{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T12:21:18Z","timestamp":1766578878464,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T00:00:00Z","timestamp":1722988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/bdcc8080089","type":"journal-article","created":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T11:33:52Z","timestamp":1723030432000},"page":"89","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Deep-Learning-Driven Turbidity Level Classification"],"prefix":"10.3390","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4983-4626","authenticated-orcid":false,"given":"Iv\u00e1n","family":"Trejo-Z\u00fa\u00f1iga","sequence":"first","affiliation":[{"name":"Laboratory of Energy Innovation and Intelligent and Sustainable Agriculture (LEIISA), Universidad Tecnol\u00f3gica de San Juan del R\u00edo, San Juan del R\u00edo 76800, Quer\u00e9taro, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6590-441X","authenticated-orcid":false,"given":"Martin","family":"Moreno","sequence":"additional","affiliation":[{"name":"Laboratory of Energy Innovation and Intelligent and Sustainable Agriculture (LEIISA), Universidad Tecnol\u00f3gica de San Juan del R\u00edo, San Juan del R\u00edo 76800, Quer\u00e9taro, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3176-7100","authenticated-orcid":false,"given":"Rene Francisco","family":"Santana-Cruz","sequence":"additional","affiliation":[{"name":"Centro de Investigaci\u00f3n en Ciencia Aplicada y Tecnolog\u00eda Avanzada, Unidad Quer\u00e9taro, Instituto Polit\u00e9cnico Nacional, Santiago de Quer\u00e9taro 76000, Quer\u00e9taro, Mexico"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0914-9314","authenticated-orcid":false,"given":"Fidel","family":"Mel\u00e9ndez-V\u00e1zquez","sequence":"additional","affiliation":[{"name":"Escuela Superior de Apan, Universidad Aut\u00f3noma del Estado de Hidalgo, Apan 43920, Hidalgo, Mexico"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cotruvo, J. (2018). Drinking Water Quality and Contaminants Guidebook, CRC Press.","DOI":"10.1201\/9781351110471"},{"key":"ref_2","unstructured":"Water, Sanitation, Hygiene and Health (WSH) (2021). A Global Overview of National Regulations and Standards for Drinking-Water Quality, WHO. [2nd ed.]."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.watres.2015.06.028","article-title":"Water quality as a predictor of gastrointestinal illness following incidental contact water recreation","volume":"83","author":"Dorevitch","year":"2015","journal-title":"Water Res."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zaman, M., Shahid, S.A., and Heng, L. (2018). Irrigation Water Quality. Guideline for Salinity Assessment, Mitigation and Adaptation Using Nuclear and Related Techniques, Springer.","DOI":"10.1007\/978-3-319-96190-3"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Malakar, A., Snow, D.D., and Ray, C. (2019). Irrigation Water Quality\u2014A Contemporary Perspective. Water, 11.","DOI":"10.3390\/w11071482"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.tifs.2019.04.003","article-title":"Treatment and utilization of dairy industrial waste: A review","volume":"88","author":"Ahmad","year":"2019","journal-title":"Trends Food Sci. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Boyd, C.E. (2020). Water Quality: An Introduction, Springer International Publishing.","DOI":"10.1007\/978-3-030-23335-8"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1007\/s002650050330","article-title":"The role of turbidity as a constraint on predator-prey interactions in aquatic environments","volume":"40","author":"Abrahams","year":"1997","journal-title":"Behav. Ecol. Sociobiol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1080\/10236240290025644","article-title":"Visual feeding of fish in a turbid environment: Physical and behavioural aspects","volume":"35","year":"2002","journal-title":"Mar. Freshw. Behav. Physiol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1002\/ece3.454","article-title":"The effect of turbidity on recognition and generalization of predators and non-predators in aquatic ecosystems","volume":"3","author":"Chivers","year":"2012","journal-title":"Ecol. Evol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.envint.2005.08.027","article-title":"Organochlorine pesticide contamination of ground water in the city of Hyderabad","volume":"32","author":"Shukla","year":"2006","journal-title":"Environ. Int."