Citation
Kamyab, Hesam and Khademi, Tayebeh and Chelliapan, Shreeshivadasan and SaberiKamarposhti, Morteza and Rezania, Shahabaldin and Yusuf, Mohammad and Farajnezhad, Mohammad and Abbas, Mohamed and Hun Jeon, Byong and Ahn, Yongtae (2023) The latest innovative avenues for the utilization of artificial Intelligence and big data analytics in water resource management. Results in Engineering, 20. p. 101566. ISSN 2590-1230
Text
8.pdf - Published Version Restricted to Repository staff only Download (3MB) |
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.
Item Type: | Article |
---|---|
Uncontrolled Keywords: | Artificial intelligence, Big data analytics, Water resource management, Water quality monitoring, Water demand forecasting |
Subjects: | Q Science > Q Science (General) > Q300-390 Cybernetics |
Divisions: | Faculty of Computing and Informatics (FCI) |
Depositing User: | Ms Nurul Iqtiani Ahmad |
Date Deposited: | 07 Dec 2023 03:34 |
Last Modified: | 07 Dec 2023 03:34 |
URII: | http://shdl.mmu.edu.my/id/eprint/11942 |
Downloads
Downloads per month over past year
Edit (login required) |