Main Article Content
The primary purpose of this study was to demonstrate the role of time series model in predicting process and to pursue analysis of time series data using long-term records of monthly mean sea level from January 1978 to October 2020 at Grand Isle, Louisiana. Following the Box–Jenkins methodology, the ARIMA(1,1,1)(2,0,0) with drift model was selected to be the best fitting model for the time series, according to the lowest AIC value in this study. Empirically, the results revealed that the MLP neural network model performed better compared to the ARIMA(1,1,1)(2,0,0) with drift model at its smaller MSE value. Hence, the MLP neural network model not only can provided information which are important in decision making process related to the future sea level change impacts, but also can be employed in forecasting the future performance for local mean sea level change outcomes. Understanding past sea level is important for the analysis of current and future sea level changes. In order to sustain these observations, research programs utilizing the resulting data should be able to improve significantly our understanding and narrow projections of future sea level rise and variability.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Box, G.E.P., & Jenkins, G.M. 1970. Time series analysis: forecasting and control. Holden-Day, San Francisco.
Box, G.E.P., Jenkins, G.M., Reinsel,G.C., & Ljung, G.M. (2016). Time series analysis: forecasting and control (5th ed.). Hoboken, N.J.: John Wiley and Sons Inc.
[Braakmann-Folgmann, A., Roscher, R., Wenzel, S., Uebbing, B., & Kusche, J. (2017). Sea level anomaly prediction using recurrent neural networks. In Proceedings of the 2017 Conference on Big Data from Space, pp. 297-300.
Bruneau, N., Polton,J., Williams, J., & Holt, J. (2020). Estimation of global coastal sea level extremes using neural Networks. Environmental Research Letters, 15(7), 074030, 1-11.
Cazenave, A., & Llovel, W. (2010). Contemporary sea level rise. Annual Review of Marine Science, 2, 145-173.
Cazenave, A., & Cozannet, G.L. (2013). Sea level rise and its coastal impacts. Earth’s Future, 2, 15–34.
Church, J.A., White, N.J., Coleman, R., Lambeck, K., & Mitrovica, J.X. (2004). Estimates of the regional distribution of sea level rise over the 1950-2000 period. Journal of Climate, 17(13), 2609-2625.
Church, J.A., & White, N.J. (2006). A 20th century acceleration in global sea-level rise. Geophysical Research Letters, 33, L01602, 1-4.
Church, J.A., White, N.J., Aarup, T., Wilson, W.S., Woodworth, P.L., Domingues, C.M., Hunter, J.R., & Lambeck, K. (2008). Understanding global sea levels: past, present and future. Sustainability Science, 3, 9-22.
Church, J.A., & White, N.J. (2011). Sea-level rise from the late 19th to the early 21st century. Surveys in Geophysics, 32, 585-602.
Foster, G., & Brown, P.T. (2014). Time and tide: analysis of sea level time series. Climate Dynamics, 45, 1-2, 291-308.
Gardner, M.W., & Dorling, S.R. (1998). Artificial neural networks (the multilayer perceptron) - a review of applications in the atmospheric sciences. Atmospheric Environment, 32(14), 2627-2636.
Haasnoot, M., Kwadijk, J., Alphen, J., Bars, D., Hurk, B., Diermanse, F.,Spek, A., Essink, G.O., Delsman, J., & Mens, M. (2020). 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, 1-15.
Horton, B.P., Kopp, R.E., Garner, A.J., Hay, C.C. Khan, N.S., Roy, K., & Shaw, T.A. (2018). Mapping sea-level change in time, space, and probability. Annual Review of Environment and Resources, 43, 481-521.
IPCC. (2014). 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, PachauriR.K., & Meyer, L.A. (eds.)], IPCC, Geneva, Switzerland, 151 pp.
Kopp, R.E., Horton,B.P., Kemp, A.C., & Tebaldi, C. (2015). Past and future sea level rise along the coast of North Carolina, USA. Climatic Change, 132, 693–707.
Kopp, R.E., Hay, C.C., Little, C.M., & Mitrovica, J.X. (2015). Geographic variability of sea-level change. Current Climate Change Reports, 1, 192–204.
Kulp, S.A., & Strauss, B.H. (2019). New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding. Nature Communications, 10, 4844, 1-12.
Ljung, G.M., & Box, G.E.O. (1978). On a measure of lack of fit in time series models. Biometrika, 65(2), 297-303.
Makarynskyy, O., Makarynska, D., Kuhn, M., & Featherstone, W.E. (2004). Predicting sea level variations with artificial neural networks at Hillarys Boat Harbour, Western Australia. Estuarine. Coastal and Shelf Science, 61(2), 351–360.
Manel, S., Dias, J.M., Buckton, S.T., & Ormerod, S.J. (1999). Alternative methods for predicting species distribution: an illustration with Himalayan river birds. Journal of Applied Ecology, 36, 734–747.
Montgomery, D.C., Jennings, C.L., & Kulahci, M. (2008). Introduction to time series analysis and forecasting. Hoboken, N.J.: John Wiley & Sons. Inc.
Neumann, J.E., Yohe, G., Nicholls, R., & Manion, M. (2020). 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 pp.
Srivastava, P.K., Islam, T., Singh, S.K., Petropoulos, G.P., Gupta, M., & Dai, Q. (2016). Forecasting Arabian sea level rise using exponential smoothing state space models and ARIMA from TOPEX and Jason satellite radar altimeter data. Meteorological Applications, 23, 633-639.
U.S. Global Change Research Program (USGCRP). (2017). Climate science special report: Fourth National Climate Assessment, Volume I. [Wuebbles, D.J., Fahey, D.W., Hibbard, K.A., Dokken, D.J., Stewart, B.C., & Maycock, T.K. (eds.)], U.S. Global Change Research Program, Washington, DC, USA, 470 pp.
Visser, H., Dangendorf, S., & Petersen, A.C. (2015). A review of trend models applied to sea level data with reference to the “acceleration-deceleration debate”. Journal of Geophysical Research: Oceans, 120(6), 3873-3895.
Wang, W., & Yuan, H. (2018). A tidal level prediction approach based on BP neural network and Cubic B-Spline Curve with Knot Insertion Algorithm. Mathematical Problems in Engineering, 2018, Article ID 9835079, 9 pp.