scholarly journals Implementation of an early warning system in urban drainage infrastructures for direct discharges and flood risk management

2014 ◽  
Author(s):  
R. Matos ◽  
F. Ferreira ◽  
J. Saldanha Matos ◽  
A. Oliveira ◽  
L. David ◽  
...  
Keyword(s):  
Risk Management ◽  
Early Warning ◽  
Flood Risk ◽  
Early Warning System ◽  
Warning System ◽  
Flood Risk Management ◽  
Urban Drainage

scholarly journals Assessment of the warning system against floods on a rural area: the case of the lower Siret River (Romania)

2013 ◽  
Vol 13 (2) ◽  
pp. 409-416 ◽  
Author(s):  
F. Salit ◽  
L. Zaharia ◽  
G. Beltrando
Keyword(s):  
Risk Management ◽  
Rural Area ◽  
Early Warning ◽  
Flood Risk ◽  
Early Warning System ◽  
Warning System ◽  
Flood Risk Management ◽  
Event Tree ◽  
The Public ◽  
Risk Education

Abstract. The development of non-structural measures such as an early warning system, across the Europe, in flood risk management, requires a better understanding of the public involved and of the territory threatened. This paper aims to conduct an assessment of early warning and information to people with an analysis of the population's behaviour, presented in a form of an event tree. The objective is to understand the strengths and weaknesses of the warning system during a deadly flood in the lower Siret River (Romania) in 2005 and to demonstrate that each warning system has to be adapted to the territory in which it is effective. The behavioural model aims to determine to what extent the warning system can be improved but also to suggest ways to adapt risk education to the study area.

Early Warning System for SMEs as a Financial Risk Detector

2009 ◽  
pp. 220-238 ◽  
Author(s):  
Ali Serhan Koyuncugil
Keyword(s):  
Data Mining ◽  
Risk Management ◽  
Early Warning ◽  
Financial Management ◽  
Early Warning System ◽  
Quantitative Data ◽  
Financial Risk ◽  
Warning System ◽  
Theoretical Background ◽  
Effect Level

This chapter introduces an early warning system for SMEs (SEWS) as a financial risk detector which is based on data mining. In this study, the objective is to compose a system in which qualitative and quantitative data about the requirements of enterprises are taken into consideration, during the development of an early warning system. Furthermore, during the formation of system; an easy to understand, easy to interpret and easy to apply utilitarian model that is far from the requirement of theoretical background is targeted by the discovery of the implicit relationships between the data and the identification of effect level of every factor. Using the system, SME managers could easily reach financial management, risk management knowledge without any prior knowledge and expertise. In other words, experts share their knowledge with the help of data mining based and automated EWS.

scholarly journals Early warning system on extreme weather events for disaster risk reduction

2019 ◽  
pp. 80-87
Author(s):  
Jacipt Alexander Ramón-Valencia ◽  
Jordi Rafael Palacios-González ◽  
Germán Rircardo Santos-Granados ◽  
Jarol Derley Ramón-Valencia
Keyword(s):  
Risk Management ◽  
Risk Reduction ◽  
Early Warning ◽  
Early Warning System ◽  
Disaster Risk Reduction ◽  
Warning System ◽  
Disaster Risk ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Weather Events

The objective of this research was to propose a strategy based on the design and implementation of an early warning system (EWS) for extreme weather events. This project had the following phases: training for municipal and regional actors, preliminary technical diagnosis of the study areas, monitoring network, and the weather forecasts using numerical models WRF and GFS. This EWS is the result of the Macro-project EWS for Climate Events in the basins of the Pamplonita River and Zulia in the North of Santander (SATC), executed by the University of Pamplona and financed by the National Risk Management Unit (UNGRD) and the German Cooperation Agency (GIZ). The research concluded that the application of a disaster risk reduction strategy through an EWS for extreme weather events is an important tool and instrument for the planning of higher risk management because it helps anticipate disasters and consequently preserve lives.

