Application of Nonlinear Autoregressive Neural Network Model to Forecast Local Mean Sea Level

Abstract

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.