## 3 Results
### AI for Monitoring, Forecasting, and Management of Aquatic Ecosystems
The study by [65] utilized NDVI data and machine learning methods to forecast the impacts of urbanization on water conservation zones in the Funiu Mountains, focusing on spatiotemporal changes in hydrological regulation and soil erosion. Equally important is the application of AI in water balance forecasting and resource management under changing climate conditions. The study by [32] modeled water availability and irrigation demand, contributing to drought and water scarcity risk reduction. The study by [41] explored the use of digital twins, AI, and parallel intelligence for analyzing water use in arid ecosystems and improving the efficiency of resource distribution. The research emphasized the importance of international cooperation and virtual water trade as mechanisms to support sustainable water management strategies. The study by [66] demonstrated how the integration of AI, IoT, and satellite data improves the accuracy of hydrological forecasts and water cycle management by reducing uncertainty in precipitation and runoff modeling. In the study by [67], the Dikrong River system was assessed using ADCP and machine learning techniques, revealing the impact of anthropogenic pressures on flow parameters, current velocity, and forest cover change. The study by [68] applied GRACE satellite data and machine learning methods to examine how agricultural greening and increasing NDVI contribute to the depletion of terrestrial water storage in Northwest China. [PERSON] [69] analyzed the dynamics of the Caspian Sea, linking wetland loss to precipitation levels, sea level changes, and runoff patterns. In the study by [70], a comprehensive investigation of the Mekong region in Cambodia combined remote sensing, geographic information systems (GIS), and AI to detect trends in declining water resources. The analysis considered both climatic and anthropogenic drivers, including irrigation practices and land-use changes. The study by [71] proposed a cloud-based platform that integrates AI and remote sensing technologies, designed to support environmental monitoring in rural and vulnerable regions. This initiative ensures cost efficiency, scalability, and the active involvement of local communities in the protection and management of water resources, including in villages and municipalities of developing countries. Thus, the analysis of an extensive body of contemporary research highlights the increasing integration of AI into tasks related to the monitoring, assessment, and management of aquatic and coastal ecosystems. AI not only reduces operational costs and enhancesprecision but also enables predictive modeling, early risk detection, and the formulation of locally adapted strategies. The contribution of AI to the resilience of local water ecosystems is particularly significant. Its application enhances the effective protection of biodiversity, supports the maintenance of water balances, and facilitates adaptation to both climatic and anthropogenic pressures. By combining algorithmic precision, real-time data processing, and the richness of remote sensing information, AI is transforming water management approaches--from reactive to predictive, from fragmented to systemic--thereby reinforcing the resilience of natural environments and social infrastructure. In total, according to the regional classification, the following references pertain to Asia [16; 43; 44; 47; 49; 51; 53; 55; 57; 59; 60; 61; 62; 63; 64; 65; 67; 68; 69; 70; 63; 65;  
The quantification of blue carbon reserves in mangrove forests has also benefited from AI applications. In [77], Random Forest and XGBoost models were used alongside satellite imagery to estimate carbon storage, improving the accuracy of carbon sequestration assessments and supporting the integration of ecosystem services into carbon markets and emission reduction strategies. AI technologies further facilitate the development of three-dimensional ecological models.