Flooding due to torrential rainfall in Kumamoto, 2012

2014 ◽  
Vol 5 (3) ◽  
pp. 260-276
Author(s):  
Natainia Lummen ◽  
Sota Nakajo ◽  
Fumihiko Yamada
Keyword(s):  
Risk Management ◽  
Response Time ◽  
Flood Risk ◽  
Warning System ◽  
Flood Risk Management ◽  
Content Type ◽  
Flood Impacts ◽  
Sequence Of Events ◽  
Torrential Rainfall ◽  
Methodological Approaches

Purpose – This study aims to answer the following questions: what is the time scale in which areas downstream are affected by significant rainfall upstream, which areas are first affected, which areas are most affected, how effective is the current warning system in allotting an appropriate amount of time for evacuation, what has been the response time thus far utilizing the current warning system, how can this response time be im-proved using numerical simulation were addressed. The accu-rate prediction of floods and potential inundated areas is crucial for effective flood risk management and this paper analyses the model created for the July 12, 2012 Kumamoto flood event. Design/methodology/approach – Using GIS, LIDAR, SIS and levelling field survey data, the inundated areas were mapped; the sequence of events and the distribution of flood waters were recreated using numerical analysis and modelling. The inundated areas generated by the model were then compared to the actual inundated areas. Findings – The Tatsuda Ichi Chome area was completely inundated within 40 minutes of the first pooling of flood waters, whereas the Tatsuda Jin area was completely inundated within 20 minutes of the initial pooling of flood waters. Originality/value – This research provides empirical evidence of flood impacts and highlights the benefits that can be derived from incorporating accurate flood modelling results into flood risk management systems, as well as extends the methodological approaches of flood risk management.

A Proactive Approach to Flood Risk Management in Urban Drainage Systems

2001 ◽  
Author(s):  
Jadranka Milina ◽  
Klaus-Peter Nieschulz ◽  
Ingrid Selseth ◽  
Wolfgang Schilling
Keyword(s):  
Risk Management ◽  
Flood Risk ◽  
Flood Risk Management ◽  
Urban Drainage ◽  
Drainage Systems ◽  
Proactive Approach ◽  
Urban Drainage Systems

scholarly journals INCLUSIVE DISASTER RISK MANAGEMENT (DRM) FOR BERTAM VALLEY COMMUNITY

2018 ◽  
Vol 16 (6) ◽  
Author(s):  
Rahsidi Sabri Muda ◽  
Izawati Tukiman ◽  
Mohd Ramzi Mohd Hussain ◽  
Ismawi Zen
Keyword(s):  
Risk Management ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Disaster Risk ◽  
Disaster Risk Management ◽  
Dam Failure ◽  
Emergency Responder ◽  
Reduction Program ◽  
Community Preferences

This paper focuses on the inclusiveness of Disaster Risk Management. The study assesses proactive adaptation of Early Warning System (EWS) for Disaster Risk Reduction Program in Bertam Valley. The study aims to identify community preferences as an initiative to improve the effectiveness of the EWS system. In order to sustain the awareness and preparedness of EWS, continuous involvement by the community is necessary to make them resilient. Specific focus is given to Person with Disabilities (PwDs), elderly and children. Therefore, it is vital to understand the efficient methods to alarm PwDs, elderly and children during emergencies occurrence, which will focus on the implementation of types of alarm in EWS with regards to the early warning alarm and emergency notification. The finding from this paper is useful to assist local authorities and emergency responder in the formulation of warning and evacuation procedures to save the PwDs if dam failure is imminent.

Early Warning for Pre and Post Flood Risk Management by Using IoT and Machine Learning

2021 ◽  
Author(s):  
S. P. M. K. W. Ilukkumbure ◽  
V. Y. Samarasiri ◽  
M. F. Mohamed ◽  
V. Selvaratnam ◽  
U. U. Samantha Rajapaksha
Keyword(s):  
Machine Learning ◽  
Risk Management ◽  
Early Warning ◽  
Flood Risk ◽  
Flood Risk Management
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