## 4 Impact of Climate Change on Oceans: Leveraging DL for Monitoring and Analysis
### Understanding Impacts on Physical Properties via DL
#### 4.1.4 Predicting Sea Level Rise with DL  
Global sea level rise refers to the ongoing increase in average sea levels, driven primarily by two factors: ice melting and thermal expansion. As atmospheric temperatures rise, the melting glaciers and continental ice masses release large amounts of freshwater into the oceans. Simultaneously, the warming atmosphere causes thermal expansion, where the expansion of saline seawater molecules increases ocean volume. According to the IPCC, the global mean sea level rose at a rate of approximately 3.6 mm per year between 2006 and 2015, about 2.5 times faster than the 1.4 mm per year observed during the previous century. Of this rise, ice sheets and glaciers contributed 1.8 mm per year, while thermal expansion accounted for 1.4 mm per year [167]. Sea level rise has significant impacts on both human communities and natural ecosystems. For human communities, many of the world's largest cities are located near coastlines, making them highly vulnerable to disasters such as flooding and shoreline erosion caused by rising seas. Meanwhile, natural ecosystems face severe threats from rising sea levels, such as the degradation and loss of coastal habitats. Accurate sea level data is essential for addressing these challenges and can be collected through two primary methods: in situ tide gauges and remote sensing satellite altimetry [184]. Tide gauges, positioned along coastlines worldwide, measure relative sea level variations caused by tides, waves, and coastal storms at specific locations. In contrast, satellite altimetry employs radar to measure the distance between satellites and the ocean surface, providing a global perspective. Together, these complementary datasets from tide gauges and satellite altimetry provide accurate measurements of both regional and global sea level rise. Building on these rich datasets, DL models have become powerful tools for analyzing and predicting future sea level rise by capturing intrinsic relationships within the data. These models are capable of forecasting both short-term and long-term sea level variations. Short-term variations, which occur daily, are influenced by phenomena such as tides [185] and storm surges [186; 187]. [PERSON] et al. [161] developed an MLP to address missing tidal observations caused by mechanical failures or storm disturbances. Using data from Taiwanese ports, the model imputed missing values and predicted future tide levels, outperforming traditional statistical models and ML methods. [PERSON] et al. [188] introduced C1 PKNet, a CNN-based model combined with principal component analysis and k-means clustering, demonstrating computational efficiency and accuracy in predicting peak storm surges during hurricanes. Similarly focusing on storm surge-induced sea level variations, [PERSON] et al. [189] proposed a ConvLSTM framework that combines CNNs for capturing spatial correlations with LSTMs for modeling temporal dynamics, outperforming Gaussian Process models in storm surge prediction. In addition, GANs have been applied to storm surge modeling. [PERSON] et al. [163] developed a GAN-based approach to transform parametric model outputs into more realistic atmospheric forcing fields. The model was trained on 34 historical typhoon-induced storm surge events from 1981 to 2012 and evaluated using four recent storm surge events. The results showed that storm surge predictions based on GAN-generated forcing fields significantly outperformed those from the original parametric models. Long-term sea level variations, occurring over months to years, provide valuable insights into gradual sea level trends. DL models, especially CNNs and RNNs, are particularly effective in analyzing these trends by extracting spatial and temporal patterns from large datasets, enabling more accurate predictions of future sea level changes [190; 191]. For instance, [PERSON] et al. [164] proposed a method combining Gaussian process regression with RNNs to predict coastal sea level variability and quantify associated uncertainties.