Automated Hydro-Oceanographic Data Pipeline for Marine Spatial Decision Support (Prototype)

Abstract

The implementation of risk-based marine spatial licensing demands accurate hydro-oceanographic data to mitigate structural and ecological hazards. However, reliance on costly in-situ measurements or computationally heavy global models—which are often plagued by shallow-water anomalies and complex binary formats—poses significant challenges for local regulatory bodies. This study presents the development of a Hybrid Intelligent Decision Support System (HIDSS), a Python-based automated spatial data pipeline designed to bridge this technical gap. The backend architecture employs automated spatial clipping to eliminate land mask boundary noise and utilizes robust vectorized operations for high-speed, 20-year tidal hindcasting using eight major harmonic constituents. Wave climates, current circulation, and dynamic bathymetric profiles are instantaneously extracted and systematically compressed into a lightweight, Parquet-formatted Data Lake. Crucially, this highly structured storage seamlessly integrates with Large Language Models (LLMs) via API and Fuzzy Logic protocols, automatically translating massive numerical arrays into comprehensive descriptive analyses and quantitative risk indices. The developed the Prototype HIDSS platform successfully empowers evaluators to conduct instantaneous, evidence-based technical audits, fundamentally optimizing the efficiency, transparency, and scientific rigor of the marine spatial planning ecosystem.