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

[1]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[2]  Quang Kim Nguyen,et al.  Simultaneous Solution for Flood Routing in Channel Networks , 1995 .

[3]  Paolo Lamberti,et al.  Flood propagation models for real-time forecasting , 1996 .

[4]  H. Fang,et al.  Three-Dimensional Mathematical Model of Suspended-Sediment Transport , 2000 .

[5]  Y. Zong,et al.  The 1998 Flood on the Yangtze, China , 2000 .

[6]  I. Saavedra,et al.  Dynamic Wave Study of Flow in Tidal Channel System of San Juan River , 2003 .

[7]  H. Fang,et al.  Three-dimensional calculations of flow and suspended sediment transport in the neighborhood of the dam for the Three Gorges Project (TGP) reservoir in the Yangtze River , 2003 .

[8]  Li Yu-rong.  Real time updating of hydrodynamic model by using Kalman filter , 2005 .

[9]  Henrik Madsen,et al.  Adaptive state updating in real-time river flow forecasting—a combined filtering and error forecasting procedure , 2005 .

[10]  Kuolin Hsu,et al.  Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter , 2005 .

[11]  A. Weerts,et al.  Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall‐runoff models , 2006 .

[12]  Ming-Hsi Hsu,et al.  Dynamic Routing Model with Real-Time Roughness Updating for Flood Forecasting , 2006 .

[13]  Stefania Tamea,et al.  Verification tools for probabilistic forecasts of continuous hydrological variables , 2006 .

[14]  Yuqiong Liu,et al.  Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework , 2007 .

[15]  Peter M. Atkinson,et al.  Flood inundation model updating using an ensemble Kalman filter and spatially distributed measurements , 2007 .

[16]  D. Lettenmaier,et al.  Prospects for river discharge and depth estimation through assimilation of swath‐altimetry into a raster‐based hydrodynamics model , 2007 .

[17]  M. Hanif Chaudhry,et al.  Open-Channel Flow , 2007 .

[18]  W. Chuan-hai.  Application of Real-time Roughness Updating Based on Hydrodynamic Model in Flood Forecasting , 2008 .

[19]  H. Moradkhani.  Hydrologic Remote Sensing and Land Surface Data Assimilation , 2008, Sensors.

[20]  P. Bickel,et al.  Obstacles to High-Dimensional Particle Filtering , 2008 .

[21]  Xi Chen,et al.  Kalman Filtering Correction in Real-Time Forecasting with Hydrodynamic Model , 2008 .

[22]  P. Bates,et al.  Optimal Cross-Sectional Spacing in Preissmann Scheme 1D Hydrodynamic Models , 2009 .

[23]  P. Bates,et al.  Progress in integration of remote sensing–derived flood extent and stage data and hydraulic models , 2009 .

[24]  Peter M. Atkinson,et al.  Evaluating the utility of the ensemble transform Kalman filter for adaptive sampling when updating a hydrodynamic model , 2009 .

[25]  Zhaoyin Wang,et al.  Strategies for managing reservoir sedimentation , 2009 .

[26]  Guangtao Fu,et al.  Classified real-time flood forecasting by coupling fuzzy clustering and neural network , 2010 .

[27]  R. De Keyser,et al.  Towards the sequential assimilation of SAR-derived water stages into hydraulic models using the Particle Filter: proof of concept , 2010 .

[28]  Jeffrey L. Anderson.  A Non-Gaussian Ensemble Filter Update for Data Assimilation , 2010 .

[29]  Dong HAN,et al.  A Coupled 1-D and 2-D Channel Network Mathematical Model Used for Flow Calculations in the Middle Reaches of the Yangtze River , 2011 .

[30]  G. Lannoy,et al.  Assimilating SAR-derived water level data into a hydraulic model: A case study , 2011 .

[31]  Pierfranco Costabile,et al.  Analysis of One-Dimensional Modelling for Flood Routing in Compound Channels , 2012, Water Resources Management.

[32]  Y. Tachikawa,et al.  Applying sequential Monte Carlo methods into a distributed hydrologic model: lagged particle filtering approach with regularization , 2011 .

[33]  Soroosh Sorooshian,et al.  Evolution of ensemble data assimilation for uncertainty quantification using the particle filter‐Markov chain Monte Carlo method , 2012 .

[34]  H. Fang,et al.  Flood management selections for the Yangtze River midstream after the Three Gorges Project operation , 2012 .

[35]  Shaofeng Liu,et al.  Improved regional hydrologic modelling by assimilation of streamflow data into a regional hydrologic model , 2012, Environ. Model. Softw..

[36]  S. Calmant,et al.  Assimilating in situ and radar altimetry data into a large-scale hydrologic-hydrodynamic model for streamflow forecast in the Amazon , 2012 .

[37]  Jasper A. Vrugt,et al.  Hydrologic data assimilation using particle Markov chain Monte Carlo simulation: Theory, concepts and applications (online first) , 2012 .

[38]  D. Pasetto,et al.  Ensemble Kalman filter versus particle filter for a physically-based coupled surface-subsurface model , 2012 .

[39]  Seong Jin Noh,et al.  Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities , 2012 .

[40]  A. Weerts,et al.  On noise specification in data assimilation schemes for improved flood forecasting using distributed hydrological models , 2013 .

[41]  Ioana Popescu,et al.  Parametric and physically based modelling techniques for flood risk and vulnerability assessment: A comparison , 2013, Environ. Model. Softw..

[42]  Peter Salamon,et al.  Assimilation of MODIS Snow Cover Area Data in a Distributed Hydrological Model Using the Particle Filter , 2013, Remote. Sens..

[43]  Jaap Schellekens,et al.  The Delft-FEWS flow forecasting system , 2013, Environ. Model. Softw..

[44]  S. Noh,et al.  Ensemble Kalman Filtering and Particle Filtering in a Lag-Time Window for Short-Term Streamflow Forecasting with a Distributed Hydrologic Model , 2013 .

[45]  Yasuto Tachikawa,et al.  Simultaneous estimation of inflow and channel roughness using 2D hydraulic model and particle filters , 2013 .

[46]  Zhongbo Yu,et al.  Coupled Hydraulic and Kalman Filter Model for Real-Time Correction of Flood Forecast in the Three Gorges Interzone of Yangtze River, China , 2013 .

[47]  Ming Wang,et al.  Dual state-parameter optimal estimation of one-dimensional open channel model using ensemble Kalman filter , 2013 .

[48]  H. Madsen,et al.  A partial ensemble Kalman filtering approach to enable use of range limited observations , 2014, Stochastic Environmental Research and Risk Assessment.

[49]  D. Seo,et al.  Ensemble prediction and data assimilation for operational hydrology , 2014 .

[50]  L. Natale,et al.  Flood mapping using LIDAR DEM. Limitations of the 1-D modeling highlighted by the 2-D approach , 2015, Natural Hazards.

[51]  Fangjian Wang,et al.  An Improved Particle Filter Algorithm Based on Ensemble Kalman Filter and Markov Chain Monte Carlo Method , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[52]  Galina Merkuryeva,et al.  Advanced river flood monitoring, modelling and forecasting , 2015, J. Comput. Sci..

[53]  Pierfranco Costabile,et al.  Enhancing river model set-up for 2-D dynamic flood modelling , 2015, Environ. Model. Softw..

[54]  Zhijia Li,et al.  Coupling the k-nearest neighbor procedure with the Kalman filter for real-time updating of the hydraulic model in flood forecasting , 2016 .