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

Xing-ya Xu; Xuesong Zhang; Hong-wei Fang; Rui-xun Lai; Yue-feng Zhang; Lei Huang; Xiao-bo Liu

doi:10.1007/s42241-018-0110-x

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

Journal of Hydrodynamics ◽

10.1007/s42241-018-0110-x ◽

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

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

Journal of Japan Society of Civil Engineers Ser B1 (Hydraulic Engineering) ◽

10.2208/jscejhe.76.2_i_859 ◽

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

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

Environmental Modelling & Software ◽

10.1016/j.envsoft.2016.11.010 ◽

2017 ◽

Vol 88 ◽

pp. 151-167 ◽

Cited By ~ 20

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

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An Adaptive Unscented Particle Filter Algorithm through Relative Entropy for Mobile Robot Self-Localization

Mathematical Problems in Engineering ◽

10.1155/2013/567373 ◽

2013 ◽

Vol 2013 ◽

pp. 1-9 ◽

Cited By ~ 3

Author(s):

Wentao Yu ◽

Jun Peng ◽

Xiaoyong Zhang ◽

Shuo Li ◽

Weirong Liu

Keyword(s):

Nonlinear System ◽

Particle Filter ◽

State Estimation ◽

Real Time ◽

Relative Entropy ◽

Gaussian Noise ◽

The Real ◽

Unscented Particle Filter ◽

Non Gaussian ◽

Number Of Particles

Self-localization is a basic skill for mobile robots in the dynamic environments. It is usually modeled as a state estimation problem for nonlinear system with non-Gaussian noise and needs the real-time processing. Unscented particle filter (UPF) can handle the state estimation problem for nonlinear system with non-Gaussian noise; however the computation of UPF is very high. In order to reduce the computation cost of UPF and meanwhile maintain the accuracy, we propose an adaptive unscented particle filter (AUPF) algorithm through relative entropy. AUPF can adaptively adjust the number of particles during filtering to reduce the necessary computation and hence improve the real-time capability of UPF. In AUPF, the relative entropy is used to measure the distance between the empirical distribution and the true posterior distribution. The least number of particles for the next step is then decided according to the relative entropy. In order to offset the difference between the proposal distribution, and the true distribution the least number is adjusted thereafter. The ideal performance of AUPF in real robot self-localization is demonstrated.

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Application of Artificial Neural Networks for Accuracy Enhancements of Real-Time Flood Forecasting in the Imjin Basin

Water ◽

10.3390/w10111626 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1626 ◽

Cited By ~ 10

Author(s):

Aida Jabbari ◽

Deg-Hyo Bae

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Real Time ◽

Bias Correction ◽

Weather Prediction ◽

Statistical Error ◽

Flood Forecasting ◽

The Real ◽

Model Complex ◽

Artificial Neural

Hydrometeorological forecasts provide future flooding estimates to reduce damages. Despite the advances and progresses in Numerical Weather Prediction (NWP) models, they are still subject to many uncertainties, which cause significant errors forecasting precipitation. Statistical postprocessing techniques can improve forecast skills by reducing the systematic biases in NWP models. Artificial Neural Networks (ANNs) can model complex relationships between input and output data. The application of ANN in water-related research is widely studied; however, there is a lack of studies quantifying the improvement of coupled hydrometeorological model accuracy that use ANN for bias correction of real-time rainfall forecasts. The aim of this study is to evaluate the real-time bias correction of precipitation data, and from a hydrometeorological point of view, an assessment of hydrological model improvements in real-time flood forecasting for the Imjin River (South and North Korea) is performed. The comparison of the forecasted rainfall before and after the bias correction indicated a significant improvement in the statistical error measurement and a decrease in the underestimation of WRF model. The error was reduced remarkably over the Imjin catchment for the accumulated Mean Areal Precipitation (MAP). The performance of the real-time flood forecast improved using the ANN bias correction method.

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Embedded System Based Target Tracking

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.468-471.1605 ◽

2012 ◽

Vol 468-471 ◽

pp. 1605-1608

Author(s):

Qi Yuan Sun ◽

Liu Sheng Li ◽

Zuo Liang Cao

Keyword(s):

Mobile Robot ◽

Embedded System ◽

Particle Filter ◽

Target Tracking ◽

Real Time ◽

Energy Cost ◽

Low Energy ◽

Experimental Results ◽

Moving Target ◽

The Real

A mobile robot based on embedded system can meet the requirement of low energy cost and miniaturization. An embedded system for moving target tracking is designed in this paper. By combining with wavelet transmission, an improved algorithm of particle filter with wavelet particles is proposed for tracking maneuver target, and then a scheme of optimization is also proposed to enhance the real-time property of the system. Experimental results show that the system can be suitable for real-time target tracking applications.

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