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Intelligent Identification of Ocean Parameters based on RBF Neural Networks

Volume 14, Number 2, February 2018, pp. 269-279
DOI: 10.23940/ijpe.18.02.p8.269279

Li Yuana, Wei Wub, Chuan Tianb, Wei Songb, Xinghui Caob, Lixin Liub

aDepartment of Physical Science, Hainan Medical University, Haikou, 571179, China
bInstitute of Deep-sea Science and Engineering, Chinese Academy of Sciences, Sanya, 572000, China



Abstract:

Ocean data assimilation is challenging because of interactive marine environmental parameters that are affected by macroscopic ocean dynamics. In order to overcome these challenges, a multi-variable assimilation scheme based on a Radial Basis Function (RBF) Neural Network is proposed in this paper. Relative influential parameters are considered as bounded time series variables so that they can be selected for nonlinear function approximating in the first stage. Then, a RBF Neural Network identification model is designed to simulate multiple interactive high-dimensional variables. This simulation is performed by applying proper hidden neurons. According to experimental results, this training method successfully approximates real circumstances. The identification accuracy and vibration are well constricted in the margin evaluated by 1.6×10-5.

 

References: 28

    1. J.L. Anderson, “Reducing Correlation Sampling Error in Ensemble Kalman Filter Data Assimilation,” Monthly Weather Review, vol. 144, no. 3, pp. 913–925, 2015.
    2. F. Bouttier and P. Courtier, “Data Assimilation Concepts and Methods March 1999,” Meteorological training course lecture series. ECMWF, 2002.
    3. J.S. Brain, Isaac M. Held. “An Assessment of Climate Feedbacks in Coupled Ocean–Atmosphere Models,” Journal of Climate, Vol. 19, pp. 3354-3357, 2006.
    4. L. Bopp, L. Resplandy, et al. “Multiple Stressors of Ocean Ecosystems in the 21st Century: Projections with CMIP5 models,” Biogeosciences, 10, 6225 -6245, 2013.
    5. M. Bocquet, H. Elbern, et al. “Data Assimilation in Atmospheric Chemistry Models: Current Status and Future Prospects for Coupled Chemistry Meteorology Models,” Atmospheric chemistry and physics, vol. 15, no. 10, pp. 5325–5358, 2015.
    6. M. Buehner, J. Morneau, and C. Charette, “Four-dimensional Ensemble-variational Data Assimilation for Global Deterministic Weather Prediction,” Nonlinear Processes in Geophysics, vol. 20, no. 5, pp. 669–682, 2013.
    7. D. Erdal and O. Cirpka, “Joint Inference of Groundwater-recharge and Hydraulic-conductivity Fields from Head Data Using the Ensemble Kalman filter,” Hydrology and Earth System Sciences, vol. 20, no. 1, pp. 555–569, 2016.
    8. C. A. Edwards, A. M. Moore, I. Hoteit, and B. D. Cornuelle, “Regional Ocean Data Assimilation,” Annual review of marine science, vol. 7, pp. 21–42, 2015.
    9. G. Evensen, “Data assimilation: the ensemble Kalman filter,” springer Science & Business Media, 2009.
    10. G. Ferri, M. Cococcioni, and A. Alvarez, “Mission Planning and Decision Support for Underwater Glider Networks: A sampling on-demand approach,” Sensors, vol. 16, no. 1, p. 28, 2015.
    11. S.M. Griffies, Michael Winton et al. “Impacts on Ocean Heat from Transient Mesoscale Eddies in a Hierarchy of Climate Models,” Journal of Climate, Vol. 28, pp. 952-970, 2015.
    12. I. Iermano, A. Moore, and E. Zambianchi, “Impacts of a 4-dimensional Variational Data Assimilation in a Coastal Ocean Model of Southern Tyrrhenian sea,” Journal of Marine Systems, vol. 154, pp. 157–171, 2016.
