Research Information System
JOURNAL OF COASTAL RESEARCH, no.**, pp.1-25, 2025 (SCI-Expanded)
Seabed observations are critical for representing wave
dynamics and understanding nearshore morphological changes. In this study,
high-resolution bathymetric maps were generated based on field measurements
conducted over a 2.5-year period along a coastal region in Rize, Turkey. A time
history–based forecasting approach was applied to predict future seabed depth
changes over multiple time horizons (2, 5, 10, 20, and 50 years) using
statistical models, including linear regression, cubic polynomial regression,
and the autoregressive integrated moving average method. Unlike many studies
that integrate hydrodynamic parameters such as wave action, sediment transport,
or storm surges, this research focused on direct point–based prediction models
using only limited-time observed depth values without incorporating external
variables. Even though this led to increased uncertainty, some applied models
failed to deliver reliable results in long-term forecasts. However, the
findings show that linear regression performed more consistently than the other
time-dependent models within the observed data set. The outcomes highlight the
promising potential of minimalist statistical approaches for bathymetric
forecasting and offer support for preliminary decision-making in coastal
planning, erosion risk assessment, and sediment evolution monitoring,
particularly in data-limited environments.