scholarly journals Application of hybrid Kalman filter for improving water level forecast

2016 ◽  
Vol 18 (5) ◽  
pp. 773-790 ◽  
Author(s):  
Xuan Wang ◽  
Vladan Babovic
Keyword(s):  
Kalman Filter ◽  
Numerical Model ◽  
Model Updating ◽  
Unscented Kalman Filter ◽  
Initial Conditions ◽  
Data Driven ◽  
Parameter Uncertainties ◽  
Hybrid Data Assimilation ◽  
Complex Nonlinear Systems ◽  
Set Up

Numerical modeling is one of the popular means to simulate and forecast the state of oceanographic systems. However, it still suffers from some limitations, e.g., parameter uncertainties, simplification of model assumptions, and absence of data for proper boundary and initial conditions. This paper proposes a hybrid data assimilation scheme, which combines the Kalman filter (KF) with a data-driven model (local linear model (LM)), to directly correct numerical model outputs at locations without measurements. Two different types of KF (unscented Kalman filter and two-sample Kalman filter) are tested and compared. A local LM is utilized to describe the evolution of the model state and then assimilated into the KF. This in turns simplifies the application of KF for highly complex nonlinear systems such as the dynamic motion of Singapore regional water. The proposed scheme is first examined using a simple hypothetical bay experiment followed by an operational model of the Singapore Regional Model (SRM), in which both are set up in the Delft3D modeling environment. This combination of KF and data-driven model provides insights into the influence of different error covariance estimations on the model updating accuracy. This research also provides guidance to offline utilization of KF in updating of numerical model output.

scholarly journals General Model and SOC Estimation of Battery

2021 ◽  
Vol 15 ◽  
pp. 155-163
Author(s):  
Li Meng ◽  
Haipeng Guo ◽  
Xiaowei Zhao
Keyword(s):  
Kalman Filter ◽  
General Model ◽  
Unscented Kalman Filter ◽  
Measurement Data ◽  
Work Conditions ◽  
Filter Method ◽  
Soc Estimation ◽  
The Difference ◽  
Kalman Filter Method ◽  
Set Up

Monitoring the battery state is of great importance for the safety and normal of the systems which are powered by batteries. SOC (State of Charge) is one of the most important state parameters of battery. SOC cannot be measured directly. The Kalman filter algorithm is one of the techniques often applied to estimate SOC value. An accurate model is necessary for this algorithm. In this paper, a general SOC model is set up. It takes into account not only the difference between discharging and charging work conditions, but also the influence of the working atmosphere, such as temperature and discharging rate. Then based on this general model, unscented Kalman filter method is used to predict the SOC value. It can avoid the error which is caused by ignoring high-order terms, which is a shortcoming exist in the extended Kalman filter method. The simulation experiments prove the approach can get satisfactory results even when the measurement data is mixed with noise or the initial SOC value is not accurate.

Inversion Algorithm Based on the Unscented Kalman Filter for Inverse Heat Conduction Problems

2013 ◽  
Vol 705 ◽  
pp. 474-482
Author(s):  
Pan Chu
Keyword(s):  
Kalman Filter ◽  
Heat Conduction ◽  
Unscented Kalman Filter ◽  
Initial Conditions ◽  
Measurement Data ◽  
Measurement Information ◽  
Inverse Heat Conduction ◽  
Space Problem ◽  
Inversion Model ◽  
Inverse Heat Conduction Problems

The inverse heat conduction problems (IHCP) analysis method provides a promising approach for acquiring the thermal physical properties of materials, the boundary conditions and the initial conditions from the known temperature measurement data, where the efficiency of the inversion algorithms plays a crucial role in real applications. In this paper, an inversion model that simultaneously utilizes the process evolution information of the objects to be estimated and the measurement information is proposed. The original IHCP is formulated into a state-space problem, and the unscented Kalman filter (UKF) method is developed for solving the proposed inversion model. The implementation of the proposed method does not require the gradient vector, the Jacobian matrix or the Hessian matrix, and thus the computational complexity is decreased. Numerical simulations are implemented to evaluate the feasibility of the proposed algorithm. For the cases simulated in this paper, satisfactory results are obtained, which indicates that the proposed algorithm is successful in solving the IHCP.

scholarly journals Improving the Reliability of Probabilistic Multi-Step-Ahead Flood Forecasting by Fusing Unscented Kalman Filter with Recurrent Neural Network

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 578 ◽  
Author(s):  
Yanlai Zhou ◽  
Shenglian Guo ◽  
Chong-Yu Xu ◽  
Fi-John Chang ◽  
Jiabo Yin
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Recurrent Neural Network ◽  
Unscented Kalman Filter ◽  
Forecast Accuracy ◽  
Flood Forecasting ◽  
Processing Technique ◽  
Data Driven ◽  
Dependence Structure ◽  
Neural Network Approach

