REAL-TIME FLOOD FORECASTING USING A PARTICLE FILTER COMBINED WITH THE RRI MODEL AND A RIVERBED EVOLUTION MODEL

2020 ◽  
Vol 76 (2) ◽  
pp. I_859-I_864
Author(s):  
Yosuke NAKAMURA ◽  
Shinji EGASHIRA ◽  
Koji IKEUCHI ◽  
Daiki KAKINUMA
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Flood Forecasting ◽  
Evolution Model

scholarly journals A real-time probabilistic channel flood-forecasting model based on the Bayesian particle filter approach

2017 ◽  
Vol 88 ◽  
pp. 151-167 ◽  
Author(s):  
Xingya Xu ◽  
Xuesong Zhang ◽  
Hongwei Fang ◽  
Ruixun Lai ◽  
Yuefeng Zhang ◽  
...  
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Flood Forecasting ◽  
Forecasting Model ◽  
Model Based ◽  
Filter Approach

Improving the real-time probabilistic channel flood forecasting by incorporating the uncertainty of inflow using the particle filter

2018 ◽  
Vol 30 (5) ◽  
pp. 828-840
Author(s):  
Xing-ya Xu ◽  
Xuesong Zhang ◽  
Hong-wei Fang ◽  
Rui-xun Lai ◽  
Yue-feng Zhang ◽  
...  
Keyword(s):  
Particle Filter ◽  
Real Time ◽  
Flood Forecasting ◽  
The Real

Development of a Real-Time Flood Forecasting System by Hydraulic Flood Routing

2001 ◽  
Author(s):  
Joo Heon Lee ◽  
Do Hun Lee ◽  
Sang Man Jeong ◽  
Eun Tae Lee
Keyword(s):  
Real Time ◽  
Flood Forecasting ◽  
Flood Routing ◽  
Forecasting System

Assessing the potential for real-time urban flood forecasting based on a worldwide survey on data availability

2013 ◽  
Vol 11 (7) ◽  
pp. 573-583 ◽  
Author(s):  
Jeanne-Rose René ◽  
Slobodan Djordjević ◽  
David Butler ◽  
Henrik Madsen ◽  
Ole Mark
Keyword(s):  
Real Time ◽  
Flood Forecasting ◽  
Data Availability ◽  
Urban Flood

A two-stage analogue model for real-time urban flood forecasting

2021 ◽  
Author(s):  
Chris Onof ◽  
Yuting Chen ◽  
Li-Pen Wang ◽  
Amy Jones ◽  
Susana Ochoa Rodriguez
Keyword(s):  
Real Time ◽  
Flood Forecasting ◽  
Image Feature ◽  
Two Stage ◽  
Analogue Model ◽  
Flood Prediction ◽  
Urban Flood ◽  
Radar Images ◽  
Proposed Model ◽  
Flood Maps

<p>In this work a two-stage (rainfall nowcasting + flood prediction) analogue model for real-time urban flood forecasting is presented. The proposed approach accounts for the complexities of urban rainfall nowcasting while avoiding the expensive computational requirements of real-time urban flood forecasting.</p><p>The model has two consecutive stages:</p><ul><li><strong>(1) Rainfall nowcasting: </strong>0-6h lead time ensemble rainfall nowcasting is achieved by means of an analogue method, based on the assumption that similar climate condition will define similar patterns of temporal evolution of the rainfall. The framework uses the NORA analogue-based forecasting tool (Panziera et al., 2011), consisting of two layers. In the <strong>first layer, </strong>the 120 historical atmospheric (forcing) conditions most similar to the current atmospheric conditions are extracted, with the historical database consisting of ERA5 reanalysis data from the ECMWF and the current conditions derived from the US Global Forecasting System (GFS). In the <strong>second layer</strong>, twelve historical radar images most similar to the current one are extracted from amongst the historical radar images linked to the aforementioned 120 forcing analogues. Lastly, for each of the twelve analogues, the rainfall fields (at resolution of 1km/5min) observed after the present time are taken as one ensemble member. Note that principal component analysis (PCA) and uncorrelated multilinear PCA methods were tested for image feature extraction prior to applying the nearest neighbour technique for analogue selection.</li> <li><strong>(2) Flood prediction: </strong>we predict flood extent using the high-resolution rainfall forecast from Stage 1, along with a database of pre-run flood maps at 1x1 km<sup>2</sup> solution from 157 catalogued historical flood events. A deterministic flood prediction is obtained by using the averaged response from the twelve flood maps associated to the twelve ensemble rainfall nowcasts, where for each gridded area the median value is adopted (assuming flood maps are equiprobabilistic). A probabilistic flood prediction is obtained by generating a quantile-based flood map. Note that the flood maps were generated through rolling ball-based mapping of the flood volumes predicted at each node of the InfoWorks ICM sewer model of the pilot area.</li> </ul><p>The Minworth catchment in the UK (~400 km<sup>2</sup>) was used to demonstrate the proposed model. Cross‑assessment was undertaken for each of 157 flooding events by leaving one event out from training in each iteration and using it for evaluation. With a focus on the spatial replication of flood/non-flood patterns, the predicted flood maps were converted to binary (flood/non-flood) maps. Quantitative assessment was undertaken by means of a contingency table. An average accuracy rate (i.e. proportion of correct predictions, out of all test events) of 71.4% was achieved, with individual accuracy rates ranging from 57.1% to 78.6%). Further testing is needed to confirm initial findings and flood mapping refinement will be pursued.</p><p>The proposed model is fast, easy and relatively inexpensive to operate, making it suitable for direct use by local authorities who often lack the expertise on and/or capabilities for flood modelling and forecasting.</p><p><strong>References: </strong>Panziera et al. 2011. NORA–Nowcasting of Orographic Rainfall by means of Analogues. Quarterly Journal of the Royal Meteorological Society. 137, 2106-2123.</p>

