## 5 Ocean-Based Climate Change Solutions Enhanced by DL
### Mitigation Strategies Using DL
#### 5.1.2 Advancing Low-Carbon Maritime Transportation Through AI
Optimizing shipping routes is another crucial strategy to reduce fuel consumption and, consequently, CO2 emissions [294; 295]. Building on the accurate prediction of engine load and corresponding fuel consumption using an artificial neural network, [PERSON] et al. [296] applied deep reinforcement learning techniques for route optimization. The study employed advanced reinforcement learning methods, including Deep Q-Network, Deep Deterministic Policy Gradient, and Proximal Policy Optimization, to analyze and optimize ship routes. In scenarios without time constraints, their approach successfully reduced fuel consumption by 6.64%, highlighting the potential of deep reinforcement learning to enhance route efficiency and lower emissions. \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Area** & **Reference** & **Application** & **Data** & **Model** & **Results** \\ \hline \multirow{8}{*}{Renewable Energy} & [PERSON] et al. [264] & Wind Speed Retrieval & CYGNSS, wind speed (ERA5, SMAP, SFMR, OSCAR) & RMSE = 1.031 m/s; bias = \(-\)0.0003 m/s. \\ \cline{2-5}  & [PERSON] et al. [268] & Wind Speed Retrieval & CYGNSS, ERA5 & CNN-LSTM CC = 0.82. \\ \cline{2-5}  & [PERSON] et al. [69] & Wind Speed Retrieval & CYGNSS, ERA5 & 1D-CNN & RMSE = 1.486 m/s; bias = \(-\)0.091 m/s; CC = 0.828. \\ \cline{2-5}  & [PERSON] et al. [271] & Wind Speed Retrieval & Sentinel-1 SAR; SFMR hurricane measurements; SMAP wind products & DCCN & RMSE = 2.61 m/s; CC = 0.95. \\ \cline{2-5}  & [PERSON] et al. [272] & Wind Direction Retrieval & Sentinel-1 SAR; ECMWF TC01279 HRES global model; satellite scatterometer (OSI-SAF); in situ wind measurements & ResNet & SAR vs. ECMWF: bias = \(-\)1.1°, SAR vs. Scatterometer: bias = 2.4°, SNR vs. in situ: bias = \(-\)4.6°. \\ \cline{2-5}  & [PERSON] et al. [270] & Wind Direction Retrieval & GF-3 SAR; EAR5 & Inception v3 & RMSE = 9.12°. \\ \cline{2-5}  & [PERSON] et al. [278] & SWH Estimation buoy-measured SWH and GRU & X-band marine radar images; integrating CNN and GRU & CGRU, a model and GRU & Rainless: RMSE = 0.29 m, CC = 0.93; Rainy: RMSE = 0.54 m, CC = 0.87. \\ \cline{2-5}  & [PERSON] et al. [279] & SWH Estimation buoy-measured SWH from X-band radar images; Triaxys directional wave buoys & TCN & RMSE = 0.24 m, bias = 0.07 m, CC = 0.94. \\ \hline \multirow{8}{*}{[PERSON] et al. [291]} & [PERSON] et al. [291] & Ship Fuel Consumption Prediction & Real-world operational data from bulk carrier & Bi-LSTM with attention & R\({}^{2}\) ranges from 0.71 to 0.94 across 8 different voyages. \\ \cline{2-5}  & [PERSON] et al. [293] & Ship Fuel Consumption Prediction & Operational data from a bulk carrier; environmental data from ECMWF and multihead attention & TGMA, a model combined with TCN, GRU, and MMSE = 0.012 g/s, R\({}^{2}\) = 0.96; Voyage 2: RMSE = 0.014 g/s, R\({}^{2}\) = 0.94. \\ \cline{2-5}  & [PERSON] et al. [292] & Ship Fuel Consumption Prediction & Operational data from three fishing ships & Bi-LSTM with self-attention & R\({}^{2}\) = 99.45\%, RMSE = 0.99, MAE = 0.36. \\ \cline{2-5}  & [PERSON] et al. [296] & Fuel Consumption Prediction \& Operational data from a container ship; dynamic weather data & ANN, DQN, DQPG, PPO & Fuel consumption prediction: RMSE = 0.097, R\({}^{2}\) = 0.989; Fuel consumption reduced by 6.64\%, 1.54\%, 1.07\% after route optimization. \\ \cline{2-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Selective DL models supporting ocean-based climate change solutions.