Application of a Bayesian processor for predictive uncertainty estimation in real time flood forecasting

2010 ◽  
Author(s):  
G. Coccia ◽  
E. Todini
Keyword(s):  
Real Time ◽  
Flood Forecasting ◽  
Uncertainty Estimation ◽  
Predictive Uncertainty

scholarly journals Case Study: A Real-Time Flood Forecasting System with Predictive Uncertainty Estimation for the Godavari River, India

Water ◽  
2016 ◽  
Vol 8 (10) ◽  
pp. 463 ◽  
Author(s):  
Silvia Barbetta ◽  
Gabriele Coccia ◽  
Tommaso Moramarco ◽  
Ezio Todini
Keyword(s):  
Real Time ◽  
Flood Forecasting ◽  
Uncertainty Estimation ◽  
Predictive Uncertainty ◽  
Godavari River ◽  
Forecasting System

The multi temporal/multi-model approach to predictive uncertainty assessment in real-time flood forecasting

2017 ◽  
Vol 551 ◽  
pp. 555-576 ◽  
Author(s):  
Silvia Barbetta ◽  
Gabriele Coccia ◽  
Tommaso Moramarco ◽  
Luca Brocca ◽  
Ezio Todini
Keyword(s):  
Real Time ◽  
Flood Forecasting ◽  
Uncertainty Assessment ◽  
Predictive Uncertainty ◽  
Multi Temporal ◽  
Model Approach

Predictive Uncertainty Estimation Using Deep Learning for Soft Robot Multimodal Sensing

2021 ◽  
Vol 6 (2) ◽  
pp. 951-957
Author(s):  
Ze Yang Ding ◽  
Junn Yong Loo ◽  
Vishnu Monn Baskaran ◽  
Surya Girinatha Nurzaman ◽  
Chee Pin Tan
Keyword(s):  
Deep Learning ◽  
Uncertainty Estimation ◽  
Soft Robot ◽  
Predictive Uncertainty ◽  
Multimodal Sensing

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>

Real-time Uncertainty Estimation Based On Intermediate Layer Variational Inference

2021 ◽  
Author(s):  
Ahmed Hammam ◽  
Seyed Eghbal Ghobadi ◽  
Frank Bonarens ◽  
Christoph Stiller
Keyword(s):  
Real Time ◽  
Intermediate Layer ◽  
Uncertainty Estimation ◽  
Variational Inference ◽  
Time Uncertainty
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