Construction of a Real-Time Forecast Model with Deep Learning Techniques for Coastal Engineering and Processes: Nested in a Basin Scale Suite of Models

Abstract

This study aims to develop a robust and adaptable real-time forecasting system for coastal and estuarine regions, considering the challenges posed by the limited or unavailable forecast data. A real-time forecast system has been designed to handle three distinct scenarios: forecast data from global models are available, unavailable, or intermittently unavailable. To address the challenge of data unavailability, this research proposes the application of a deep learning model (DLM). This study involves the construction of a high-resolution numerical model nested within global models. The performance of the DLM is evaluated by comparing the simulation results of the nested model generated using DLM-predicted data against those obtained using data from global models. The results demonstrate a high correlation and around 90% accuracy for up to the initial 5 days of the forecast. Even in the absence of hindcast data up to 4 days prior to the forecast beginning time, the real-time forecast with DLM can achieve an accuracy exceeding 70% of the forecast data from global models. However, direct application of the developed DLM to an unknown domain results in significantly lower performance, highlighting the importance of retraining the DLM with local data. Overall, this study presents a comprehensive approach to constructing a real-time forecasting system for coastal and estuarine regions that adeptly handles various data availability scenarios through the integration of machine learning techniques.