scholarly journals Flood Forecasting and Warning System Structures: Procedure and Application to a Small Urban Stream in South Korea

Water ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1571 ◽  
Author(s):  
Song ◽  
Park ◽  
Lee ◽  
Park ◽  
Song
Keyword(s):  
Decision Making ◽  
Water Level ◽  
Real Time ◽  
Lead Time ◽  
Flash Flood ◽  
Warning System ◽  
Flood Forecasting ◽  
Urban Stream ◽  
Forecasting And Warning ◽  
Flood Warnings

The runoff from heavy rainfall reaches urban streams quickly, causing them to rise rapidly. It is therefore of great importance to provide sufficient lead time for evacuation planning and decision making. An efficient flood forecasting and warning method is crucial for ensuring adequate lead time. With this objective, this paper proposes an analysis method for a flood forecasting and warning system, and establishes the criteria for issuing urban-stream flash flood warnings based on the amount of rainfall to allow sufficient lead time. The proposed methodology is a nonstructural approach to flood prediction and risk reduction. It considers water level fluctuations during a rainfall event and estimates the upstream (alert point) and downstream (confluence) water levels for water level analysis based on the rainfall intensity and duration. We also investigate the rainfall/runoff and flow rate/water level relationships using the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) and the HEC’s River Analysis System (HEC-RAS) models, respectively, and estimate the rainfall threshold for issuing flash flood warnings depending on the backwater state based on actual watershed conditions. We present a methodology for issuing flash flood warnings at a critical point by considering the effects of fluctuations in various backwater conditions in real time, which will provide practical support for decision making by disaster protection workers. The results are compared with real-time water level observations of the Dorim Stream. Finally, we verify the validity of the flash flood warning criteria by comparing the predicted values with the observed values and performing validity analysis.

scholarly journals A Robust Skewed Boxplot for Detecting Outliers in Rainfall Observations in Real-Time Flood Forecasting

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Chao Zhao ◽  
Jinyan Yang
Keyword(s):  
Real Time ◽  
Exponential Function ◽  
Flood Forecasting ◽  
Interquartile Range ◽  
Skewed Data ◽  
Symmetric Distributions ◽  
Contaminated Data ◽  
Forecasting System

The standard boxplot is one of the most popular nonparametric tools for detecting outliers in univariate datasets. For Gaussian or symmetric distributions, the chance of data occurring outside of the standard boxplot fence is only 0.7%. However, for skewed data, such as telemetric rain observations in a real-time flood forecasting system, the probability is significantly higher. To overcome this problem, a medcouple (MC) that is robust to resisting outliers and sensitive to detecting skewness was introduced to construct a new robust skewed boxplot fence. Three types of boxplot fences related to MC were analyzed and compared, and the exponential function boxplot fence was selected. Operating on uncontaminated as well as simulated contaminated data, the results showed that the proposed method could produce a lower swamping rate and higher accuracy than the standard boxplot and semi-interquartile range boxplot. The outcomes of this study demonstrated that it is reasonable to use the new robust skewed boxplot method to detect outliers in skewed rain distributions.

Sign in / Sign up

Export Citation Format

Share Document