early-warning-and-conflict-resolution-report-by-rodolfo-stavenhagen-rapporteur-of-the-consultation-on-early-warnings-geneva-12-august-1989-10-pp

2016 ◽  
Keyword(s):  
Conflict Resolution ◽  
Early Warning ◽  
Early Warnings

scholarly journals A Flash Flood Early Warning System for Rural Kenya: A Pilot Study

2018 ◽  
Vol 7 (4.38) ◽  
pp. 1310
Author(s):  
Prof. Dr. Ir Vinesh Thiruchelvam ◽  
Mbau Stella Nyambura
Keyword(s):  
Climate Change ◽  
Early Warning ◽  
Rural Areas ◽  
Early Warning System ◽  
Flash Flood ◽  
Warning System ◽  
Flash Floods ◽  
Short Term ◽  
Moderation Effect ◽  
Early Warnings

The cost of climate change has increased phenomenally in recent years. Therefore, understanding climate change and its impacts, that are likely to get worse and worse into the future, gives us the ability to predict scenarios and plan for them. Flash floods, which are a common result of climate change, follow increased precipitation which then increases risk and associated vulnerability due to the unpredictable rainfall patterns. Developing countries suffer grave consequences in the event that weather disasters strike because they have the least adaptive capacity. At the equator where the hot days are hotter and winds carrying rainfall move faster, Kenya’s Tana River County is noted for its vulnerability towards flash floods. Additionally, this county and others that are classified as rural areas in Kenya do not receive short term early warnings for floods. This county was therefore selected as the study area for its vulnerability. The aim of the study is therefore to propose a flash flood early warning system framework that delivers short term early warnings. Using questionnaires, information about the existing warning system will be collected and analyzed using SPSS. The results will be used to interpret the relationships between variables of the study, with a particular interest in the moderation effect in order to confirm that the existing system can be modified; that is, if the moderation effect is confirmed.       

scholarly journals A Flash Flood Early Warning System for Rural Kenya: A Pilot Study

2018 ◽  
Vol 7 (4.38) ◽  
pp. 810
Author(s):  
Prof. Dr. Ir Vinesh Thiruchelvam ◽  
Mbau Stella Nyambura
Keyword(s):  
Climate Change ◽  
Early Warning ◽  
Rural Areas ◽  
Early Warning System ◽  
Flash Flood ◽  
Warning System ◽  
Flash Floods ◽  
Short Term ◽  
Moderation Effect ◽  
Early Warnings

The cost of climate change has increased phenomenally in recent years. Therefore, understanding climate change and its impacts, that are likely to get worse and worse into the future, gives us the ability to predict scenarios and plan for them. Flash floods, which are a common result of climate change, follow increased precipitation which then increases risk and associated vulnerability due to the unpredictable rainfall patterns. Developing countries suffer grave consequences in the event that weather disasters strike because they have the least adaptive capacity. At the equator where the hot days are hotter and winds carrying rainfall move faster, Kenya’s Tana River County is noted for its vulnerability towards flash floods. Additionally, this county and others that are classified as rural areas in Kenya do not receive short term early warnings for floods. This county was therefore selected as the study area for its vulnerability. The aim of the study is therefore to propose a flash flood early warning system framework that delivers short term early warnings. Using questionnaires, information about the existing warning system will be collected and analyzed using SPSS. The results will be used to interpret the relationships between variables of the study, with a particular interest in the moderation effect in order to confirm that the existing system can be modified; that is, if the moderation effect is confirmed.   

scholarly journals Early Warning for the Construction Safety Risk of Bridge Projects Using a RS-SSA-LSSVM Model

2021 ◽  
Vol 2021 ◽  
pp. 1-14 ◽  
Author(s):  
Gang Li ◽  
Ruijiang Ran ◽  
Jun Fang ◽  
Hao Peng ◽  
Shengmin Wang
Keyword(s):  
Early Warning ◽  
Construction Safety ◽  
Bridge Engineering ◽  
Bridge Construction ◽  
Safety Risk ◽  
Early Warning Model ◽  
Safety Risks ◽  
Proposed Model ◽  
Warning Model ◽  
Early Warnings

