Pattern Analysis of Periodic Sediment Elevation Data: A Decision Support Framework for Deposited Sediment Management in Coastal Environment

Abstract

This research explores sediment deposition patterns in coastal areas by employing synthetic data representing wandering set and recurrent patterns, utilizing Fourier transformation and power-law analysis to characterize frequency content and scaling behavior. The study employs fundamental models to interpret observed patterns and investigates the relevance of Poincaré's theorem in understanding recurrent patterns. Leveraging Python-based computation systems and open-source satellite data, the findings reveal contrasting behaviors in power-law exponents between scenarios, with wandering set patterns exhibiting scale-free characteristics and recurrent patterns displaying structured frequency distributions. This holistic approach advances scientific knowledge on sediment dynamics, informing evidence-based decision-making for effective sediment management and coastal resilience planning. Through its methodology and insights, the research contributes to understanding sediment deposition processes, elucidating underlying mechanisms driving coastal sedimentation, and providing practical approaches for monitoring and studying sediment dynamics in coastal environments, ultimately enhancing strategies for coastal management and conservation.