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The latest innovative avenues for the utilization of artificial Intelligence and big data analytics in water resource management
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Kamyab, Hesam | - |
| dc.contributor.author | Khademi, Tayebeh | - |
| dc.contributor.author | Chelliapan, Shreeshivadasan | - |
| dc.contributor.author | SaberiKamarposhti, Morteza | - |
| dc.contributor.author | Rezania, Shahabaldin | - |
| dc.contributor.author | Yusuf, Mohammad | - |
| dc.contributor.author | Farajnezhad, Mohammad | - |
| dc.contributor.author | Abbas, Mohamed | - |
| dc.contributor.author | Hun Jeon, Byong | - |
| dc.contributor.author | Ahn, Yongtae | - |
| dc.date.accessioned | 2024-12-20T07:56:00Z | - |
| dc.date.available | 2024-12-20T07:56:00Z | - |
| dc.date.issued | 2023-12 | - |
| dc.identifier.issn | 2590-1230 | - |
| dc.identifier.issn | 2590-1230 | - |
| dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/203985 | - |
| dc.description.abstract | The effective management of water resources is essential to environmental stewardship and sustainable development. Traditional approaches to water resource management (WRM) struggle with real-time data acquisition, effective data analysis, and intelligent decision-making. To address these challenges, innovative solutions are required. Artificial Intelligence (AI) and Big Data Analytics (BDA) are at the forefront and have the potential to revolutionize the way water resources are managed. This paper reviews the current applications of AI and BDA in WRM, highlighting their capacity to overcome existing limitations. It includes the investigation of AI technologies, such as machine learning and deep learning, and their diverse applications to water quality monitoring, water allocation, and water demand forecasting. In addition, the review explores the role of BDA in the management of water resources, elaborating on the various data sources that can be used, such as remote sensing, IoT devices, and social media. In conclusion, the study synthesizes key insights and outlines prospective directions for leveraging AI and BDA for optimal water resource allocation. | - |
| dc.format.extent | 11 | - |
| dc.language | 영어 | - |
| dc.language.iso | ENG | - |
| dc.publisher | Elsevier | - |
| dc.title | The latest innovative avenues for the utilization of artificial Intelligence and big data analytics in water resource management | - |
| dc.type | Article | - |
| dc.publisher.location | 네델란드 | - |
| dc.identifier.doi | 10.1016/j.rineng.2023.101566 | - |
| dc.identifier.scopusid | 2-s2.0-85176242418 | - |
| dc.identifier.wosid | 001114230600001 | - |
| dc.identifier.bibliographicCitation | Results in Engineering, v.20, pp 1 - 11 | - |
| dc.citation.title | Results in Engineering | - |
| dc.citation.volume | 20 | - |
| dc.citation.startPage | 1 | - |
| dc.citation.endPage | 11 | - |
| dc.type.docType | Article | - |
| dc.description.isOpenAccess | Y | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.description.journalRegisteredClass | esci | - |
| dc.relation.journalResearchArea | Engineering | - |
| dc.relation.journalWebOfScienceCategory | Engineering, Multidisciplinary | - |
| dc.subject.keywordPlus | QUALITY | - |
| dc.subject.keywordAuthor | Artificial intelligence | - |
| dc.subject.keywordAuthor | Big data analytics | - |
| dc.subject.keywordAuthor | Water demand forecasting | - |
| dc.subject.keywordAuthor | Water quality monitoring | - |
| dc.subject.keywordAuthor | Water resource management | - |
| dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S259012302300693X?via%3Dihub | - |
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