Real-time monitoring of water quality dynamics using low-cost sensor networks in Lagos lagoon
Aquatic ecosystem monitoring is important for supporting biodiversity and environmental stability, yet it faces increasing threats from pollution, climate change and human activities. This study presents the development and deployment of a low-cost multi-sensor data logging system for real-time moni...
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2025-01-01
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| Series: | Cambridge Prisms: Water |
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| Online Access: | https://www.cambridge.org/core/product/identifier/S2755177625100063/type/journal_article |
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| author | Idowu Ayisat Aneyo Mumin Olatunji Oladipo Funmilayo Victoria Doherty Julius Osato Ehigie Adebayo Fasasi Adebari Abdulwakeel Oluwatobi Atoyebi Peter Ozomata Balogun Ambrose Obinna Ikpele |
| author_facet | Idowu Ayisat Aneyo Mumin Olatunji Oladipo Funmilayo Victoria Doherty Julius Osato Ehigie Adebayo Fasasi Adebari Abdulwakeel Oluwatobi Atoyebi Peter Ozomata Balogun Ambrose Obinna Ikpele |
| author_sort | Idowu Ayisat Aneyo |
| collection | DOAJ |
| description | Aquatic ecosystem monitoring is important for supporting biodiversity and environmental stability, yet it faces increasing threats from pollution, climate change and human activities. This study presents the development and deployment of a low-cost multi-sensor data logging system for real-time monitoring of Lagos Lagoon. The system integrates temperature sensors, hydrophones, and imaging devices to collect environmental data. Results showed that temperature variations ranged from ~28.5 to 31.5 °C, with fluctuations influenced by partial and full submersion. Acoustic analysis revealed dominant frequencies below 500 Hz, indicative of biological and anthropogenic activity in the lagoon. Machine learning models trained on 31 species closely agreed with the environmental dataset despite some noticeable deviations, suggesting potential improvements through data augmentation and model refinement. Despite challenges such as signal attenuation in submerged conditions and image degradation due to water turbidity, the system successfully recorded and logged environmental parameters. This study demonstrates the feasibility of using artificial intelligence-powered, cost-effective sensor technology for continuous aquatic monitoring, with implications for biodiversity conservation and water resource management. Future research should focus on enhancing wireless communication, refining species detection algorithms and improving sensor resilience in harsh aquatic conditions. |
| format | Article |
| id | doaj-art-b79cb7d6804d4564b620adce30be25dd |
| institution | Kabale University |
| issn | 2755-1776 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Cambridge Prisms: Water |
| spelling | doaj-art-b79cb7d6804d4564b620adce30be25dd2025-08-26T06:44:08ZengCambridge University PressCambridge Prisms: Water2755-17762025-01-01310.1017/wat.2025.10006Real-time monitoring of water quality dynamics using low-cost sensor networks in Lagos lagoonIdowu Ayisat Aneyo0https://orcid.org/0000-0002-7128-9572Mumin Olatunji Oladipo1Funmilayo Victoria Doherty2Julius Osato Ehigie3Adebayo Fasasi Adebari4Abdulwakeel Oluwatobi Atoyebi5Peter Ozomata Balogun6Ambrose Obinna Ikpele7Department of Zoology, University of Lagos, Lagos, NigeriaDepartment of Mathematical and Computing Sciences, https://ror.org/03k2z3e59 Koladaisi University , Ibadan, NigeriaDepartment of Biological Science, https://ror.org/030chxw89 Yaba College of Technology , Yaba, NigeriaDepartment of Mathematics, University of Lagos, Lagos, NigeriaDepartment of Computer Engineering, https://ror.org/030chxw89 Yaba College of Technology , Yaba, NigeriaDepartment of Social Sciences, https://ror.org/030chxw89 Yaba College of Technology , Yaba, NigeriaDepartment of Biological Science, https://ror.org/030chxw89 Yaba College of Technology , Yaba, NigeriaDepartment of Mathematics, University of Lagos, Lagos, NigeriaAquatic ecosystem monitoring is important for supporting biodiversity and environmental stability, yet it faces increasing threats from pollution, climate change and human activities. This study presents the development and deployment of a low-cost multi-sensor data logging system for real-time monitoring of Lagos Lagoon. The system integrates temperature sensors, hydrophones, and imaging devices to collect environmental data. Results showed that temperature variations ranged from ~28.5 to 31.5 °C, with fluctuations influenced by partial and full submersion. Acoustic analysis revealed dominant frequencies below 500 Hz, indicative of biological and anthropogenic activity in the lagoon. Machine learning models trained on 31 species closely agreed with the environmental dataset despite some noticeable deviations, suggesting potential improvements through data augmentation and model refinement. Despite challenges such as signal attenuation in submerged conditions and image degradation due to water turbidity, the system successfully recorded and logged environmental parameters. This study demonstrates the feasibility of using artificial intelligence-powered, cost-effective sensor technology for continuous aquatic monitoring, with implications for biodiversity conservation and water resource management. Future research should focus on enhancing wireless communication, refining species detection algorithms and improving sensor resilience in harsh aquatic conditions.https://www.cambridge.org/core/product/identifier/S2755177625100063/type/journal_articleAquatic monitoringMachine Learning in EcologyLow-cost sensorAI-Based Species Identification |
| spellingShingle | Idowu Ayisat Aneyo Mumin Olatunji Oladipo Funmilayo Victoria Doherty Julius Osato Ehigie Adebayo Fasasi Adebari Abdulwakeel Oluwatobi Atoyebi Peter Ozomata Balogun Ambrose Obinna Ikpele Real-time monitoring of water quality dynamics using low-cost sensor networks in Lagos lagoon Cambridge Prisms: Water Aquatic monitoring Machine Learning in Ecology Low-cost sensor AI-Based Species Identification |
| title | Real-time monitoring of water quality dynamics using low-cost sensor networks in Lagos lagoon |
| title_full | Real-time monitoring of water quality dynamics using low-cost sensor networks in Lagos lagoon |
| title_fullStr | Real-time monitoring of water quality dynamics using low-cost sensor networks in Lagos lagoon |
| title_full_unstemmed | Real-time monitoring of water quality dynamics using low-cost sensor networks in Lagos lagoon |
| title_short | Real-time monitoring of water quality dynamics using low-cost sensor networks in Lagos lagoon |
| title_sort | real time monitoring of water quality dynamics using low cost sensor networks in lagos lagoon |
| topic | Aquatic monitoring Machine Learning in Ecology Low-cost sensor AI-Based Species Identification |
| url | https://www.cambridge.org/core/product/identifier/S2755177625100063/type/journal_article |
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