Application of Nonlinear Autoregressive Neural Network Model to Forecast Local Mean Sea Level
DOI:
https://doi.org/10.32734/jocai.v6.i2-8975Abstract
The primary purpose of this study was to apply the nonlinear autoregressive neural network to model the long-term records of the monthly mean sea level from January 1978 to October 2020 at Grand Isle, Louisiana, as extracted from the National Oceanic and Atmospheric Administration Tides and Currents database. In this study, the empirical results revealed that the Bayesian Regularization algorithm was the best-suit training algorithm for its high regression R-value and low mean square error compared to the Levenberg-Marquardt and Scaled Conjugate Gradient algorithms for the nonlinear autoregressive neural network. Understanding past sea levels is important for the analysis of current and future sea level changes. In order to sustain these observations, research programs utilizing the existing data should be able to improve our understanding and significantly narrow our projections of ffuture sea-level changes.
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J. E. Neumann, G. Yohe, R. Nicholls, and M. Manion, Sea-Level Rise & Global Climate Change: A Review of Impacts to U.S. Coasts, The Center for Climate and Energy Solutions prepared for the Pew Center on Global Climate Change, 38 pages, 2020. [Online] Available: https://www.c2es.org/document/sea-level-rise-global-climate-change-a-review-of-impacts-to-u-s-coasts/ [Accessed: January 25, 2022]
J. A. Church, N. J. White, R. Coleman, K. Lambeck, and J. X. Mitrovica, Estimates of the regional distribution of sea level rise over the 1950-2000 period, Journal of Climate, 17(13), pp. 2609-2625, 2004.
J. A. Church, and N. J. White, A 20th century acceleration in global sea-level rise, Geophysical Research Letters, 33, L01602, pp. 1-4, 2006.
J. A, Church, and N. J. White, Sea-level rise from the late 19th to the early 21st century, Surveys in Geophysics, 32, pp. 585-602. 2011.
IPCC, Climate Change 2014: Synthesis Report, Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)], IPCC, Geneva, Switzerland, 151 pages, 2014. [Online] Available: https://www.ipcc.ch/report/ar5/syr/ [Accessed: January 25, 2022].
U.S. Global Change Research Program (USGCRP), Climate science special report: Fourth National Climate Assessment, Volume I, [D. J. Wuebbles, D. W. Fahey, K. A. Hibbard, D. J. Dokken, B. C. Stewart, and T. K. Maycock (eds.)], U.S. Global Change Research Program, Washington, DC, USA, 470 pages, 2017. [Online] Available: https://science2017.globalchange.gov/ [Accessed: January 25, 2022].
J. A. Church, N. J. White, T. Aarup, W. S. Wilson, P. L. Woodworth, C. M. Domingues, J. R. Hunter, and K. Lambeck, Understanding global sea levels: past, present and future, Sustainability Science, 3, pp. 9-22, 2008.
A. Cazenave, and W. Llovel, Contemporary sea level rise, Annual Review of Marine Science, 2, pp. 145-173, 2010.
A. Cazenave, and G. Le. Cozannet, Sea level rise and its coastal impacts, Earth’s Future, 2, pp. 15–34, 2013.
B. P. Horton, R. E. Kopp, A. J. Garner, C. C. Hay, N. S. Khan, K. Roy, and T. A. Shaw, Mapping sea-level change in time, space, and probability, Annual Review of Environment and Resources, 43, pp. 481-521, 2018.
S. A. Kulp, and B. H. Strauss, New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding, Nature Communications, 10:4844, pp. 1-12, 2019.
M. Haasnoot, J. Kwadijk, J. van Alphen, D. Le Bars, B. van den Hurk, F. Diermanse, A. van der Spek, G. O. Essink, J. Delsman, and M. Mens, Adaptation to uncertain sea-level rise; how uncertainty in Antarctic mass-loss impacts the coastal adaptation strategy of the Netherlands, Environmental Research Letters, 15, 034007, pp. 1-15, 2020.
