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Accurate and timely water level predictions are essential for effective shoreline and coastal ecosystem management. As sea levels rise, the frequency and severity of coastal inundation events are increasing, causing significant societal and economic impacts. Predicting these events with sufficient lead time is essential for decision-makers to mitigate economic losses and protect coastal communities. While machine learning methods have been developed to predict water levels at specific sites, there remains a need for more generalized models that perform well across diverse locations. This study presents a robust deep learning model for predicting water levels at multiple tide gauge locations along the Gulf of Mexico, including the open coast, embayments, and ship channels, all near major ports. The selected architecture, Seq2Seq, achieves significant improvements over the existing literature. It meets the National Oceanic and Atmospheric Administration’s (NOAA) operational criterion, with the percentage of predictions within 15 cm for lead times up to 108 h at the tide gauges of Port Isabel (92.2%) and Rockport (90.4%). These results represent a significant advancement over current models typically failing to meet NOAA’s standard beyond 48 h. This highlights the potential of deep learning models to improve water level predictions, offering crucial support for coastal management and flood mitigation. 

Climate change and rising sea levels pose significant threats to coastal regions, necessitating accurate and timely forecasts. Current methods face limitations due to their inability to fully capture nonlinear complexities, high computational costs, gaps in historical data, and bridging the gap between short-term and long-term forecasting intervals. Our study addresses these challenges by combining advanced machine learning techniques to provide region-specific sea level predictions in the Mediterranean Sea. By integrating high-resolution sea surface temperature data spanning 40 years, we employed a tailored k-means clustering technique to identify regions of high variance. Using these clusters, we developed RNN-GRU models that integrate historical tide gauge data and sea surface height data, offering regional sea level predictions on timescales ranging from one month to three years. Our approach achieved the highest predictive accuracy, with correlation values ranging from 0.65 to 0.84 in regions with comprehensive datasets, demonstrating the model’s robustness. In areas with fewer tide gauge stations or shorter time series, our models still performed moderately well, with correlations between 0.51 and 0.70. However, prediction accuracy decreases in regions with complex geomorphology. Yet, all regional models effectively captured sea level variability and trends. This highlights the model’s versatility and capacity to adapt to different regional characteristics, making it invaluable for regional planning and adaptation strategies. Our methodology offers a powerful tool for identifying regions with similar variability and providing sub-regional scale predictions up to three years in advance, ensuring more reliable and actionable sea level forecasts for Mediterranean coastal communities. 

Automatically detecting the wet/dry shoreline from remote sensing imagery has many benefits for beach management in coastal areas by enabling managers to take measures to protect wildlife during high water events. This paper proposes the use of a modified HED (Holistically-Nested Edge Detection) architecture to create a model for automatic feature identification of the wet/dry shoreline and to compute its elevation from the associated DSM (Digital Surface Model). The model is generalizable to several beaches in Texas and Florida. The data from the multiple beaches was collected using UAS (Uncrewed Aircraft Systems). UAS allow for the collection of high-resolution imagery and the creation of the DSMs that are essential for computing the elevations of the wet/dry shorelines. Another advantage of using UAS is the flexibility to choose locations and metocean conditions, allowing to collect a varied dataset necessary to calibrate a general model. To evaluate the performance and the generalization of the AI model, we trained the model on data from eight flights over four locations, tested it on the data from a ninth flight, and repeated it for all possible combinations. The AP and F1-Scores obtained show the success of the model’s prediction for the majority of cases, but the limitations of a pure computer vision assessment are discussed in the context of this coastal application. The method was also assessed more directly, where the average elevations of the labeled and AI predicted wet/dry shorelines were compared. The absolute differences between the two elevations were, on average, 2.1 cm, while the absolute difference of the elevations’ standard deviations for each wet/dry shoreline was 2.2 cm. The proposed method results in a generalizable model able to delineate the wet/dry shoreline in beach imagery for multiple flights at several locations in Texas and Florida and for a range of metocean conditions.