USE OF AI IN PREDICTING AND MANAGING ALGAL BLOOMS IN FRESHWATER BODIES

Abstract

Background:Algal blooms in freshwater bodies pose significant ecological and economic challenges, necessitating advanced predictive and management strategies. Traditional monitoring approaches are often reactive, failing to provide timely interventions. Recent advancements in Artificial Intelligence (AI) have enabled more accurate predictions of algal bloom occurrences, allowing forproactive management based on environmental and hydrodynamic data.

Objective:This study aims to develop and validate AI-driven models for predicting and managing algal blooms in freshwater bodies by integrating machine learning techniques with real-time and historical environmental data. The study focuses on enhancing predictive accuracy and enabling early-warning systems for effective intervention strategies.

Methods:This research employs a combination of observational and experimental study designs to collect and analyze environmental data from multiple freshwater bodies. Real-time and historical datasets are obtained through in situ water quality monitoring, remote sensing via satellite imagery, and hydrodynamic modeling. The study population includes diverse freshwater bodies with varying trophic states and algal species such as cyanobacteria and dinoflagellates, which are known for causing harmful algal blooms. AI models, including Gradient Boosting Regressor (GBR), Long Short-Term Memory (LSTM) networks, and Artificial Neural Networks (ANNs), are applied to analyze the collected data. Model performance is evaluated using accuracy, precision, recall, F1-score, and R-squared (R²) metrics. Data preprocessing techniques, including normalization, handling of missing values, and feature selection, areemployed to enhance model efficiency.

Results:The AI models demonstrated strong predictive capabilities in forecasting algal bloom occurrences based on environmental parameters. LSTM networks outperformed other models in capturing temporal patterns and seasonal variations in bloom dynamics, while GBR provided high-accuracy predictions of chlorophyll concentrations. ANN models effectively identified patterns associated with harmful algal bloom formation. Comparative analysis of model performance showed that incorporating hydrodynamic data improved prediction accuracy. The integration of remote sensing data enhanced spatial resolution, enabling early detection of bloom-prone regions. Overall, the study findings highlight the potential of AI-based approaches to improve the accuracy and efficiency of algal bloom prediction and management strategies.

Conclusion:AI-based models provide a robust framework for early detection and management of algal blooms. The integration of diverse data sources and machine learning algorithms enhances forecasting accuracy, offering a proactive approach to mitigating harmful blooms in freshwater ecosystems. Future research should focus on refining AI models to improve adaptability across different water bodies and environmental conditions.