It is fundamentally challenging to quantify the uncertainty of data-driven flood forecasting. This study introduces a general framework for probabilistic flood forecasting conditional on point forecasts. We adopt an unscented Kalman filter (UKF) post-processing technique to model the point forecasts made by a recurrent neural network and their corresponding observations. The methodology is tested by using a long-term 6-h timescale inflow series of the Three Gorges Reservoir in China. The main merits of the proposed approach lie in: first, overcoming the under-prediction phenomena in data-driven flood forecasting; second, alleviating the uncertainty encountered in data-driven flood forecasting. Two commonly used artificial neural networks, a recurrent and a static neural network, were used to make the point forecasts. Then the UKF approach driven by the point forecasts demonstrated its competency in increasing the reliability of probabilistic flood forecasts significantly, where predictive distributions encountered in multi-step-ahead flood forecasts were effectively reduced to small ranges. The results demonstrated that the UKF plus recurrent neural network approach could suitably extract the complex non-linear dependence structure between the model’s outputs and observed inflows and overcome the systematic error so that model reliability as well as forecast accuracy for future horizons could be significantly improved.

scholarly journals A Bayesian optimized framework for successful application of unscented Kalman filter in parameter identification of MDOF structures

2021 ◽  
pp. 146134842110143
Author(s):  
Mohamadreza Sheibani ◽  
Ge Ou
Keyword(s):  
Kalman Filter ◽  
Parameter Identification ◽  
Unscented Kalman Filter ◽  
Initial Conditions ◽  
Coupling Effect ◽  
Experimental Tests ◽  
Optimization Method ◽  
Bayesian Optimization ◽  
Noise Parameters ◽  
Inclusive Approach

The success of the unscented Kalman filter can be jeopardized if the required initial parameters are not identified carefully. These parameters include the initial guesses and the levels of uncertainty in the target parameters and the process and measurement noise parameters. While a set of appropriate initial target parameters give the unscented Kalman filter a head start, the uncertainty levels and noise parameters set the rate of convergence in the process. Therefore, due to the coupling effect of these parameters, an inclusive approach is desired to maintain the chance of convergence for expensive experimental tests. In this paper, a framework is proposed that, via a virtual emulation prior to the experiment, determines a set of initial conditions to ensure a successful application of the online parameter identification. A Bayesian optimization method is proposed, which considers the level of confidence in the initial guesses for the target parameters to suggest the appropriate noise covariance matrices. The methodology is validated on a five-story shear frame tested on a shake table. The results indicate that, indeed, a trade-off can be made between the robustness of the online updating and the final parameter accuracy.

Ensemble Kalman Filter Data Assimilation in a 1D Numerical Model Used for Fog Forecasting

2010 ◽  
Vol 138 (5) ◽  
pp. 1792-1810 ◽  
Author(s):  
Samuel Rémy ◽  
Thierry Bergot
Keyword(s):  
Kalman Filter ◽  
Numerical Model ◽  
Ensemble Kalman Filter ◽  
Initial Conditions ◽  
Specific Humidity ◽  
Error Statistics ◽  
Background Error ◽  
Fog Forecasting ◽  
Low Clouds ◽  
The Impact

Abstract Because poor visibility conditions have a considerable influence on airport traffic, a need exists for accurate and updated fog and low-cloud forecasts. Couche Brouillard Eau Liquide (COBEL)-Interactions between Soil, Biosphere, and Atmosphere (ISBA), a boundary layer 1D numerical model, has been developed for the very short-term forecast of fog and low clouds. This forecast system assimilates local observations to produce initial profiles of temperature and specific humidity. The initial conditions have a great impact on the skill of the forecast. In this work, the authors first estimated the background error statistics; they varied greatly with time, and cross correlations between temperature and humidity in the background were significant. This led to the implementation of an ensemble Kalman filter (EnKF) within COBEL-ISBA. The new assimilation system was evaluated with temperature and specific humidity scores, as well as in terms of its impact on the quality of fog forecasts. Simulated observations were used and focused on the modeling of the atmosphere before fog formation and also on the simulation of the life cycle of fog and low clouds. For both situations, the EnKF brought a significant improvement in the initial conditions and the forecasts. The forecast of the onset and burn-off times of fogs was also improved. The EnKF was also tested with real observations and gave good results. The size of the ensemble did not have much impact when simulated observations were used, thanks to an adaptive covariance inflation algorithm, but the impact was greater when real observations were used.

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