Using an ensemble Kalman filter method to calibrate parameters of a prediction model for chemical transport from soil to surface runoff

2020 ◽  
Author(s):  
Xiangbo Meng ◽  
Juxiu Tong ◽  
Bill X. Hu
Keyword(s):  
Kalman Filter ◽  
Prediction Model ◽  
Ensemble Kalman Filter ◽  
Surface Runoff ◽  
Chemical Transport ◽  
Filter Method ◽  
Kalman Filter Method

Adaptive Kalman Filter Based Aircraft Ground Icing Thickness Prediction

2012 ◽  
Vol 562-564 ◽  
pp. 1660-1667
Author(s):  
Zhi Wei Xing ◽  
Hui Zhang ◽  
Zhun Ren
Keyword(s):  
Kalman Filter ◽  
Prediction Model ◽  
Prediction Method ◽  
Theory And Practice ◽  
Filter Method ◽  
Support Vector ◽  
Aircraft Icing ◽  
Adaptive Kalman Filter ◽  
Thickness Prediction ◽  
Kalman Filter Method

The nonlinear dynamics model is used to describe the change of aircraft icing thickness and icing deformation accelerations is viewed as dynamic noise in this paper. Then, a dynamic prediction model of aircraft icing thickness is established with the theory of adaptive kalman filter. And the adaptive kalman filter method based aircraft icing thickness prediction model is employed to forecast aircraft ground icing thickness and compared with support vector machine, BP neural network prediction method. The result of the instance simulation and analysis indicates that the adaptive kalman filter method based aircraft icing thickness prediction posed in this paper is reliable, simple and rapid, and the model has high prediction precision which can realize real-time tracking and prediction and has definite value of both theory and practice.

scholarly journals Solution Estimation of Logistic Growth Model with Ensemble Kalman Filter Method

2014 ◽  
Vol 14 (2) ◽  
pp. 85
Author(s):  
Vianda Nuning Fitriani ◽  
Kosala Dwidja Purnomo
Keyword(s):  
Kalman Filter ◽  
Analytical Solution ◽  
Ensemble Kalman Filter ◽  
Nonlinear Models ◽  
Specific Form ◽  
Filter Method ◽  
Logistic Growth ◽  
Parabolic Form ◽  
Logistic Growth Model ◽  
Kalman Filter Method

Ensemble Kalman Filter (EnKF) can be applied for linear or nonlinear models. This paper is aimed to estimate the logistic growth of population models using EnKF. The estimation will be compared with the analytical solution. We assume that we can find the analytical solution of the models. The models is in the specific form i.e comparison between the population growth rate and the amount of population is in the parabolic form. The good estimation will be attained by choosing 100 as size of ensembles in EnKF. The result of estimation really so closed to the analytical solution. Keywords : Analytical solution, EnKF, ensemble

Real-time forecasting of fire in a two-story building using ensemble Kalman filter method

2018 ◽  
Vol 97 ◽  
pp. 19-28 ◽  
Author(s):  
Jie Ji ◽  
Chunxiang Liu ◽  
Zihe Gao ◽  
Liangzhu (Leon) Wang
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Ensemble Kalman Filter ◽  
Filter Method ◽  
Kalman Filter Method ◽  
Story Building
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