## 3 Results
### AI for Monitoring, Forecasting, and Management of Aquatic Ecosystems  
Regional coverage: This sub-section includes 34 studies. The majority of cases pertain to Asia (\(n=20\); e.g., China, Japan, Indonesia, Taiwan, India, Bangladesh, Cambodia), with additional case studies identified in Africa (\(n=2\); Algeria and South Africa), Europe (\(n=1\)), and North America (\(n=4\), e.g., USA). Eight studies adopt a global perspective (\(n=8\)). AI is gradually assuming a central role in environmental monitoring and the sustainable management of aquatic and coastal ecosystems. In the face of global climate change, urbanization, increasing freshwater demand, and water pollution, there is a growing need for precise, scalable, and adaptive tools that can provide timely and reliable data for informed decision making. The study by [42] presents an integrated water resource management system that combines natural process modeling with AI tools and hierarchical optimization strategies. This approach accounts for the interactions between groundwater and agriculture in arid coastal regions. AI contributes to multi-criteria decision optimization, ensuring a sustainable balance among water quality, availability, and regional socio-economic conditions. One of the most prominent areas for AI application remains coastal zone monitoring, where anthropogenic pressures such as aquaculture, urban expansion, recreational use, and pollution are particularly intense. Study [43] introduced an object-based model for identifying aquaculture facilities in Asia using SAR and optical satellite imagery. This model effectively tracked spatiotemporal changes and assessed the degradation levels of coastal ecosystems. In a related study, Ref. [44] used Landsat imagery and machine learning algorithms on Google Earth Engine to map aquaculture ponds and their impact on mangrove forests. The study identified symbiotic zones and priority areas for ecosystem restoration, with seasonal water level variations improving the accuracy of anthropogenic impact assessments. Using machine learning techniques, study [45] analyzed the impact of climate change and land use on phytoplankton and cyanobacteria in lakes, drawing on data from 1971 to 2016. The findings reveal that climatic factors dominate in remote areas, whereas urbanization emerges as the key driver in urban lakes, underscoring the need for locally tailored management approaches. Study [46] emphasizes the shift from empirical models to AI-driven approaches for processing remote sensing data, which enables the analysis of long-term and large-scale changes in lakes, including phytoplankton blooms and eutrophication. The advancement of interpretable machine learning (IML) and explainable artificial intelligence (XAI) offers a significant leap in freshwater ecosystem monitoring. These technologies enhance predictive modeling and facilitate the integration of heterogeneous data streams to support sustainable water management decisions. The study by [47] focused on coastal green infrastructure in Puducherry, India, utilizing Sentinel-2 imagery and ALTM laser topography. A Random Forest model was used to assess the effectiveness of ecosystems in mitigating climate risks such as flooding, as well as their role in biodiversity conservation and carbon sequestration. In coastal Louisiana, the study by [48] employed XGBoost and Random Forest models to simulate land loss and predict vulnerable areas, offering restoration scenarios for future planning. Studies [49; 50] focused on the analysis of marine pollution. The former examined marine debris concentrations in Japan using satellite and drone imagery processed through machine learning algorithms, while the latter developed AI-based models for automatic recognition of electronic waste, enabling proactive removal of toxic and non-biodegradable pollutants. Together, these studies highlight the potential of AI in combating marine pollution.