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Volume 14
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10.3390/technologies14040223
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

A Feasibility Study of IoT-Based Classification of Residential Water-Use Activities in Storage Tank Systems: A Comparative Analysis of Decision Trees, Random Forest, SVM, KNN, and Neural Networks

by
Iván Neftalí Chávez-Flores
1,
Héctor A. Guerrero-Osuna
1,*,
Jesuś Antonio Nava-Pintor
1,
Fabián García-Vázquez
1,
Luis F. Luque-Vega
2,3,
Rocío Carrasco-Navarro
4,
Marcela E. Mata-Romero
5,
Jorge A. Lizarraga
6 and
Salvador Castro-Tapia
3
1
Posgrado en Ingeniería y Tecnología Aplicada, Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Zacatecas 98000, Zacatecas, Mexico
2
Department of Technological and Industrial Processes, ITESO, Tlaquepaque 45604, Jalisco, Mexico
3
Tecnológico Nacional de México, Instituto Tecnológico Superior de Jerez, Jerez 99863, Zacatecas, Mexico
4
Research Laboratory on Optimal Design, Devices and Advanced Materials—OPTIMA, Department of Mathematics and Physics, ITESO, Tlaquepaque 45604, Jalisco, Mexico
5
Subdirección de Investigación, Centro de Enseñanza Técnica Industrial, C. Nueva Escocia 1885, Guadalajara 44638, Jalisco, Mexico
6
Departamento de Investigación, Centro de Enseñanza Técnica Industrial, Guadalajara 44638, Jalisco, Mexico
*
Author to whom correspondence should be addressed.
Technologies 2026, 14(4), 223; https://doi.org/10.3390/technologies14040223
Submission received: 28 February 2026 / Revised: 2 April 2026 / Accepted: 8 April 2026 / Published: 13 April 2026
(This article belongs to the Special Issue AI for Smart Engineering Systems)
Versions Notes

Abstract

The increasing scarcity of urban water resources, particularly in regions with intermittent supply and household water storage tanks, demands monitoring approaches capable of identifying end-use consumption patterns beyond aggregated volume measurements. Framed primarily as a feasibility study, this research presents an IoT-based framework for the automated classification of residential water consumption activities using water-level dynamics and supervised machine learning. A non-intrusive sensing architecture based on hydrostatic pressure measurements was deployed in a domestic water tank and integrated with a cloud-based data acquisition and processing platform. Five representative household states and activities were considered: tank refilling, stable state, toilet flushing, washing clothes, and taking a bath. A labeled dataset comprising 4396 consumption events was used to train and evaluate Decision Tree, Random Forest, Support Vector Machine (SVM), k-Nearest Neighbors, and Recurrent Neural Network (LSTM) models using features derived from water-level variations. All models achieved high performance, with accuracies above 0.92 and weighted F1-scores up to 0.93. The evaluated models showed highly comparable results, with the SVM (RBF) achieving a slightly higher accuracy (0.9307) in this evaluation setting, while ROC analysis showed AUC values between 0.97 and 1.00 across all classes, indicating strong discriminative capability. Additionally, specific activities such as washing clothes and tank refilling achieved precision and recall values above 0.95. These findings confirm that hydrostatic pressure-based sensing, combined with machine learning, enables reliable identification of domestic water-use events under intermittent supply conditions. The proposed approach provides actionable insights for demand management, leak detection, and user awareness, supporting more efficient and sustainable residential water consumption strategies.
Keywords: water monitoring; water management; machine learning; support vector machine; IoT architecture water monitoring; water management; machine learning; support vector machine; IoT architecture

Share and Cite

MDPI and ACS Style

Chávez-Flores, I.N.; Guerrero-Osuna, H.A.; Nava-Pintor, J.A.; García-Vázquez, F.; Luque-Vega, L.F.; Carrasco-Navarro, R.; Mata-Romero, M.E.; Lizarraga, J.A.; Castro-Tapia, S. A Feasibility Study of IoT-Based Classification of Residential Water-Use Activities in Storage Tank Systems: A Comparative Analysis of Decision Trees, Random Forest, SVM, KNN, and Neural Networks. Technologies 2026, 14, 223. https://doi.org/10.3390/technologies14040223

AMA Style

Chávez-Flores IN, Guerrero-Osuna HA, Nava-Pintor JA, García-Vázquez F, Luque-Vega LF, Carrasco-Navarro R, Mata-Romero ME, Lizarraga JA, Castro-Tapia S. A Feasibility Study of IoT-Based Classification of Residential Water-Use Activities in Storage Tank Systems: A Comparative Analysis of Decision Trees, Random Forest, SVM, KNN, and Neural Networks. Technologies. 2026; 14(4):223. https://doi.org/10.3390/technologies14040223

Chicago/Turabian Style

Chávez-Flores, Iván Neftalí, Héctor A. Guerrero-Osuna, Jesuś Antonio Nava-Pintor, Fabián García-Vázquez, Luis F. Luque-Vega, Rocío Carrasco-Navarro, Marcela E. Mata-Romero, Jorge A. Lizarraga, and Salvador Castro-Tapia. 2026. "A Feasibility Study of IoT-Based Classification of Residential Water-Use Activities in Storage Tank Systems: A Comparative Analysis of Decision Trees, Random Forest, SVM, KNN, and Neural Networks" Technologies 14, no. 4: 223. https://doi.org/10.3390/technologies14040223

APA Style

Chávez-Flores, I. N., Guerrero-Osuna, H. A., Nava-Pintor, J. A., García-Vázquez, F., Luque-Vega, L. F., Carrasco-Navarro, R., Mata-Romero, M. E., Lizarraga, J. A., & Castro-Tapia, S. (2026). A Feasibility Study of IoT-Based Classification of Residential Water-Use Activities in Storage Tank Systems: A Comparative Analysis of Decision Trees, Random Forest, SVM, KNN, and Neural Networks. Technologies, 14(4), 223. https://doi.org/10.3390/technologies14040223

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