Abstract

Aquatic ecosystems have the potential to be the most responsive to environmental changes, and adaptive management should incorporate innovative technologies, considering the scale and dynamic nature of ecosystem changes. This research develops the first step in this direction by integrating Artificial Intelligence and Remote Sensing Technologies for predictive and continuous monitoring of the health of aquatic ecosystems. This methodology, named AI-REM (Artificial Intelligence-Remote Ecosystem Monitoring), is based on the use of Remote Sensing-derived indices of ecosystem health, such as NDWI (Normalized Difference Water Index), Chlorophyll-a concentration, and turbidity, and combined with in situ macro water quality data. The disparate data are integrated in an innovative way using a hybrid deep learning architecture designed for this purpose that combines CNNs to extract spatial features and LSTMs to predict values over time. Dynamic predictions of critical eco-forecasting models are provided for dissolving nutrient levels, predicting hypoxia, nutrient loading, and algal bloom formation. Validation of these models on coastal water and inland water bodies for multiple years predicts over 92% accuracy, which is significantly higher than the accuracy of traditional models using regression and random forest approaches. This is the first application of AI remote predictive analytics to ecosystem monitoring, which not only improves forecasting for integrated water resource management but also sustains early action on climate change.

Keywords: Artificial intelligence, Remote sensing, Aquatic ecosystems, Predictive monitoring, Deep learning, Cnn-Lstm model, Water quality, Algal bloom forecasting, Ai-Rem framework, Environmental management