## 5 Ocean-Based Climate Change Solutions Enhanced by DL
### Mitigation Strategies Using DL
#### 5.1.3. DL Applications in Ocean Carbon Sink
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Area** & **Reference** & **Application** & **Data** & **Model** & **Results** \\ \hline \multirow{8}{*}{Fisheries} & [PERSON] et al. [310] & Fishing Ground & Operational records; & 3D CNN & Central fishing ground: \\  & & Prediction & environmental variables & Precision = 0.72, \\  & & & such as SST, Chl-a, SLA, SSS, & Recall = 0.80, \\  & & dissolved oxygen from CMS & F1-score = 0.76. \\ \cline{2-6}  & [PERSON] et al. [311] & Fishing Ground & Commercial catch records; & U-Net & OA = 89.90\%, \\  & & Prediction & SST (NOAA OceanWatch) & & Precision = 0.9125, \\  & & & & & Recall = 0.9005, \\  & & & & & F1-score = 0.9050. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._surface ocean variables, including SST, 10 m wind speeds, total heat flux, chlorophyll-a concentration, and ocean surface pCO\({}_{2}\). The study revealed declining DIC concentrations in the 1990s and 2000s, followed by increases since the 2010s, especially in the upper ocean. Seasonal variability was also observed, with the strongest carbon uptake occurring in summer due to heightened biological activity. [PERSON] et al. [298] investigated carbon flux dynamics using oceanic data, such as SST and salinity, spanning 1998 to 2018. By combining correlation analysis with a feed-forward neural network, they reconstructed pCO\({}_{2}\) grid data in the Southern Ocean. Their findings indicated an overall increase in the carbon sink capacity since 2000, with a temporary decline between 2010 and 2013, followed by a substantial rise. However, a recent study using a boosting ensemble feed-forward neural network highlighted that the uneven seasonal distribution of data led to an underestimation of surface ocean pCO\({}_{2}\), which caused an overestimation of the Southern Ocean's carbon sink over the past three decades [319]. Particulate organic carbon (POC), a key component of the biological pump, plays a vital role in the marine carbon cycle, and advanced data-driven methods have proven effective in tracking changes in these particles [320]. Tree-based approaches like RF and XGBoost have shown superior performance in estimating POC concentrations. [PERSON] et al. [321] compared XGBoost, SVM, and MLP models using remote sensing data from the Ocean Colour Climate Change Initiative (OC-CCI) combined with in situ measurements. Their findings demonstrated that ML models significantly outperformed the traditional blue-to-green band ratio algorithm, with XGBoost achieving the best overall performance and MLP excelling in optically complex waters. [PERSON] et al. [322] utilized geodetector to identify 20 key factors influencing POC concentrations, including temperature, salinity, and chlorophyll-a levels. They tested multiple models, including CNNs, attention-based neural networks, RF, and XGBoost, and found RF delivered the best performance, achieving a mean deviation of 2.73%. In addition, [PERSON] et al. [299] employed a U-Net architecture to predict deep-ocean particle distributions. By incorporating inputs such as temperature, vorticity, velocity, and sea surface height, their model effectively captured spatial and temporal variations in particle distribution from 200 m to 900 m depths at 100 m intervals.