## 5 Ocean-Based Climate Change Solutions Enhanced by DL
### Mitigation Strategies Using DL
#### 5.1.1 Developing Ocean-Based Renewable Energy with DL  
Developing renewable energies such as wind, wave, and tidal energy is a key ocean-based mitigation strategy. These renewable energies provide a sustainable alternative to fossil fuels, significantly reducing GHG emissions. However, ensuring a stable and reliable energy supply remains a challenge, as it requires accurate and timely information about oceanic conditions. By integrating remote sensing technologies with DL methods, it is possible to provide high-resolution, real-time insights into ocean parameters like wind speed, wave height, and tidal patterns. These advancements not only optimize energy generation but also enhance the efficiency and reliability of ocean-based renewable energy systems. Sea surface wind speed is one of the most critical parameters for effective offshore wind energy system development, as it directly impacts energy generation. Accurate measurements of wind speed and direction are essential for optimizing wind farm efficiency and reliability. Advanced remote sensing technologies, such as GNSS-R [258], SAR [259], and X-band radar [260, 261], are widely utilized to deliver comprehensive insights into sea surface wind patterns. Among these technologies, GNSS-R has gained significant attention in recent years for its advantages of low cost, high resolution, and the ability to operate under various weather conditions. Combined with DL models, researchers can achieve precise wind speed estimation [262; 263; 264]. For example, CNNs, one of the most widely used DL architectures, excel at extracting spatial features from complex GNSS-R data [265]. [PERSON] et al. [69] utilized a 1D convolutional network to process delay waveforms extracted from the DDM in GNSS-R data. Their approach incorporated auxiliary features and employed a fully connected layer to estimate wind speed. To address the high bias observed in high wind speed ranges, the authors applied a cumulative distribution function matching technique for bias correction. When evaluated on CYGNSS data collected between 8 August and 31 December 2019, the method achieved an RMSE of 1.48 m/s and demonstrated an improvement of 7.19% for high wind speed estimation. [PERSON] et al. [266] utilized a 2D convolutional network to directly extract features from DDM images, also achieving accurate wind speed retrieval in low and medium wind speed ranges. Researchers have integrated CNNs with other architectures, such as LSTM and Transformers, to improve wind speed retrieval performance. [PERSON] et al. [267] designed a dual-branch model combining CNN and Transformer architectures for wind speed estimation from GNSS-R data. The CNN branch captures local spatial features, while the Transformer branch models long-range dependencies. This integration effectively extracts both local and global information, enhancing estimation accuracy. [PERSON] et al. [268] developed a hybrid model combining CNN and LSTM networks for wind speed estimation. The CNN extracts spatial patterns from input DDM, while the LSTM captures temporal dependencies. Tested on CYGNSS data from October to December 2021, the model achieved an RMSE of 1.34 m/s compared to ERA5 wind data, highlighting its effectiveness. In the context of SAR data, CNNs have proven effective in extracting intricate features for analyzing wind speed and direction [269; 270]. [PERSON] et al. [271] proposed a CNN-based model with residual and dense connections for retrieving hurricane wind speeds from Sentinel-1 SAR data. The model integrated texture features from the gray-level co-occurrence matrix, physical parameters linked to backscattering energy, and morphological features, achieving an RMSD of 1.72 m/s for wind speeds exceeding 75 m/s. For wind direction retrieval, [PERSON] et al. [272] employed a ResNet architecture with shortcut connections to estimate wind direction from Sentinel-1 SAR data.