Bridge engineering is an important component of the transportation system, and early warnings of construction safety risks are crucial for bridge engineering construction safety. To solve the challenges faced by early warnings risk and the low early warning accuracy in bridge construction safety, this study proposed a new early-warning model for bridge construction safety risk. The proposed model integrates a rough set (RS), the sparrow search algorithm (SSA), and the least squares support vector machine (LSSVM). In particular, the initial early warning factors of bridge construction safety risk from five factors (men, machines, methods, materials, and environment) were selected, and the RS was used to reduce the attributes of 20 initial early warning factors to obtain the optimized early warning factor set. This overcame the problem of multiple early warning factors and reduced the complexity of the subsequent prediction model. Then, the LSSVM with the strongest nonlinear modelling ability was selected to build the bridge construction early-warning model and adopted the SSA to optimize the LSSVM parameter combination, improving the early warning accuracy. The Longlingshan Project in Wuhan and the Shihe Bridge Project in Xinyang, China, were then selected as case studies for empirical research. Results demonstrated a significant improvement in the performance of the early-warning model following the removal of redundancy or interference factors via the RS. Compared with the standard LSSVM, Back Propagation Neural Network and other traditional early-warning models, the proposed model exhibited higher computational efficiency and a better early warning performance. The research presented in this article has important theoretical and practical significance for the improvement of the early warning management of bridge construction safety risks.

Study on the Changes in People’s Consciousness Regarding the Earthquake Early Warning Before and After the Great East Japan Earthquake – Analysis Based on Regular Disaster Information Survey Results –

2013 ◽  
Vol 8 (sp) ◽  
pp. 792-801
Author(s):  
Miho Ohara ◽  
◽  
Atsushi Tanaka ◽  
Keyword(s):  
Early Warning ◽  
General Public ◽  
Earthquake Early Warning ◽  
Great East Japan Earthquake ◽  
Secular Changes ◽  
Study Results ◽  
Disaster Information ◽  
Before And After ◽  
Survey Results ◽  
Early Warnings

In Japan, earthquake early warnings (EEWs), also known as alerts, have been broadcast to the general public since October 1, 2007. Issuance times of EEWincreased drastically after the Great East Japan Earthquake, and citizens had much more frequent experience with EEWs. This study analyzes secular changes in rates of recognition and reception experiences of EEW. It also analyzes awareness regarding EEW accuracy by comparing study results with EEW issuance history nationwide. Secular changes in the expectations of the general public regarding EEWs are also clarified.

AN EARLY WARNING OF AN IMPENDING CURRENCY CRISIS IN CHINA

2018 ◽  
Vol 64 (05) ◽  
pp. 1101-1125
Author(s):  
RUOXI ZHANG ◽  
XUE LI ◽  
SATISH CHAND
Keyword(s):  
Financial Crisis ◽  
Loss Function ◽  
Early Warning ◽  
Currency Crisis ◽  
Economic Indicators ◽  
International Reserves ◽  
Remedial Action ◽  
Bank Deposits ◽  
Signal Approach ◽  
Early Warnings

Are there early warnings of an impending financial crisis in China? Our analysis using the Kaminsky–Lizondo–Reinhart (KLR) signal approach reveals that the probability of China having a currency crisis in the 24 months to October 2017 could be increased assuming no remedial action by the authorities to avert an impending crisis. Notwithstanding the above, our analysis shows that nine out of 15 economic indicators are effective in predicting a currency crisis. Loss function of policymakers and evaluation of usefulness are then employed to verify their validity. The results show that bank deposits and M2/international reserves are the most powerful indicators.