G. Foster, and P. T. Brown, Time and tide: analysis of sea level time series, Climate Dynamics, 45(1-2), pp. 291-308, 2014.
H. Visser, S. Dangendorf, and A. C. Petersen, A review of trend models applied to sea level data with reference to the “acceleration-deceleration debate”, Journal of Geophysical Research: Oceans, 120(6), pp. 3873-3895, 2015.
D. Bolin, P. Guttorp, A. Januzzi1, D. Jones, M. Novak, H. Podschwit, L. Richardson, A. S¨arkk¨a, C. Sowder, and A. Zimmerman, Statistical prediction of global sea level from global temperature, Statistica Sinica, 25, pp. 351-367, 2015.
P. K. Srivastava, T. Islam, S. K. Singh, G. P. Petropoulos, M. Gupta, and Q. Dai, Forecasting Arabian sea level rise using exponential smoothing state space models and ARIMA from TOPEX and Jason satellite radar altimeter data, Meteorological Applications, 23, pp. 633-639, 2016.
O. Makarynskyy, D. Makarynska, M. Kuhn, and W. E. Featherstone, Predicting sea level variations with artificial neural networks at Hillarys Boat Harbour, Western Australia. Estuarine, Coastal and Shelf Science, 61(2), pp. 351–360, 2004.
A. Braakmann-Folgmann, R. Roscher, S. Wenzel, B. Uebbing, and J. Kusche, Sea level anomaly prediction using recurrent neural networks, In Proceedings of the 2017 conference on Big Data from Space, pp. 297-300, 2017. [Online] Available: https://arxiv.org/abs/1710.07099 [Accessed: January 25, 2022].
W. Wang, and H. Yuan, A Tidal Level Prediction Approach Based on BP Neural Network and Cubic B-Spline Curve with Knot Insertion Algorithm, Mathematical Problems in Engineering, Volume 2018, Article ID 9835079, 9 pages, 2018. [Online] Available: https://doi.org/10.1155/2018/9835079 [Accessed: January 25, 2022].
N. Bruneau, J. Polton, J. Williams, and J. Holt, Estimation of global coastal sea level extremes using neural Networks, Environmental Research Letters, 15 (7), 074030, 11 pages, 2020.
M. H. Beale, M. T. Hagan, and H. B. Demuth, Deep learning ToolboxTM: getting started guide, Natick, MA: The MathWorks, Inc., 2019.
K. Levenberg, A method for the solution of certain non-linear problems in least squares, Quarterly of Applied Mathematics, 2(2), pp. 164–168, 1944.
D. W. Marquardt, An algorithm for least-squares estimation of nonlinear parameters, Journal of the Society for Industrial and Applied Mathematics, Vol. 11, No. 2, pp. 431-441, 1963.
H. P. Gavin, The Levenberg-Marquardt algorithm for nonlinear least squares curve-fitting problems, Department of Civil and Environmental Engineering Duke University, 19 pages, 2020. [Online] Available: http://people.duke.edu/~hpgavin/ce281/lm.pdf [Accessed: January 25, 2022].
D. J. C. MacKay, Bayesian Interpolation, Neural Computation, Vol. 4, No. 3, pp. 415–447, 1992.
E. Sariev, and G. Germano, Bayesian regularized artificial neural networks for the estimation of the probability of default, Quantitative Finance, Vol. 20, No. 2, pp. 311–328, 2020.
M. F. Moller, A scaled conjugate gradient algorithm for fast supervised learning, Neural Networks, Volume 6, Issue 4, pp. 525-533, 1993.
R. E. Kopp, B. P. Horton, A. C. Kemp, and C. Tebaldi, Past and future sea-level rise along the coast of North Carolina, USA, Climatic Change, 132, pp. 693–707, 2015.
R. E. Kopp, C. C. Hay, C. M. Little, and J. X. Mitrovica, Geographic variability of sea-level change, Current Climate Change Reports, 1, pp. 192–204, 2015.
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