Modeling Coastal and Port Hydrodynamics Using Sparse Nonlinear Dynamic System Intelligent Algorithms

In this study, we investigate the usability of the sparse identification of nonlinear dynamics (SINDy) algorithm in modeling the mechanisms of coastal and port hydrodynamic processes. In the SINDy approach, the primary goal is to explain the drift paths of objects and particles determined through computational or measurement techniques with a minimal number of sparse component ordinary differential equations. We reconstruct the drift paths and time series by analyzing Lagrangian drifter data obtained through a floating buoy in the Pacific Ocean. With the obtained data, we specifically examine the applicability of the SINDy algorithm in modeling hydrodynamic effects in coastal and port hydrodynamics. We propose that the SINDy-based algorithmic approach can quickly and accurately predict region-specific coastal and port hydrodynamics equations in events and disasters of the specified type. Also, an assessment is provided about the practical applications, usage areas, and potential research areas related to our findings.