What question did this study set out to answer?

The research aims to benchmark and analyze AI and ML techniques in various environmental domains.

March 31, 2026Open Access

The Role of Artificial Intelligence and Machine Learning in Environmental Monitoring and Management

Key Points

The research aims to benchmark and analyze AI and ML techniques in various environmental domains.
Used publicly available datasets for training AI models.
Evaluated multiple AI models across air quality, water pollution, deforestation, and biodiversity.
Applied specific algorithms like Random Forest, CNNs, LSTMs, and SVMs for different environmental tasks.
Measured model performance using accuracy, precision, recall, MAE, RMSE, and R² metrics.
AI methods outperformed traditional approaches with up to 95.1% accuracy in water pollution detection.
Improved accuracy ranged from 17.7% to 23% across environmental domains.
Achieved 93.2% accuracy in PM2.5 prediction and 94% detection rate for illegal logging.
Significant reductions in illegal logging (41.7%) and water contamination (44.2%) incidents after AI integration.

Abstract

This study presents a structured benchmarking and comparative analysis of Artificial Intelligence (AI) and Machine Learning (ML) techniques for environmental monitoring and management. Using publicly available datasets and reproducible modeling workflows, representative AI models were trained or re-implemented and evaluated across multiple environmental domains, including air quality, water pollution, deforestation, and biodiversity monitoring. The available used datasets include the Air Quality Open Dataset, AquaSat, Global Forest Watch, and iNaturalist, multiple AI models and were developed, trained, and validated to address key environmental domains. Random Forest was applied for air quality prediction, Convolutional Neural Networks (CNNs) for water pollution detection, Long Short-Term Memory (LSTM) networks for deforestation monitoring, and Support Vector Machines (SVMs) for wildlife species identification. Model performance was evaluated using accuracy, precision, recall, Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and coefficient of determination (R²) metrics. Results showed that AI-based methods significantly outperformed traditional monitoring approaches, achieving up to 95.1% accuracy in water pollution detection and 92.4% accuracy in air quality prediction, with accuracy improvements ranging from 17.7% to 23% across domains. Gradient Boosting achieved a 93.2% accuracy in PM2.5 prediction (R² = 0.92), while YOLOv5 reached a 94% detection rate for illegal logging. Environmental impact assessments revealed substantial reductions after AI integration, including a 41.7% decrease in illegal logging and a 44.2% decline in water contamination incidents. Deployment analysis indicated high-cost efficiency, with Return on Investment (ROI) values up to 175% over three years and time savings between 68% and 73% across monitoring tasks. These findings confirm that AI and ML not only enhance predictive precision but also deliver tangible environmental and economic benefits, underscoring their potential as essential tools for sustainable environmental governance.

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