Autonomous Water Quality Monitoring for Remote Communities: A Proposed Architecture and AI Model for Prevention and Management

Authors

  • Thalisson Lian Gomes Oliveira UEPA CAMPUS XV
  • Marcos Eduardo Melo Dos Santos Universidade do Estado de São Paulo (USP)
  • Hugo Santos Universidade do Estado do Pará (UEPA)

DOI:

https://doi.org/10.66104/3weft486

Keywords:

Water Security; Environmental Monitoring; Artificial Intelligence; Predictive Maintenance; Water Asset Management.

Abstract

This study proposes an autonomous architecture and an Artificial Intelligence (IA) model for asset management and continuous water contamination monitoring in remote areas, specifically focusing on the Yanomami Indigenous Territory (DSEI Yanomami). The objective is to validate a proof of concept capable of predicting heavy metal contamination, detecting hardware anomalies, and optimizing the resilience of supply systems. The methodology integrates low-cost IoT sensors with a Long Short-Term Memory (LSTM) recurrent neural network model fed by real-world logistical vulnerability indicators. Simulated results demonstrate that the AI can anticipate chemical contamination events 48 hours in advance and predict critical infrastructure hardware failures with a 30-day logistical window, achieving 95% accuracy in telemetry. Furthermore, AI-driven dynamic power management resulted in a 75% reduction in energy consumption. It is concluded that the proposal strengthens water security and public health by ensuring operational sustainability in vulnerable and isolated territories.

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References

ATLAS SCIENTIFIC. pH Sensor Probe & Circuit Overview. Atlas Scientific, 2023. Disponível em: https://atlas-scientific.com/kits/ph-sensor-kit/. Acesso em: 15 abr. 2025.

BRASIL. Ministério do Meio Ambiente. Qualidade da Água – Indicadores Ambientais. Governo do Brasil, 2022. Disponível em: https://www.gov.br/mma/pt-br/assuntos/monitoramento-e-informacoes-ambientais/qualidade-da-agua. Acesso em: 15 abr. 2025.

BRASIL. Ministério da Saúde. Vigipara - Vigilância da Qualidade da Água para Consumo Humano. Brasília, DF: MS, 2023. Disponível em: https://www.gov.br/saude/pt-br. Acesso em: 10 jan. 2026.

DFROBOT. Gravity: Heavy Metal Sensor Analog. DFRobot, 2023. Disponível em: https://wiki.dfrobot.com/Gravity_Heavy_Metal_Sensor_SKU_SEN0248. Acesso em: 15 abr. 2025.

GREFF, K. et al. LSTM: A Search Space Odyssey. IEEE Transactions on Neural Networks and Learning Systems, v. 28, n. 10, p. 2222-2232, out. 2017. Disponível em: https://ieeexplore.ieee.org/document/7508408. Acesso em: 18 out. 2025. DOI: https://doi.org/10.1109/TNNLS.2016.2582924

GSMA. Mobile IoT (NB-IoT & LTE-M): Enabling the Internet of Things. GSM Association, 2023. Disponível em: https://www.gsma.com/iot/mobile-iot/. Acesso em: 15 abr. 2025.

HAMEED, M. et al. Application of artificial neural networks for predicting water quality index in tropical rivers. Environmental Science and Pollution Research, v. 24, n. 28, p. 22618-22627, 2017. Disponível em: https://link.springer.com/article/10.1007/s11356-017-9900-5. Acesso em: 24 mar. 2026.

HOCHREITER, S.; SCHMIDHUBER, J. Long Short-Term Memory. Neural Computation, v. 9, n. 8, p. 1735-1780, nov. 1997. Disponível em: https://direct.mit.edu/neco/article/9/8/1735/6109. Acesso em: 18 out. 2025. DOI: https://doi.org/10.1162/neco.1997.9.8.1735

LECUN, Y.; BENGIO, Y.; HINTON, G. Deep learning. Nature, v. 521, n. 7553, p. 436-444, maio 2015. Disponível em: https://www.nature.com/articles/nature14539. Acesso em: 11 jan. 2026. DOI: https://doi.org/10.1038/nature14539

MURATA. Battery Fuel Gauge ICs for Lithium-ion. Murata, 2023. Disponível em: https://www.murata.com/en-us/products/batteries/fuelgauge. Acesso em: 15 abr. 2025.

