Deep-Learning-Driven Turbidity Level Classification

• Published in: Big data and cognitive computing Vol. 8; no. 8; p. 89
• Main Authors: Trejo-Zúñiga, Iván, Moreno, Martin, Santana-Cruz, Rene Francisco, Meléndez-Vázquez, Fidel
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2024
• Subjects: Accuracy; Aquatic ecosystems; Artificial neural networks; Chemical contaminants; Classification; Datasets; Deep learning; Digital cameras; Drinking water; Health risk assessment; Health risks; Heavy metals; Light; Machine learning; Neural networks; Nutrients; Particle shape; Sensors; Turbidimeters; Turbidity; turbidity classification; Water quality; Water sampling; Mexico
• ISSN: 2504-2289; 2504-2289
• DOI: 10.3390/bdcc8080089

Accurate turbidity classification is essential for maintaining water quality in various contexts, from drinking water to industrial processes. Traditional turbidimeters face challenges, including interference from colored substances, particle shape and size variations, and the need for regular calibration and maintenance. This paper implements a convolutional neural network (CNN) to classify water samples based on their turbidity levels. The dataset consisted of images captured under controlled laboratory conditions, with turbidity levels measured using a 2100P Portable Turbidimeter. The CNN achieved a classification accuracy of 97.00% in laboratory settings. When tested on real-world water body samples, the model maintained an accuracy of 85.00%. The results demonstrate that deep learning can effectively classify turbidity levels, offering a promising solution to overcome the limitations of traditional methods. The study highlights the potential of CNNs for accurate and efficient turbidity measurement, balancing accuracy with practical applicability in field conditions.