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Nicolopoulou-Stamati1, P., Maipas, S., Kotampasi, C., Stamatis, P., and Hens, L. (2016). Chemical Pesticides and Human Health: The Urgent Need for a New Concept in Agriculture. Front. Public Health, 4.","DOI":"10.3389\/fpubh.2016.00148"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ghazi, R.M., Yusoff, N.R.N., Halim, N.S.A., Wahab, I.R.A., Latif, N.A., Hasmoni, S.H., Zaini, M.A.A., and Zakaria, Z.A. (2023). Health effects of herbicides and its current removal strategies. Bioengineered, 14.","DOI":"10.1080\/21655979.2023.2259526"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"e29128","DOI":"10.1016\/j.heliyon.2024.e29128","article-title":"Pesticides impacts on human health and the environment with their mechanisms of action and possible countermeasures","volume":"10","author":"Ahmad","year":"2024","journal-title":"Heliyon"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1080\/09603123.2013.769201","article-title":"Water quality indicators: Bacteria, coliphages, enteric viruses","volume":"23","author":"Lin","year":"2013","journal-title":"Int. J. Environ. Health Res."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"8311","DOI":"10.3390\/s91008311","article-title":"Turbidimeter Design and Analysis: A Review on Optical Fiber Sensors for the Measurement of Water Turbidity","volume":"9","year":"2009","journal-title":"Sensors"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1177\/002029408401700904","article-title":"Intelligent Turbidity Monitoring","volume":"17","author":"Extance","year":"1984","journal-title":"Meas. Control"},{"key":"ref_18","unstructured":"Gregory, J. (2006). Particles in Water: Properties and Processes, CRC Press."},{"key":"ref_19","unstructured":"Eaton, A.D., Clesceri, L.S., Rice, E.W., and Greenberg, A.E. (2000). Standard Methods for the Examination of Water and Wastewater, American Library Association. [20th ed.]."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1177\/0309133317726540","article-title":"A review of the principles of turbidity measurement","volume":"41","author":"Kitchener","year":"2017","journal-title":"Prog. Phys. Geogr. Earth Environ."},{"key":"ref_21","unstructured":"Rangers, W. (2024, June 19). Turbidity (NTU), Available online: https:\/\/www.waterboards.ca.gov\/water_issues\/programs\/swamp\/docs\/cwt\/guidance\/3150en.pdf."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Parra, L., Ahmad, A., Sendra, S., Lloret, J., and Lorenz, P. (2024). Combination of Machine Learning and RGB Sensors to Quantify and Classify Water Turbidity. Chemosensors, 12.","DOI":"10.3390\/chemosensors12030034"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wan, S., Yeh, M.-L., Ma, H.-L., and Chou, T.-Y. (2022). The Robust Study of Deep Learning Recursive Neural Network for Predicting of Turbidity of Water. Water, 14.","DOI":"10.3390\/w14050761"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"9132","DOI":"10.1109\/JSTARS.2021.3109292","article-title":"Remote Sensing of Turbidity for Lakes in Northeast China Using Sentinel-2 Images with Machine Learning Algorithms","volume":"14","author":"Ma","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"012004","DOI":"10.1088\/1742-6596\/1757\/1\/012004","article-title":"Research of Water Body Turbidity Classification Model for Aquiculture Based on Transfer Learning","volume":"1757","author":"Zheng","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"9510713","DOI":"10.1109\/TIM.2022.3205915","article-title":"Self-Organizing Multichannel Deep Learning System for River Turbidity Monitoring","volume":"71","author":"Gu","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lopez-Betancur, D., Moreno, I., Guerrero-Mendez, C., Saucedo-Anaya, T., Gonz\u00e1lez, E., Bautista-Capetillo, C., and Gonz\u00e1lez-Trinidad, J. (2022). Convolutional Neural Network for Measurement of Suspended Solids and Turbidity. Appl. Sci., 12.","DOI":"10.3390\/app12126079"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1007\/s13762-022-04531-y","article-title":"An image-based deep learning model for water turbidity estimation in laboratory conditions","volume":"20","author":"Feizi","year":"2022","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_29","unstructured":"(2004). Portable Turbidimeter (Standard No. Model 2100P ISO)."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Trejo-Z\u00fa\u00f1iga, I., Moreno, M., Santana-Cruz, R.F., and Mel\u00e9ndez-V\u00e1zquez, F. (2024). Deep Learning-Driven of Turbidity Levels Dataset, European Organization for Nuclear Research.","DOI":"10.3390\/bdcc8080089"},{"key":"ref_32","unstructured":"(2001). Technical Report: NMX-AA-038-SCFI-2001 An\u00e1lisis de Agua-Determinaci\u00f3n de Turbiedad en Aguas Naturales, Residuales y Residuales Tratadas-M\u00e9todo de Prueba, Secretar\u00eda de Econom\u00eda."}],"container-title":["Big Data and Cognitive Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-2289\/8\/8\/89\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:31:16Z","timestamp":1760110276000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-2289\/8\/8\/89"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,7]]},"references-count":32,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["bdcc8080089"],"URL":"https:\/\/doi.org\/10.3390\/bdcc8080089","relation":{},"ISSN":["2504-2289"],"issn-type":[{"type":"electronic","value":"2504-2289"}],"subject":[],"published":{"date-parts":[[2024,8,7]]}}}