An Evidence Theory Based Embedding Model for the Management of Smart Water Environments
Having access to safe water and using it properly is crucial for human well-being, sustainable development, and environmental conservation. Nonetheless, the increasing disparity between human demands and natural freshwater resources is causing water scarcity, negatively impacting agricultural and in...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-05-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/10/4672 |
| _version_ | 1797598370026487808 |
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| author | Maha Driss Wadii Boulila Haithem Mezni Mokhtar Sellami Safa Ben Atitallah Nouf Alharbi |
| author_facet | Maha Driss Wadii Boulila Haithem Mezni Mokhtar Sellami Safa Ben Atitallah Nouf Alharbi |
| author_sort | Maha Driss |
| collection | DOAJ |
| description | Having access to safe water and using it properly is crucial for human well-being, sustainable development, and environmental conservation. Nonetheless, the increasing disparity between human demands and natural freshwater resources is causing water scarcity, negatively impacting agricultural and industrial efficiency, and giving rise to numerous social and economic issues. Understanding and managing the causes of water scarcity and water quality degradation are essential steps toward more sustainable water management and use. In this context, continuous Internet of Things (IoT)-based water measurements are becoming increasingly crucial in environmental monitoring. However, these measurements are plagued by uncertainty issues that, if not handled correctly, can introduce bias and inaccuracy into our analysis, decision-making processes, and results. To cope with uncertainty issues related to sensed water data, we propose combining network representation learning with uncertainty handling methods to ensure rigorous and efficient modeling management of water resources. The proposed approach involves accounting for uncertainties in the water information system by leveraging probabilistic techniques and network representation learning. It creates a probabilistic embedding of the network, enabling the classification of uncertain representations of water information entities, and applies evidence theory to enable decision making that is aware of uncertainties, ultimately choosing appropriate management strategies for affected water areas. |
| first_indexed | 2024-03-11T03:21:16Z |
| format | Article |
| id | doaj.art-41b22c1e437745e2b01631b4e1762d0f |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T03:21:16Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-41b22c1e437745e2b01631b4e1762d0f2023-11-18T03:10:49ZengMDPI AGSensors1424-82202023-05-012310467210.3390/s23104672An Evidence Theory Based Embedding Model for the Management of Smart Water EnvironmentsMaha Driss0Wadii Boulila1Haithem Mezni2Mokhtar Sellami3Safa Ben Atitallah4Nouf Alharbi5Security Engineering Lab, CCIS, Prince Sultan University, Riyadh 12435, Saudi ArabiaRIADI Laboratory, University of Manouba, Manouba 2010, TunisiaCollege of Computer Science and Engineering, Taibah University, Madinah 42353, Saudi ArabiaRIADI Laboratory, University of Manouba, Manouba 2010, TunisiaRIADI Laboratory, University of Manouba, Manouba 2010, TunisiaCollege of Computer Science and Engineering, Taibah University, Madinah 42353, Saudi ArabiaHaving access to safe water and using it properly is crucial for human well-being, sustainable development, and environmental conservation. Nonetheless, the increasing disparity between human demands and natural freshwater resources is causing water scarcity, negatively impacting agricultural and industrial efficiency, and giving rise to numerous social and economic issues. Understanding and managing the causes of water scarcity and water quality degradation are essential steps toward more sustainable water management and use. In this context, continuous Internet of Things (IoT)-based water measurements are becoming increasingly crucial in environmental monitoring. However, these measurements are plagued by uncertainty issues that, if not handled correctly, can introduce bias and inaccuracy into our analysis, decision-making processes, and results. To cope with uncertainty issues related to sensed water data, we propose combining network representation learning with uncertainty handling methods to ensure rigorous and efficient modeling management of water resources. The proposed approach involves accounting for uncertainties in the water information system by leveraging probabilistic techniques and network representation learning. It creates a probabilistic embedding of the network, enabling the classification of uncertain representations of water information entities, and applies evidence theory to enable decision making that is aware of uncertainties, ultimately choosing appropriate management strategies for affected water areas.https://www.mdpi.com/1424-8220/23/10/4672smart water environmentswater information networknetwork representation learninguncertainty modelingwater monitoringsensor cloud services |
| spellingShingle | Maha Driss Wadii Boulila Haithem Mezni Mokhtar Sellami Safa Ben Atitallah Nouf Alharbi An Evidence Theory Based Embedding Model for the Management of Smart Water Environments Sensors smart water environments water information network network representation learning uncertainty modeling water monitoring sensor cloud services |
| title | An Evidence Theory Based Embedding Model for the Management of Smart Water Environments |
| title_full | An Evidence Theory Based Embedding Model for the Management of Smart Water Environments |
| title_fullStr | An Evidence Theory Based Embedding Model for the Management of Smart Water Environments |
| title_full_unstemmed | An Evidence Theory Based Embedding Model for the Management of Smart Water Environments |
| title_short | An Evidence Theory Based Embedding Model for the Management of Smart Water Environments |
| title_sort | evidence theory based embedding model for the management of smart water environments |
| topic | smart water environments water information network network representation learning uncertainty modeling water monitoring sensor cloud services |
| url | https://www.mdpi.com/1424-8220/23/10/4672 |
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