithm in underground coal mine

2021 ◽  
pp. 1-10
Author(s):  
Yifan Zhao ◽  
Shuicheng Tian
Keyword(s):  
Coal Mine ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Test Sample ◽  
Hazard Identification ◽  
Underground Coal Mine ◽  
Identification Error ◽  
Warning Response ◽  
Decision Forest

Aiming at the problem of large error and long time of early warning response in the traditional system, this paper designs a hazard identification early warning system based on random forest algorithm in underground coal mine. By random classification decision forest created dangerous content in different areas of the downhole information input into the decision tree as a test sample, according to the result of the output of the leaf node determine the risk level of decision trees, and USES the high precision of decision forest classification ability the threat level assessment test sample, radically reducing hazards identification error. Then, based on the evaluation results, combined with the threshold value of warning criteria to identify the gas exceeding limit area, and determine the fire source warning level, so as to realize the hazard source identification and warning. The simulation results show that the average hazard location identification error of the system is only 4.1%, and the warning response time can be controlled within 9 s.

The innovation of the FloodHub system for a reliable flood early warning and crisis management

2021 ◽  
Author(s):  
Alexia Tsouni ◽  
Haris Kontoes ◽  
Themistocles Herekakis ◽  
Stavroula Sigourou ◽  
Theodora Perrou
Keyword(s):  
Real Time ◽  
Crisis Management ◽  
Early Warning ◽  
Urban Areas ◽  
Critical Infrastructure ◽  
Warning System ◽  
Disaster Resilience ◽  
Statistical Regression ◽  
Societal Benefit ◽  
Monitoring Tools

<p>Flood has become the most frequent and deadliest type of disaster by far, responsible for the 43.5% of deaths in 2019. What is more, the number of flood events has extremely increased during the last decade (2000-2019), compared to the previous one (1980-1999) (CRED 2020). Therefore, policy and decision makers, more than ever, need efficient flood monitoring tools in order to facilitate their work towards increasing disaster resilience, especially in the urban and peri-urban areas, where most of the population and critical infrastructure are located. For this purpose, the FloodHub system has been developed by the Center of Earth Observation and Satellite Remote Sensing BEYOND, at the National Observatory of Athens, in the framework of the EuroGEO Disaster Resilience Action Group, supported by on-going actions (SMURBS / ERA-PLANET and Excelsior H2020 projects and the sponsor Hellenic Petroleum S.A.). The innovation of the system lies in the integration of different data sources, so as to deliver a reliable flood early warning system, and an operational awareness picture of the crisis every 5’ to the relevant authorities, namely on three levels: municipality, region, and national civil protection. FloodHub allows the near-real-time ingestion and assimilation of hydrometeorological measurements from in-situ telemetric stations, Sentinels data, and crowdsourced data, in a multi-source data fusion concept, using sophisticated hydrologic and hydraulic modelling and statistical regression techniques. It offers increased reliability through a continuous validation and optimization of results, automation in assimilating flood modeling in real time, computational efficiency, openness, flexibility, scalability, transferability, and the speed to meet rapid awareness during the crisis. Therefore, FloodHub is a useful tool in the hands of the relevant authorities and key stakeholders, contributing to an effective flood risk and crisis management. This is in line with the requirements for the implementation of the EU Floods Directive 2007/60/EC, the Sendai Framework for Disaster Risk Reduction, the UN SDGs, as well as the GEO’s Societal Benefit Areas.</p>

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