    13. C. James, C. Gennady, C. Xianhe, and G. Benjamin, “A Simple Ocean Data Assimilation Analysis of the Global Upper Ocean 1950-95. part i: Methodology,” Journal of Physical Oceanography, vol. 30, no. 2, pp.294–309, 2000.
    14. M. Kretschmer, B. R. Hunt, and E. Ott, “Data Assimilation Using a Climatologically Augmented Local Ensemble Transform Kalman filter,” Tellus A, vol. 67, pp. 1–5, 2015.
    15. Z.J. Li, James C. McWilliams, et al. “Coastal Ocean Data Assimilation Using a Multi-scale Three-dimensional Variational Scheme,” Ocean Dynamics, 65(7), 1001-1015, 2015.
    16. Y. Liu, H. Meier, and L. Axell, “Reanalyzing Temperature and Salinity on Decadal Time Scales Using the Ensemble Optimal Interpolation Data Assimilation Method and a 3D Ocean Circulation Model of the Baltic sea,” Journal of Geophysical Research: Oceans, vol. 118, no. 10, pp. 5536–5554, 2013.
    17. S. Mohanty, A. Chattopadhyay, P. Peralta, and S. Das, “Bayesian Statistic Based Multivariate Gaussian Process Approach for Offline/online Fatigue Crack Growth Prediction,” Experimental mechanics, vol. 51, no. 6, pp.833–843, 2011.
    18. P.A. Muscarella, M. Carrier, and H. Ngodock, “An Examination of a Multi-scale Three-dimensional Variational Data Assimilation Scheme in the Kuroshio Extension Using the Naval Coastal Ocean Model,” Continental Shelf Research, vol. 73, pp. 41–48, 2014.
    19. A. Mozaffari, K. A. Scott, S. Chenouri, and N. L. Azad, “A Modular Ridge Randomized Neural Network with Differential Evolutionary Distributor Applied to the Estimation of Sea Ice Thickness,” Soft Computing, pp. 1–25, 2016.
    20. P.R. Oke, G. B. Brassington, D. A. Griffin, and A. Schiller, “The Bluelink Ocean Data Assimilation System (bodas),” Ocean Modelling, vol. 21, no. 1, pp. 46–70, 2008.
    21. Q. Peng and C. Lei., “The Assimilation of Jason-2 Significant Wave Height Data in The North Indian Ocean Using the Ensemble Optimal Interpolation,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 1, pp. 287–297, August 2016.
    22. D. Paiva, R. C., M. T. Durand, and F. Hossain, “Spatiotemporal Interpolation of Discharge Across a River Network by Using Synthetic Swot Satellite Data,” Water Resources Research, vol. 51, no. 1, pp. 430–449, 2015.
    23. S. Suranjana, Moorthi, et al., “The Ncep Climate Forecast System Reanalysis,” Bulletin of the American Meteorological Society, vol. 91, no. 8, pp. 1015–1057, August 2010.
    24. N. Usui, Y. Fujii, K. Sakamoto, and M. Kamachii, “Development of a Four-dimensional Variational Assimilation System for Coastal Data Assimilation around Japan,” Monthly Weather Review, vol. 143, no. 10, pp. 3874–3892, 2015.
    25. C. Ubelmann, P. Klein, and L. L. Fu, “Dynamic Interpolation of Sea Surface Height and Potential Applications for Future High-resolution Altimetry Mapping,” Journal of Atmospheric and Oceanic Technology, vol. 32, no. 1, pp. 177–184, 2015.
    26. C. W. and D. Hill, “Deterministic Learning and Rapid Dynamical Pattern Recognition,” IEEE Transactions on Neural Networks, vol. 18, no. 3, pp. 617–630, 2007.
    27. W.Z. Zhang, M. Hao, M. Snir. Predicting HPC Parallel Program Performance Based on LLVM compiler. Cluster Computing, 20(2), 1179-1192, 2017.
    28. M. Zhang and F. Zhang, “E4dvar: Coupling an Ensemble Kalman filter with Four-dimensional Variational Data Assimilation in a Limited-area Weather Prediction Model,” Monthly Weather Review, vol. 140, no. 2, pp. 587–600, 2012.

       

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