scholarly journals Flood Early Warning and Prediction System for Tributary Streams

2022 ◽  
Vol 5 (1) ◽  
pp. 93
Author(s):  
P. K. D. C. R. Panapitiya ◽  
D. Dhammearatchi ◽  
R. Perera
2021 ◽  
Vol 15 (1) ◽  
pp. 21-40
Author(s):  
Yedi Dermadi ◽  
Yoanes Bandung

It is very important for tsunami early warning systems to provide inundation predictions within a short period of time. Inundation is one of the factors that directly cause destruction and damage from tsunamis. This research proposes a tsunami impact prediction system based on inundation data analysis. The inundation data used in this analysis were obtained from the tsunami modeling called TsunAWI. The inundation data analysis refers to the coastal forecast zones for each city/regency that are currently used in the Indonesia Tsunami Early Warning System (InaTEWS). The data analysis process comprises data collection, data transformation, data analysis (through GIS analysis, predictive analysis, and simple statistical analysis), and data integration, ultimately producing a pre-calculated inundation database for inundation prediction and tsunami impact prediction. As the outcome, the tsunami impact prediction system provides estimations of the flow depth and inundation distance for each city/regency incorporated into generated tsunami warning bulletins and impact predictions based on the Integrated Tsunami Intensity Scale (ITIS-2012). In addition, the system provides automatic sea level anomaly detection from tide gauge sensors by applying a tsunami detection algorithm. Finally, the contribution of this research is expected to bring enhancements to the tsunami warning products of InaTEWS.


2021 ◽  
Vol 893 (1) ◽  
pp. 012027
Author(s):  
H N Rahmadini ◽  
U Efendi ◽  
A Rifani ◽  
A Kristianto

Abstract Convective clouds can be related to the development of intense storms that produce various extreme weather. The development of extreme weather could involve strong nonlinear interactions of many factors in the atmosphere, hence the ability to forecast extreme weather especially heavy rainfall and issued an early warning, becomes very important. BMKG has developed a time-lagged ensemble prediction system by utilizing the initial time difference, which is considered capable of providing data updates more closely to the forecasts final results. This study examines the percentile classification in the ensemble prediction system, to look for an extreme values distribution, then used it as extreme threshold. The extreme threshold was tested in a heavy rain case on February 15th 2019, on D-7, D-3, and D-1 of early warning dissemination. Based on this research, it was found that the use of the 90th and 95th percentile classification method was able to show a signal of extreme events on D-7 and D-3 events with a consistent probability pattern. In the D-1 prediction period, the probability value increases and the average precipitation value exceeds the extreme threshold.


Author(s):  
Xiaofei Li ◽  
Cesar L. Escalante ◽  
James E. Epperson ◽  
Lewell F. Gunter

1995 ◽  
Vol 34 (05) ◽  
pp. 518-522 ◽  
Author(s):  
M. Bensadon ◽  
A. Strauss ◽  
R. Snacken

Abstract:Since the 1950s, national networks for the surveillance of influenza have been progressively implemented in several countries. New epidemiological arguments have triggered changes in order to increase the sensitivity of existent early warning systems and to strengthen the communications between European networks. The WHO project CARE Telematics, which collects clinical and virological data of nine national networks and sends useful information to public health administrations, is presented. From the results of the 1993-94 season, the benefits of the system are discussed. Though other telematics networks in this field already exist, it is the first time that virological data, absolutely essential for characterizing the type of an outbreak, are timely available by other countries. This argument will be decisive in case of occurrence of a new strain of virus (shift), such as the Spanish flu in 1918. Priorities are now to include other existing European surveillance networks.


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