Sea Level Change Analysis and Models Identification Based on Short Tidal Gauge Measurements in Alexandria, Egypt

Sea level change analysis and models identification are important factors used for coastal engineering applications. Moreover, sea level change modeling is used widely to evaluate and study shoreline and climate changes. This study intends to analyze and model Alexandria, Egypt sea level change by investigating yearly tide gauge data collected in a short duration (2008–2011). The time-frequency method was used to evaluate the meteorological noise frequencies. Two models were used to predict the time series data: Neural Network Autoregressive Moving Average (NNARMA) and Adaptive Neuro-Fuzzy Inference System (ANFIS). The time-frequency analysis and models identification results showed that no extreme events were detected for Alexandria point during the monitoring period. Therefore, the NNARMA and ANFIS models can be used to identify the sea level change. The estimates of the models were compared with the three different statistics, determination coefficient, root mean square errors, and auto-correlation function. Comparison of these results revealed that the NNARMA model performs better than the ANFIS model for the study area.