SMB: Web-Based Dam Monitoring and Data Analysis System

Authors

Keywords:

Dam safety, machine learning, web application

Abstract

The monitoring and supervision of complex infrastructures, such as the dams and dikes of the Belo Monte Hydroelectric Plant, demand continuous data collection and analysis from various instruments (e.g., piezometers, surface markers, flow meters). In the context of structural health monitoring, recent advances propose the use of machine learning to recognize patterns and detect anomalies in monitoring data. This study presents a web-based application designed to support the early detection of structural anomalies through machine learning algorithms for anomaly detection, coupled with an interactive dashboard for data visualization. The system enables the establishment of baseline structural behavior and the identification of deviations that may indicate potential risks. Models were trained and tested with historical data, and multiple visualization tools were developed to facilitate the interpretation of results by engineers and decision-makers. The outcomes demonstrate that the proposed solution introduces innovative and effective technologies for the structural monitoring domain, contributing to the modernization of diagnostic processes and enhancing safety in critical energy infrastructure.

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Author Biographies

Raimundo Neto, Federal University of Pará (UFPA)

Raimundo Neto received the B.Sc. degree in Information Systems from the Federal University of Southern and Southeastern Pará (UNIFESSPA), Marabá, Brazil, in 2021. He is currently a master's student at the Federal University of Pará (UFPA), where he works on Applied Computing projects in Structural Integrity Monitoring using Machine Learning techniques.

Victor Souza, Federal University of Pará (UFPA)

Victor Souza received the B.Sc. degree in Information Systems from the Federal University of Southern and Southeastern Pará (UNIFESSPA), Marabá, Brazil, in 2021. He is currently a master's student at the Federal University of Pará (UFPA), focusing on Machine Learning projects with an emphasis on Large Language Models and Data Science.

Max Júnior, Federal University of Pará (UFPA)

Max Junior received the B.Sc. degree in Computer Science at the Federal University of Para (UFPA), Belém, Brazil, in 2022. He has experience with web development, data mining, data science, and has worked on projects involving MBA (Market Basket Analysis). The research areas that interest him most involve the improvement of optimization and machine learning algorithms, as well as the application of data science and data mining to extract meaningful insights from data sets.

Iury Silva, Federal University of Pará (UFPA)

Iury Silva received the B.Sc. degree in Computer Science from the Federal University of Pará (UFPA), Belém, Brazil, in 2023. Currently working with development and maintenance of banking systems. I participated in research projects focusing on non-deterministic population algorithms and Computer Vision (Structural Health Monitoring).

 

Luiz Samico, Federal University of Pará (UFPA)

Luiz Samico received the B.Sc. degree in Laws from the University Center of Pará (CESUPA), Belém, Brazil, in 2019. Currently studying Computer Science at the Federal University of Pará (UFPA), Belém, Brazil. Volunteer at the Artificial Intelligence Laboratory (LAAI) performing research in the area of performance analysis. His main academic interests are: data science and analysis, data engineering, data mining techniques, and jurimetrics.

Adam Santos, Federal University of Southern and Southeastern Pará (UNIFESSPA)

Adam Santos received the M.Sc. degree in electrical engineering (telecommunications) from the Federal University of Pará (UFPA), Belém, Brazil, in 2014, where he also received the Ph.D. degree in electrical engineering (applied computing) in 2017. He is currently an Adjunct Professor at the Federal University of Southern and Southeastern Pará (UNIFESSPA), a Researcher at the Center of Excellence in Artificial Intelligence at the Federal University of Goiás (UFG), and a permanent professor at the PPGCF and PPCA, developing research related to artificial intelligence methods applied to complex systems problems.

Reginaldo Santos, Federal University of Pará (UFPA)

Reginaldo Santos received the M.Sc. degree in Computer Science from the Federal University of Pará (UFPA), Belém, Brazil, in 2016, where he also received his Ph.D. in Computer Science in 2019. He is currently a professor and vice-rector of the Faculty of Computing (FACOMP) and a member of the Graduate Program in Computer Science (PPGCC). His main scientific interests are: improving metaheuristics for multimodal optimization problems, using machine learning algorithms for pattern detection, and extracting non-trivial information from databases through data science and data mining technologies.

 

Hugo Kuribayashi, Federal University of Southern and Southeastern Pará (UNIFESSPA)

Hugo Kuribayashi received the M.Sc. degree in Computer Science from the Federal University of Pará (UFPA), Belém, Brazil, in 2011, where he also obtained his Ph.D. in Electrical Engineering with an Emphasis in Applied Computing, in 2021. He is currently a professor at the Federal University of Southern and Southeastern Pará (UNIFESSPA) and the Postgraduate Program in Forensic Sciences (PPGCF). He has experience in Computing, with an emphasis on Data Analysis, Machine Learning Methods, and Cybersecurity.

 

Carlos Frânces, Federal University of Pará (UFPA)

Carlos Frances received the M.Sc. degree in Computer Science from the University of Sao Paulo (USP), Sao Paulo, Brazil, in 1998, where he also obtained his Ph.D. in Computer Science and Computational Mathematics, in 2001. Currently, he is a professor at the Faculty of Computer Engineering and Telecommunications of the Federal University of Pará (UFPA). He has experience in the areas of Telecommunications Systems and Applied Computing, Telecommunications Systems, Applied Computing: Artificial Intelligence Techniques and Computer Vision.

 

João Costa, Federal University of Pará (UFPA)

Joao Costa received the M.Sc. degree in electrical engineering from the Pontifical Catholic University of Rio de Janeiro, Brazil, in 1989, and the Ph.D. degree in electrical engineering from the State University of Campinas, Campinas, Brazil, in 1994. Currently professor and vice-dean of the Faculty of Computing (FACOMP), member of the Graduate Program in Electrical Engineering (PPGEE) of the Federal University of Pará (UFPA). Main scientific interests are: improving metaheuristics for multimodal optimization problems, using machine learning algorithms to detect patterns and extracting non-trivial information from databases through data science and data mining technologies.

 

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Published

2026-01-04

How to Cite

Neto, R., Souza, V., Júnior, M., Silva, I., Samico, L., Santos, A., Santos, R., Kuribayashi, H., Frânces, C., & Costa, J. (2026). SMB: Web-Based Dam Monitoring and Data Analysis System. IEEE Latin America Transactions, 24(1), 6–14. Retrieved from https://latamt.ieeer9.org/index.php/transactions/article/view/10150

Issue

Vol. 24 No. 1 (2026): Ordinary Issue

Section

Computing