## 5 Ocean-Based Climate Change Solutions Enhanced by DL
### Adaptation Strategies Supported by DL
#### 5.2.1 Strengthening Coastal Protection with DL  
Coastal areas are crucial to both human societies and natural ecosystems. They are one of the most densely populated regions, with 38% of the world's population living within 100 km of the coast and nearly 45% residing within 150 km [323]. Economically, these areas host major cities, ports, and industries that drive global trade and commerce. Ecologically, they support invaluable ecosystems like mangroves, salt marshes, and coral reefs, which play a critical role in biodiversity conservation. However, coastal regions are highly vulnerable to climate change impacts, including sea level rise, floods, and ocean acidification, emphasizing the urgent need for effective adaptation strategies [324; 325]. Flooding, or inundation, is a major threat to coastal areas, intensified by sea level rise, storms, and extreme weather. These events can cause extensive damage, destroying homes, infrastructure, and ecosystems, and leading to substantial economic losses. To address this challenge, adaptation strategies include constructing flood defenses such as seawalls, levees, and storm surge barriers to safeguard urban areas and critical infrastructure. Advanced drainage systems and managed retreat--relocating vulnerable communities to safer locations--are also essential to reducing flood-related impacts [326]. Besides, restoring natural ecosystems like wetlands and mangroves plays a key role, as they act as natural buffers to absorb wave energy and reduce erosion. Moreover, real-time monitoring and early warning systems further enhance resilience by enabling timely responses to flooding events. Combined with remote sensing data, DL is a powerful tool for flood monitoring and prediction [327]. Remote sensing programs like Landsat, MODIS, and Sentinel provide real-time, high-resolution data on coastal areas. By applying DL algorithms to these datasets, researchers can accurately analyze flood impacts and predict their variations, contributing to the development of more effective adaptation strategies for coastal regions. In flood inundation mapping, DL models such as CNNs can effectively extract features from satellite images to identify flood-affected areas. For example, [PERSON] et al. [300] used a CNN integrated with multisource data fusion to generate flood maps at a 30 m resolution. The model employed three parallel CNN branches to process datasets including Landsat imagery, dual-polarized SAR data, and LiDAR-derived elevation data. Features extracted from these datasets were concatenated and fused for classification. Experiments were conducted on flooding caused by Hurricane Matthew in October 2016 along the southeast U.S. coast, achieving an OA of 97% and showing strong agreement with flood maps from the Coastal Emergency Risk Assessment. [PERSON] et al. [301] proposed a DL-based coastal inundation mapping approach using dual-polarization Sentinel-1 SAR data. Their model, built on a modified U-Net architecture, incorporated physics-aware inputs and a spatial dropout layer for regularization. This approach was applied to analyze flooding caused by Hurricane Harvey in 2017 near Houston, Texas. It achieved an F\({}_{1}\) score of 0.88, identifying approximately 4000 km\({}^{2}\) of inundated areas, with 76% affecting agricultural lands. The study demonstrated the advantages of integrating SAR data with DL techniques for effective flood monitoring, particularly during extreme weather events. Flood inundation mapping offers a detailed visualization of areas impacted by flooding, while flood prediction emphasizes forecasting potential flood events before they occur [303]. By combining historical patterns, real-time environmental data, and advanced DL models, researchers can predict future flooding events, supporting timely and effective adaptation strategies. [PERSON] et al. [328] utilized a CNN model to predict spatiotemporal variations in flood inundation extent and depth in eastern coastal regions of China.