The human dimension of early warning – a viewpoint

2019 ◽  
Vol 11 (2) ◽  
pp. 263-274
Author(s):  
Mo Hamza ◽  
Peter Månsson
Keyword(s):  
Early Warning ◽  
Warning System ◽  
Early Warning Systems ◽  
Human Dimension ◽  
Warning Systems ◽  
Theoretical Frameworks ◽  
Content Type ◽  
Starting Point ◽  
End To End ◽  
Early Warnings

Purpose The 2004 Boxing Day tsunami prompted global efforts to develop end-to-end multi-hazard warning systems. Taking this event as a starting point, and drawing on experiences from the following advancement of the Indonesian tsunami early warning system, this paper aims to highlight the importance of paying attention to human factors and the perceptions and behaviors of end recipients when trying to design efficient early warning systems. Design/methodology/approach The study is a viewpoint where theoretical frameworks for the design of efficient early warning systems are used as backdrop to an extensive review and analysis of secondary data, including scientific papers and newspaper articles. Findings The paper presents what an end-to-end warning system means, explores process problems related to perception and communication and concludes with views and recommendations toward more inclusive early warnings. Originality/value Research and practice related to early warning systems have traditionally had a strong focus on technological elements whilst the target groups of early warnings (i.e. communities) have received far less attention and resources. This paper focuses on the human dimension of warning systems and uses a real case to exemplify how efficient warning systems not only require a sound scientific and technological basis, but also depend on the awareness, trust and will of the people they aim to protect.

A NN-Based Approach for Monitoring Early Warnings of Risk in Historic Buildings via Image Novelty Detection

2014 ◽  
Vol 628 ◽  
pp. 212-217 ◽  
Author(s):  
Leonarda Carnimeo ◽  
Rosamaria Nitti
Keyword(s):  
Neural Network ◽  
Early Warning ◽  
Vulnerability Assessment ◽  
Novelty Detection ◽  
Historic Buildings ◽  
Architectural Heritage ◽  
Early Warnings

Architectural heritage is an important part of the history and identity of countries, but ancient buildings suffer a high vulnerability to hazards, which may induce unpredictable damages. For this purpose, the development of techniques for monitoring historic buildings and immediately alerting in case of early vulnerability assessment is a main objective to be pursued. This paper concerns with a proposal of noninvasive Neural Network-based approach for predicting risk events in artistic buildings. More in detail, a neural approach is suggested for detecting temporal novelties in images of historic evidences with the aim of monitoring early warning of risk events.

scholarly journals Early warning of climate tipping points from critical slowing down: comparing methods to improve robustness

2012 ◽  
Vol 370 (1962) ◽  
pp. 1185-1204 ◽  
Author(s):  
T. M. Lenton ◽  
V. N. Livina ◽  
V. Dakos ◽  
E. H. van Nes ◽  
M. Scheffer
Keyword(s):  
Early Warning ◽  
Thermohaline Circulation ◽  
Critical Slowing Down ◽  
Ice Age ◽  
Fluctuation Analysis ◽  
Test Bed ◽  
Slowing Down ◽  
Last Ice Age ◽  
Atlantic Thermohaline Circulation ◽  
Early Warnings

We address whether robust early warning signals can, in principle, be provided before a climate tipping point is reached, focusing on methods that seek to detect critical slowing down as a precursor of bifurcation. As a test bed, six previously analysed datasets are reconsidered, three palaeoclimate records approaching abrupt transitions at the end of the last ice age and three models of varying complexity forced through a collapse of the Atlantic thermohaline circulation. Approaches based on examining the lag-1 autocorrelation function or on detrended fluctuation analysis are applied together and compared. The effects of aggregating the data, detrending method, sliding window length and filtering bandwidth are examined. Robust indicators of critical slowing down are found prior to the abrupt warming event at the end of the Younger Dryas, but the indicators are less clear prior to the Bølling-Allerød warming, or glacial termination in Antarctica. Early warnings of thermohaline circulation collapse can be masked by inter-annual variability driven by atmospheric dynamics. However, rapidly decaying modes can be successfully filtered out by using a long bandwidth or by aggregating data. The two methods have complementary strengths and weaknesses and we recommend applying them together to improve the robustness of early warnings.

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