CONVOLUTIONAL NEURAL NETWORKS FOR REAL TIME DETECTION OF WATER CONTAMINANTS AND MEDICAL HEALTH RISK PREVENTION
DOI:
https://doi.org/10.48047/ykj84j47Keywords:
Water Quality Prediction, Industrial Water Pollution and Public Health, Artificial Intelligence, Deep Learning, Formaldehyde Detection, Real-Time Monitoring,Abstract
Formaldehyde contamination in water poses serious health risks, including respiratory issues, skin irritation, and long-term carcinogenic effects. Chronic exposure to contaminated water can lead to conditions such as dermatitis, asthma, and even organ damage. This study provides an AI-driven early detection system that ensures safer water for both industrial workers and communities, reducing the burden of waterborne diseases and toxic exposure-related health complications. Deep learning
techniques, such as Convolutional Neural Networks (CNNs), are widely used in medical diagnostics (e.g., cancer detection and radiology).
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References
Wai, K. P., et al. "Applications of deep learning in water quality management: A state-of-the art review." Journal of Hydrology 613 (2022): 128332.
Barzegar, R., et al. "Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model." Stochastic Environmental Research and Risk Assessment 34.2 (2020): 415-433.
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