Short-Term Flood Prediction System

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Konrad Nering
Keyword(s):  
Flash Floods ◽  
Prediction System ◽  
Short Term ◽  
Data Set ◽  
Flood Prediction ◽  
Full Operation

AbstractThis paper describes a fully functional short-term flood prediction system. Its effect has been tested on watershed of Lubieńka river in Małopolska. To use this system it must have a data set also described in this paper. A modification of the system to adopt for predicting flash floods was described. Full operation of the system is shown on example of real flood on Lubieńka river in June 2011.

Design and FPGA Prototyping of a Flood Prediction System

2008 ◽  
Author(s):  
Sandeep Gandla ◽  
Waleed K. Al-Assadi ◽  
Sahra Sedigh ◽  
Raghu A. R. Rao
Keyword(s):  
Prediction System ◽  
Flood Prediction

scholarly journals Study on Flood Prediction System Based on Radar Rainfall Data

2008 ◽  
Vol 41 (11) ◽  
pp. 1153-1162 ◽  
Author(s):  
Won-Il Kim ◽  
Kyoung-Doo Oh ◽  
Won-Sik Ahn ◽  
Byong-Ho Jun
Keyword(s):  
Rainfall Data ◽  
Prediction System ◽  
Radar Rainfall ◽  
Flood Prediction

Flood Prediction System using IoT and LoRa Technologies

2021 ◽  
Author(s):  
G. Nitish Satya Sai ◽  
Kondreddy Sai Manikanta ◽  
Sri Ganesh Arjula ◽  
Reddy Sudheer ◽  
Bandi Narasimha Rao ◽  
...  
Keyword(s):  
Prediction System ◽  
Flood Prediction

scholarly journals On Location Privacy Protection Algorithm Based on Edge Computing and Caching

2021 ◽  
Vol 2138 (1) ◽  
pp. 012010
Author(s):  
Xiaobei Xu ◽  
Huaju Song ◽  
Kai Zhang ◽  
Liwen Chen ◽  
Yuwen Qian
Keyword(s):  
Markov Chain ◽  
Privacy Protection ◽  
Location Privacy ◽  
Transmission Delay ◽  
Mobile Users ◽  
Communication Overhead ◽  
Prediction System ◽  
Hit Rate ◽  
Protection Method ◽  
Location Privacy Protection

Abstract To resolve the communication overhead problem of anonymous users, we propose a location privacy protection method based on the cache technology. In particular, we first place the cache center on edge server nodes to reduce interaction between servers and users. In this way, the risk of privacy leaks can be reduced. Furthermore, to improve the caching hit rate, a prediction system based on Markov chain is designed to protect the trajectory privacy of mobile users. Simulations show that the algorithm can protect the privacy of users and reduce the transmission delay.

scholarly journals Building a Multimodel Flood Prediction System with the TIGGE Archive

2016 ◽  
Vol 17 (11) ◽  
pp. 2923-2940 ◽  
Author(s):  
Ervin Zsótér ◽  
Florian Pappenberger ◽  
Paul Smith ◽  
Rebecca Elizabeth Emerton ◽  
Emanuel Dutra ◽  
...  
Keyword(s):  
Land Surface ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Initial Conditions ◽  
Model Averaging ◽  
Surface Model ◽  
Prediction System ◽  
Flood Prediction ◽  
Forecast Performance ◽  
The Impact

Abstract In the last decade operational probabilistic ensemble flood forecasts have become common in supporting decision-making processes leading to risk reduction. Ensemble forecasts can assess uncertainty, but they are limited to the uncertainty in a specific modeling system. Many of the current operational flood prediction systems use a multimodel approach to better represent the uncertainty arising from insufficient model structure. This study presents a multimodel approach to building a global flood prediction system using multiple atmospheric reanalysis datasets for river initial conditions and multiple TIGGE forcing inputs to the ECMWF land surface model. A sensitivity study is carried out to clarify the effect of using archive ensemble meteorological predictions and uncoupled land surface models. The probabilistic discharge forecasts derived from the different atmospheric models are compared with those from the multimodel combination. The potential for further improving forecast skill by bias correction and Bayesian model averaging is examined. The results show that the impact of the different TIGGE input variables in the HTESSEL/Catchment-Based Macroscale Floodplain model (CaMa-Flood) setup is rather limited other than for precipitation. This provides a sufficient basis for evaluation of the multimodel discharge predictions. The results also highlight that the three applied reanalysis datasets have different error characteristics that allow for large potential gains with a multimodel combination. It is shown that large improvements to the forecast performance for all models can be achieved through appropriate statistical postprocessing (bias and spread correction). A simple multimodel combination generally improves the forecasts, while a more advanced combination using Bayesian model averaging provides further benefits.

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