## 3 Results
### AI for Monitoring, Forecasting, and Management of Aquatic Ecosystems
The study by [51] addressed the impact of unregulated tourism on marine ecosystems by developing an AI-driven decision support system (DSS) aimed at minimizing environmental damage. The WILDetet platform [52] leverages aerial imagery and AI to monitor seabird populations, generating biodiversity data and enabling the detection of ecological trends for the formulation of effective marine conservation strategies. The study by [53] applied neural networks, geographical detectors, spatial correlation analysis, and trend analysis to assess pollution in the Yangtze River Basin, focusing on NPPE (nitrogen and phosphorus) emissions and their dynamics across municipalities. Additionally, the study by [16] contributed to wetland monitoring by introducing AI-based methods for analyzing spatiotemporal changes and mapping aquatic ecosystem structures using remote sensing data. Considerable attention is given to the assessment of water quality and the forecasting of its changes. The study by [54] tested five machine learning algorithms d tree, K-nearest neighbors, discriminant analysis, support vector machines, and extra trees) and found that SVM achieved the highest accuracy (95.4%) in predicting the Water Quality Index (WQI) in Algeria, thereby reducing the need for costly laboratory analyses. The study by [55] utilized support vector machines to estimate nutrient concentrations based on Landsat satellite data in Yueqing Bay (China), identifying both natural and anthropogenic influences. The use of Random Forest models and Landsat data in the study by [10] enabled the identification of high-risk zones and the prediction of aquatic ecosystem degradation, particularly in areas surrounding reservoirs, rivers, and agricultural landscapes. The study by [56] developed an LSTM model to reconstruct missing nitrate concentration data in watercourses, a method particularly relevant for regions with infrequent monitoring. The study by [57] applied decision tree and KNN algorithms to model groundwater vulnerability to nitrate and salinity contamination in the Indo-Gangetic Plain, highlighting zones of high toxicity. The study by [58] conducted a long-term analysis (1985-2019) of groundwater levels using satellite data and AI techniques, revealing a persistent decline in 44% of the examined regions, posing a threat to the hydrological integrity of local watersheds. In the study by [59], deep learning (VMD-iTransformer) was employed to accurately forecast groundwater levels in the arid Kubuki Desert region. The model effectively addressed the non-stationarity of time series data and maintained high predictive accuracy even under data-scarce conditions. The study by [60] applied the XGBoost machine learning algorithm to improve the accuracy of groundwater quality (GWQ) assessment in a coastal region of Bangladesh. The XGBoost model achieved high sensitivity (R\({}^{2}\) = 0.97), significantly reducing uncertainty in forecasting the Water Quality Index. In the study by [61], water turbidity in the Yangtze River Delta was analyzed over the period 1990-2020 using remote sensing techniques and big data analytics. Satellite imagery from Landsat was processed to assess the impact of urbanization, ecological engineering interventions, and the Three Gorges Dam. The study by [62] applied deep learning to correct anomalies in satellite time series data, enabling improved ecological assessment of the Miyun Reservoir in China. Study [63] employed a multi-model AI approach to analyze the impacts of land-use change on hydrological processes, including the prediction of emissions and water consumption. In the study by [64], AI was used to identify the key natural and anthropogenic drivers influencing water conservation in the tropical forests of Hainan, China--factors such as precipitation, evaporation, and agricultural activity were highlighted.