Sea-level Records Analysis with Improved Empirical Mode Decomposition (EMD) and Artificial Neural Networks (ANN)

摘要

In this study, we illustrate artificial signal tests and sea-level records analyses with improved empirical model decomposition (EMD) and artificial neural network (ANN) for predicting the non-linear process of sea-level in terms of predicting a non-linear intrinsic mode for missed data and a non-linear trend. The EMD is intuitive, direct, and adaptive method for decomposing a signal into intrinsic modes, and does not require any predetermined parametric functions for analyzing a non-linear and non-stationary data. The ANN is one of machine learning methods to estimate stationary or non-stationary patterns/values. In our analyses, an artificial signal and sea-levels are decomposed into intrinsic modes, and then mainly low frequency modes are tested with ANN for predicting missing parts and for estimating future variabilities. Our results show that the combination of improved EMD and ANN is highly capable of predicting non-linear processes of sea-levels and can be applicable not only for predicting a missing data but also for estimating long-term natural variabilities and a trend.