## 5 Ocean-Based Climate Change Solutions Enhanced by DL
### Adaptation Strategies Supported by DL
#### 5.2.2 Improving Fisheries Management Leveraging DL  
Climate change significantly impacts global fisheries. Rising ocean temperatures cause fish to migrate to cooler waters, disrupting traditional fishing grounds and threatening marine biodiversity. Extreme marine events like ENSO and MHWs further exacerbate these challenges [329], with MHW-induced shifts in fish distribution occurring at rates four times greater than natural variability [330]. Effective adaptation strategies are needed to ensure the sustainability of fisheries in the face of climate change. These strategies include adjusting fishing operations to align with shifting fish populations, diversifying income sources to reduce dependence on fisheries, updating regulations to accommodate changing environmental conditions, and adopting advanced technologies to enhance sustainability and resilience [331]. Among these, advanced data-driven methodologies have emerged as essential tools for enhancing fisheries management, enabling more effective monitoring and forecasting. For fish monitoring, DL algorithms can process diverse datasets, including satellite imagery, underwater images, and acoustic sensor data, to track fish populations, assess their health, and identify migratory patterns. This allows for real-time stock monitoring, providing critical insights for resource management. In terms of prediction, DL models can analyze historical and real-time data to forecast future fish movements, offering early warnings of population shifts and reducing the risk of overfishing. These capabilities support fisheries in adapting to climate change, facilitating more efficient resource management. In fish identification and monitoring, cameras mounted on autonomous underwater vehicles, remotely operated vehicles, or vessels capture high-resolution optical images of marine life. These images are then analyzed using deep neural networks, which demonstrate exceptional capabilities in detecting and identifying fish species with remarkable accuracy [332,333]. Convolution-based computer vision models, such as Mask R-CNN, a state-of-the-art segmentation model, are one of the most commonly used models in this context. Utilizing over 400,000 underwater video clips collected via the SmartBay facility, [PERSON] et al. [334] created a comprehensive benchmark dataset for underwater marine species detection. The videos, recorded under diverse visibility and environmental conditions, were meticulously filtered to exclude clips lacking objects of interest. The selected samples were then manually annotated frame by frame. With this dataset, Mask R-CNN was trained and achieved a mean average precision (mAP) of 0.628, showcasing its effectiveness in detecting and classifying marine species in underwater environments. [PERSON] et al. [306] employed onboard cameras and deep neural networks to automate the analysis of images collected from tropical tuna purse-seine fishing vessels. Their approach involved two key steps: fish segmentation using Mask R-CNN and species classification paired with size estimation using ResNet. The model achieved classification accuracies exceeding 70% for all species, and its size distribution estimates closely matched official port measurements. [PERSON] et al. [307] presented an innovative coastal fisheries monitoring system to enhance fisheries management across the Pacific region. The system leveraged DL models, including YOLOv4 and ResNet101, to analyze images of fish and invertebrates, enabling accurate species identification along with length and weight measurements. In addition, the system incorporated essential fisheries data like catch rates, locations, and volumes, to support efficient and adaptive fisheries management strategies. In addition to optical data, acoustic data from sonars or echo sounders provide an effective and reliable method for estimating fish biomass and abundance in complex deep waters, where turbidity can impair the performance of optical sensors [335].