E. Kalnay, Atmospheric Modeling, Data Assimilation and Predictability. Cambridge, MA, USA: Cambridge University Press, 2003.
J. Blum, F. X. De Dimet, and I. M. Navon, Data assimilation for geophysical fluids, in Hand-book of Numerical Analysis. Elsevier, 2005.
M. D’Elia, M. Perego, and A. Veneziani, A variational data assimilation procedure for the incompressible navier-stokes equations in hemodynamics, J. Sci. Comput., vol. 52, no. 2, pp. 340-359, 2012.
R. Potthast and P. B. Graben, Inverse problems in neural field theory, SIAM J. Appl. Dyn. Syst., vol. 8, no. 4, pp. 1405-1433, 2009.
R. E. Kalman, A new approach to linear filtering and prediction problems, Journal of Basic Engineering, vol. 82, no. 1, pp. 35-45, 1960.
D. Daescu and I. Navon, Sensitivity analysis in nonlinear variational data assimilation: Theoretical aspects and applications, in Advanced Numerical Methods for Complex Environmental Models: Needs and Availability, I. Farago, Z. Zlatev, eds. Sharjah, United Arab Emirates: Bentham Science Publishers, 2013.
D. Cacuci, I. Navon, and M. Ionescu-Bujor, Computational Methods for Data Evaluation and Assimilation. Boca Raton, FL, USA: CRC Press, 2013.
R. Arcucci, L. D’Amorea, J. Pistoia, R. Toumi, and A. Murli, On the variational data assimilation problem solving and sensitivity analysis, Journal of Computational Physics, vol. 335, pp. 311-326, 2017.
V. Babovic, M. Keijzer, and M. Bundzel, From global to local modelling: A case study in error correction of deterministic models, in Proceedings of the Fourth International Conference on Hydro Informatics, Iowa City, IA, USA, 2000.
V. Babovic, R. Caňizares, H. R. Jensen, and A. Klinting, Neural networks as routine for error updating of numerical models, Journal of Hydraulic Engineering, vol. 127, no. 3, pp. 181-193, 2001.
V. Babovic and D. R. Fuhrman, Data assimilation of local model error forecasts in a deterministic model, International Journal for Numerical Methods in Fluids, vol. 39, no. 10, pp. 887-918, 2002.
Y. Sun, V. Babovic, and E. S. Chan, Artificial neural networks as routine for error correction with an application in Singapore regional model, Ocean Dynamics, vol. 62, no. 5, pp. 661-669, 2012.
O. Makarynskyy, Improving wave predictions with artificial neural networks, Ocean Engineering, vol. 31, nos. 5&6, pp. 709-724, 2004.
D. I. Gopinath and G. Dwarakish, Wave prediction using neural networks at new Mangalore port along west coast of india, Aquatic Procedia, vol. 4, pp. 143-150, 2015.
P. Jain and M. Deo, Artificial intelligence tools to forecast ocean waves in real time, Open Ocean Engineering Journal, vol. 1, pp. 13-20, 2008.
A.-S. Chen and M. T. Leung, Regression neural network for error correction in foreign exchange forecasting and trading, Computers & Operations Research, vol. 31, no. 7, pp. 1049-1068, 2004.
M. Gevreya, I. Dimopoulosb, and S. Leka, Review and comparison of methods to study the contribution of variables in artificial neural network models, Ecological Modelling, vol. 160, no. 3, pp. 249-264, 2003.
Y. W. Foo, C. Goh, and Y. Li, Machine learning with sensitivity analysis to determine key factors contributing to energy consumption in cloud data centers, in 2016 International Conference on Cloud Computing Research and Innovations (ICCCRI), 2016, pp. 107-113.
N. Nichols, Mathematical concepts of data assimilation, in Data Assimilation, W. Lahoz, B. Khattatov, and R. Menard, eds. Springer, 2010.
E. N. Lorenz, Deterministric nonperiodic flow, in the Theory of Chaotic Attractors, B. R. Hunt, T. Li, J. A. Kennedy, H. E. Nusse, eds. Springer, 2004.
A. S. Lawless, Data assimiliation with the Lorenz equations, University of Reading, UK, 2002.