Machine Learning and AI-Driven Water Quality Monitoring and Treatment
This study examines the latest utilization of the combination of machine learning (ML) and artificial intelligence (AI) in the monitoring and upgrading of water quality, which has become a crucial component of environmental management. In this paper, a thorough examination of modern methods and rece...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/35/e3sconf_icarae2023_03012.pdf |
| _version_ | 1797235229504569344 |
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| author | Rajitha Akula K Aravinda Nagpal Amandeep Kalra Ravi Maan Preeti Kumar Ashish Abdul-Zahra Dalael Saad |
| author_facet | Rajitha Akula K Aravinda Nagpal Amandeep Kalra Ravi Maan Preeti Kumar Ashish Abdul-Zahra Dalael Saad |
| author_sort | Rajitha Akula |
| collection | DOAJ |
| description | This study examines the latest utilization of the combination of machine learning (ML) and artificial intelligence (AI) in the monitoring and upgrading of water quality, which has become a crucial component of environmental management. In this paper, a thorough examination of modern methods and recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) algorithms, which have considerably enhanced the precision and effectiveness of water quality tracking systems. The study analyzes the integration of these innovations into water treatment methods, focusing their ability to more efficiently identify and reduce contaminants compared to traditional techniques. This paper examines a collection of case studies in which artificial intelligence (AI)-powered devices have been used, showcasing significant developments in the evaluation of water quality and improved levels of treatment efficiency. The present study additionally analyzes the various problems and potential future developments of Artificial Intelligence (AI) and Machine Learning (ML) within this particular domain. These challenges cover issues of scalability, data security, as well as the importance for interdisciplinary collaboration. This paper gives a comprehensive analysis of the impact of AI and ML technologies on water quality management, demonstrating their potential to transform current practices towards greater sustainability and efficiency. |
| first_indexed | 2024-04-24T16:44:38Z |
| format | Article |
| id | doaj.art-0814f347be2541ceabbfb8285d7bbb1f |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-04-24T16:44:38Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-0814f347be2541ceabbfb8285d7bbb1f2024-03-29T08:30:07ZengEDP SciencesE3S Web of Conferences2267-12422024-01-015050301210.1051/e3sconf/202450503012e3sconf_icarae2023_03012Machine Learning and AI-Driven Water Quality Monitoring and TreatmentRajitha Akula0K Aravinda1Nagpal Amandeep2Kalra Ravi3Maan Preeti4Kumar Ashish5Abdul-Zahra Dalael Saad6Institute of Aeronautical EngineeringDepartment of Electronics and Communication Engineering, New Horizon College of EngineeringLovely Professional UniversityLloyd Institute of Engineering & TechnologyLloyd Institute of Management and TechnologyDepartment of Mechanical Engineering, IES College of TechnologyHilla university collegeThis study examines the latest utilization of the combination of machine learning (ML) and artificial intelligence (AI) in the monitoring and upgrading of water quality, which has become a crucial component of environmental management. In this paper, a thorough examination of modern methods and recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) algorithms, which have considerably enhanced the precision and effectiveness of water quality tracking systems. The study analyzes the integration of these innovations into water treatment methods, focusing their ability to more efficiently identify and reduce contaminants compared to traditional techniques. This paper examines a collection of case studies in which artificial intelligence (AI)-powered devices have been used, showcasing significant developments in the evaluation of water quality and improved levels of treatment efficiency. The present study additionally analyzes the various problems and potential future developments of Artificial Intelligence (AI) and Machine Learning (ML) within this particular domain. These challenges cover issues of scalability, data security, as well as the importance for interdisciplinary collaboration. This paper gives a comprehensive analysis of the impact of AI and ML technologies on water quality management, demonstrating their potential to transform current practices towards greater sustainability and efficiency.https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/35/e3sconf_icarae2023_03012.pdfmachine learningartificial intelligencewater quality monitoringwater treatment technologies environmental managementai algorithms in water managementsustainability in water resourcesdata-driven water treatment |
| spellingShingle | Rajitha Akula K Aravinda Nagpal Amandeep Kalra Ravi Maan Preeti Kumar Ashish Abdul-Zahra Dalael Saad Machine Learning and AI-Driven Water Quality Monitoring and Treatment E3S Web of Conferences machine learning artificial intelligence water quality monitoring water treatment technologies environmental management ai algorithms in water management sustainability in water resources data-driven water treatment |
| title | Machine Learning and AI-Driven Water Quality Monitoring and Treatment |
| title_full | Machine Learning and AI-Driven Water Quality Monitoring and Treatment |
| title_fullStr | Machine Learning and AI-Driven Water Quality Monitoring and Treatment |
| title_full_unstemmed | Machine Learning and AI-Driven Water Quality Monitoring and Treatment |
| title_short | Machine Learning and AI-Driven Water Quality Monitoring and Treatment |
| title_sort | machine learning and ai driven water quality monitoring and treatment |
| topic | machine learning artificial intelligence water quality monitoring water treatment technologies environmental management ai algorithms in water management sustainability in water resources data-driven water treatment |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2024/35/e3sconf_icarae2023_03012.pdf |
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