Adaptive neuro-fuzzy inference system for the prediction of monthly shoreline changes in northeastern Taiwan

Abstract

This study intends to model the shoreline change by investigating monthly shoreline position data collected from seven sandy beaches located at the Yilan County in Taiwan during 2004–2011. The harmonic analysis results indicate shorelines appear significantly periodic with great variation. The adaptive neuro-fuzzy inference system network (ANFIS) is configured with two scenarios, namely lumped and site-specific ones, to extract significant features of shoreline changes for making shoreline position predictions in the next year. The lumped models for all stations are first investigated based on a number of possible input information, such as month, location, and the maximum and mean wave heights. The results, however, are not as favorable as expected, and wave heights do not contribute to modeling due to their high variability. Consequently, a site-specific model is constructed for each station, with its current position and nearby stations׳ positions as model inputs, to predict its shoreline position in the next year. Compared with the harmonic analysis and the autoregressive exogenous (ARX) model, the ANFIS model produces more accurate prediction results. The results indicate that the constructed ANFIS models can accurately predict shoreline changes and can serve as a valuable tool for future coastline erosion warning and management.