Improving numerical forecast accuracy with ensemble Kalman filter and chaos theory: Case study on Ciliwung river model-Reference-Cited by-同舟云学术

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Improving numerical forecast accuracy with ensemble Kalman filter and chaos theory: Case study on Ciliwung river model

Published:2014-05 Issue: Volume:512 Page:540-548
ISSN:0022-1694
Container-title:Journal of Hydrology
language:en
Short-container-title:Journal of Hydrology

Author:

Sun Yabin^ORCID,Doan Chi Dung,Dao Anh Tuan,Liu Jiandong,Liong Shie-Yui

Funder

Willis Research Network

Publisher

Elsevier BV

Subject

Water Science and Technology

Reference27 articles.

1. Analysis of Observed Chaotic Data;Abaranel,1996

2. Computational Hydraulics;Abbot,1998

3. Error correction of a predictive ocean wave model using local model approximation;Babovic;J. Mar. Syst.,2005

4. Time Series Analysis: Forecasting and Control;Box,1976

5. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods;Cristianini,2000

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1. Morphometric analysis of Ciliwung river and identification of suitable locations to build artificial dams for flood mitigation;Environmental Quality Management;2021-11-16

2. Comparison of local and global approximators in multivariate chaotic forecasting of daily streamflow;Hydrological Sciences Journal;2020-03-03

3. Enhancement of chaotic hydrological time series prediction with real-time noise reduction using Extended Kalman Filter;Journal of Hydrology;2018-10

4. Watershed management strategies for flood mitigation: A case study of Jakarta's flooding;Weather and Climate Extremes;2018-09

5. Enhancing accuracy of autoregressive time series forecasting with input selection and wavelet transformation;Journal of Hydroinformatics;2016-03-19

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