WaterNet: A Network for Monitoring and Assessing Water Quality for Drinking and Irrigation Purposes
Water is a fundamental requirement for human, animal, and plant survival. Despite its importance, quality water is not always fit for drinking, domestic and/or industrial use. Numerous factors such as industrialization, mining, pollution, and natural occurrences impact the quality of water, as they...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9766319/ |
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| author | Olasupo O. Ajayi Antoine B. Bagula Hloniphani C. Maluleke Zaheed Gaffoor Nebo Jovanovic Kevin C. Pietersen |
| author_facet | Olasupo O. Ajayi Antoine B. Bagula Hloniphani C. Maluleke Zaheed Gaffoor Nebo Jovanovic Kevin C. Pietersen |
| author_sort | Olasupo O. Ajayi |
| collection | DOAJ |
| description | Water is a fundamental requirement for human, animal, and plant survival. Despite its importance, quality water is not always fit for drinking, domestic and/or industrial use. Numerous factors such as industrialization, mining, pollution, and natural occurrences impact the quality of water, as they introduce or alter various parameters present therein, thus, affecting its suitability for human consumption or general use. The World Health Organization has guidelines which stipulate the threshold levels of various parameters present in water samples intended for consumption or irrigation. The Water Quality Index (WQI) and Irrigation WQI (IWQI) are metrics used to express the level of these parameters to determine the overall water quality. Collecting water samples from different sources, measuring the various parameters present, and bench-marking these measurements against pre-set standards, while adhering to various guidelines during transportation and measurement can be extremely daunting. To this end this study proposes a network architecture to collect data on water parameters in real-time and use Machine Learning (ML) tools to automatically determine suitability of water samples for drinking and irrigation purposes. The developed monitoring network is based on LoRa and takes the land topology into consideration. Results of simulations done in Radio Mobile revealed a partial mesh network topology as the most adequate. Due to the absence of large and open datasets on drinking and irrigation water, datasets usable for training ML models were developed. Three ML models - Random Forest (RF), Logistic Regression (LR) and Support Vector Machine (SVM) were considered for the water classification process and results obtained showed that LR performed best for drinking water, while SVM was better suited for irrigation water. Recursive feature elimination was then combined with the three ML models to reveal which of the water parameters had the greatest influence on the classification accuracies of the respective model. |
| first_indexed | 2024-04-14T00:02:08Z |
| format | Article |
| id | doaj.art-6e8319886c7841e3b589ba4ddbda353d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-14T00:02:08Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6e8319886c7841e3b589ba4ddbda353d2022-12-22T02:23:40ZengIEEEIEEE Access2169-35362022-01-0110483184833710.1109/ACCESS.2022.31722749766319WaterNet: A Network for Monitoring and Assessing Water Quality for Drinking and Irrigation PurposesOlasupo O. Ajayi0https://orcid.org/0000-0001-6583-3749Antoine B. Bagula1https://orcid.org/0000-0003-0774-5251Hloniphani C. Maluleke2https://orcid.org/0000-0001-8972-0167Zaheed Gaffoor3https://orcid.org/0000-0003-1631-6627Nebo Jovanovic4Kevin C. Pietersen5https://orcid.org/0000-0002-2723-5809Department of Computer Science, University of the Western Cape, Cape Town, Bellville, South AfricaDepartment of Computer Science, University of the Western Cape, Cape Town, Bellville, South AfricaDepartment of Computer Science, University of the Western Cape, Cape Town, Bellville, South AfricaDepartment of Earth Science, University of the Western Cape, Cape Town, Bellville, South AfricaDepartment of Earth Science, University of the Western Cape, Cape Town, Bellville, South AfricaInstitute for Water Studies, University of the Western Cape, Cape Town, Bellville, South AfricaWater is a fundamental requirement for human, animal, and plant survival. Despite its importance, quality water is not always fit for drinking, domestic and/or industrial use. Numerous factors such as industrialization, mining, pollution, and natural occurrences impact the quality of water, as they introduce or alter various parameters present therein, thus, affecting its suitability for human consumption or general use. The World Health Organization has guidelines which stipulate the threshold levels of various parameters present in water samples intended for consumption or irrigation. The Water Quality Index (WQI) and Irrigation WQI (IWQI) are metrics used to express the level of these parameters to determine the overall water quality. Collecting water samples from different sources, measuring the various parameters present, and bench-marking these measurements against pre-set standards, while adhering to various guidelines during transportation and measurement can be extremely daunting. To this end this study proposes a network architecture to collect data on water parameters in real-time and use Machine Learning (ML) tools to automatically determine suitability of water samples for drinking and irrigation purposes. The developed monitoring network is based on LoRa and takes the land topology into consideration. Results of simulations done in Radio Mobile revealed a partial mesh network topology as the most adequate. Due to the absence of large and open datasets on drinking and irrigation water, datasets usable for training ML models were developed. Three ML models - Random Forest (RF), Logistic Regression (LR) and Support Vector Machine (SVM) were considered for the water classification process and results obtained showed that LR performed best for drinking water, while SVM was better suited for irrigation water. Recursive feature elimination was then combined with the three ML models to reveal which of the water parameters had the greatest influence on the classification accuracies of the respective model.https://ieeexplore.ieee.org/document/9766319/Cyber physical systemLoRadrinking waterirrigation watermachine learningwater quality index |
| spellingShingle | Olasupo O. Ajayi Antoine B. Bagula Hloniphani C. Maluleke Zaheed Gaffoor Nebo Jovanovic Kevin C. Pietersen WaterNet: A Network for Monitoring and Assessing Water Quality for Drinking and Irrigation Purposes IEEE Access Cyber physical system LoRa drinking water irrigation water machine learning water quality index |
| title | WaterNet: A Network for Monitoring and Assessing Water Quality for Drinking and Irrigation Purposes |
| title_full | WaterNet: A Network for Monitoring and Assessing Water Quality for Drinking and Irrigation Purposes |
| title_fullStr | WaterNet: A Network for Monitoring and Assessing Water Quality for Drinking and Irrigation Purposes |
| title_full_unstemmed | WaterNet: A Network for Monitoring and Assessing Water Quality for Drinking and Irrigation Purposes |
| title_short | WaterNet: A Network for Monitoring and Assessing Water Quality for Drinking and Irrigation Purposes |
| title_sort | waternet a network for monitoring and assessing water quality for drinking and irrigation purposes |
| topic | Cyber physical system LoRa drinking water irrigation water machine learning water quality index |
| url | https://ieeexplore.ieee.org/document/9766319/ |
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