ONU BRASIL. Declaração das Nações Unidas sobre os Direitos dos Povos Indígenas. Organização das Nações Unidas, 2007. Disponível em: https://brasil.un.org/pt-br/236649-declaracao-das-nacoes-unidas-sobre-os-direitos-dos-povos-indigenas. Acesso em: 18 out. 2025.

ORGANIZAÇÃO PAN-AMERICANA DA SAÚDE (OPAS). Exposição humana ao mercúrio na Amazônia brasileira. Brasília: OPAS, 2022. Disponível em: https://iris.paho.org/handle/10665.2/8145. Acesso em: 15 abr. 2025.

PASIKA, S.; GANDLA, S. T. Smart water quality monitoring system with cost-effective using IoT. Heliyon, v. 6, n. 7, e04096, jul. 2020. Disponível em: https://doi.org/10.1016/j.heliyon.2020.e04096. Acesso em: 24 mar. 2026. DOI: https://doi.org/10.1016/j.heliyon.2020.e04096

RAJAEE, T. et al. A review of artificial intelligence methods for predicting water quality. Journal of Hydrology, v. 582, p. 124451, mar. 2020. Disponível em: https://doi.org/10.1016/j.jhydrol.2019.124451. Acesso em: 24 mar. 2026. DOI: https://doi.org/10.1016/j.jhydrol.2019.124451

SANTOS, G. R.; SANTANA, A. S. Gestão Comunitária da Água: soluções e dificuldades do saneamento rural no Brasil. Texto para Discussão (IPEA), Brasília, n. 2601, out. 2020. Disponível em: https://repositorio.ipea.gov.br/handle/11058/10287. Acesso em: 24 mar. 2026.

SAYED, A. et al. A critical review of renewable energy based smart grid with internet of things (IoT). IEEE Access, v. 9, p. 114643-114676, 2021. Disponível em: https://ieeexplore.ieee.org/document/9512399. Acesso em: 13 jan. 2026.

SILVA, A. et al. Avaliação de redes LoRaWAN para monitoramento ambiental remoto em áreas de floresta. Revista Brasileira de Recursos Hídricos, v. 26, e15, 2021. Disponível em: https://www.scielo.br/j/rbrh/a/lorawan-floresta. Acesso em: 24 mar. 2026.

THE THINGS NETWORK. LoRaWAN - Long Range Communication for IoT. The Things Network, 2024. Disponível em: https://www.thethingsnetwork.org/docs/lorawan/. Acesso em: 15 abr. 2025.

U-BLOX. GPS Module NEO-6M Series. u-blox, 2022. Disponível em: https://www.u-blox.com/en/product/neo-6-series. Acesso em: 15 abr. 2025.

VILLALVA, M. G. Energia solar fotovoltaica: conceitos e aplicações. 2. ed. São Paulo: Érica, 2015. Disponível em: https://www.google.com.br/books/edition/Energia_solar_fotovoltaica/pYInDwAAQBAJ. Acesso em: 13 jan. 2026.

WORLD HEALTH ORGANIZATION (WHO). Guidelines for drinking-water quality: 4th edition incorporating the 1st addendum. Geneva: WHO, 2017. Disponível em: https://www.who.int/publications/i/item/9789241549950. Acesso em: 15 abr. 2025.

ZUBI, G. et al. The role of Li-ion batteries in the future 21st century energy systems. Renewable and Sustainable Energy Reviews, v. 81, p. 2935-2947, jan. 2018. Disponível em: https://doi.org/10.1016/j.rser.2017.06.102. Acesso em: 13 jan. 2026. DOI: https://doi.org/10.1016/j.rser.2017.06.102

Published

2026-06-04

How to Cite

Autonomous Water Quality Monitoring for Remote Communities: A Proposed Architecture and AI Model for Prevention and Management. (2026). REMUNOM, 13(12), 1-33. https://doi.org/10.66104/3weft486