## 5 Ocean-Based Climate Change Solutions Enhanced by DL
### Mitigation Strategies Using DL
#### 5.1.2 Advancing Low-Carbon Maritime Transportation Through AI  
Maritime transport, encompassing international and domestic shipping, plays a vital role in facilitating global passenger and freight movement. However, it is also a significant contributor to GHG emissions, releasing approximately 1 gigaton of CO2 equivalent annually--around 3% of global anthropogenic CO2 emissions [257]. To address this challenge, the International Maritime Organization (IMO) established an initial strategy in 2018, aiming to reduce annual GHG emissions by at least 50% by 2050 compared to 2008 levels. In 2023, the IMO introduced an updated and more ambitious strategy targeting net-zero GHG emissions around 2050. This strategy includes interim milestones to achieve a minimum reduction of 20% (with an aspiration of 30%) by 2030 and at least 70% (striving for 80%) by 2040 [284]. Achieving these goals requires innovative solutions focused on enhancing fuel efficiency, optimizing shipping routes, and transitioning to alternative fuels. DL technologies have emerged as pivotal tools in these efforts, offering advanced capabilities for fuel consumption prediction and route optimization. These advancements not only support emission reductions but also improve energy efficiency and promote sustainable maritime operations. Fuel consumption forecasting is a critical aspect of maritime transportation, as it directly influences energy efficiency and operational costs. Traditional physics-based methods, which rely on resistance and propulsion performance [285], face limitations in complex or extreme operating conditions due to their limited ability to account for dynamic environmental factors such as wind speed, wave height, and ocean currents. ML techniques, including Support Vector Regression (SVR), decision trees, RF, and XGBoost, have demonstrated remarkable improvements utilizing large datasets [286; 287; 288]. Compared to traditional ML models, deep neural networks can more effectively capture complex relationships, leading to improved predictive performance. Among these, LSTM networks are widely used for fuel consumption forecasting due to their ability to learn from historical data, including ship operational conditions, environmental factors, and prior consumption trends [289; 290]. [PERSON] et al. [291] conducted a comprehensive study by analyzing over 200 variables related to ship operations and environmental conditions, such as heading, sailing speed, trim, weather, and sea state. They first employed a decision tree model to identify the most critical factors influencing fuel consumption. Using these factors, they developed a bi-directional LSTM model enhanced with an attention mechanism to capture complex temporal dependencies and assign greater importance to key variables. This method demonstrated R2 values ranging from 0.7 to 0.9 across eight voyages, underscoring its potential for accurate fuel consumption forecasting. [PERSON] et al. [292] introduced a multi-task learning framework to predict the fuel oil consumption of both main and auxiliary engines simultaneously. The model utilized a shared bi-directional LSTM layer updated by both tasks, followed by two task-specific branches. Each branch incorporated a Transformer module with a multihead self-attention mechanism and dense layers for separate predictions. Evaluated on data from three vessels, the model achieved R2 values between 0.94 and 0.99 for the main engine, showcasing its effectiveness. Moreover, [PERSON] et al. [293] proposed a novel energy consumption prediction model that integrated TCN, GRU, and a multihead self-attention mechanism. To boost performance, they conducted a correlation analysis to identify key features such as shaft speed and shaft power. Training the model on these selected features resulted in an accuracy of 96%, outperforming existing approaches and highlighting the potential of advanced DL models in maritime energy forecasting.