LEVERAGING AI AND MACHINE LEARNING FOR SUSTAINABLE URBAN WETLAND RESOURCE MANAGEMENT: INSIGHT FROM A WETLAND CONSERVATION LENS

Show simple item record

dc.contributor.author Diwyanjalee, G.R.
dc.contributor.author Bellanthudawa, B. K. A.
dc.date.accessioned 2024-01-31T03:40:45Z
dc.date.available 2024-01-31T03:40:45Z
dc.date.issued 2023-12-19
dc.identifier.citation Proceedings of International Conference on EcoHealth Nexus: Bridging Cascade Ecology and Human Well-Being en_US
dc.identifier.isbn 978-624-5884-24-
dc.identifier.uri http://repository.rjt.ac.lk/handle/123456789/6737
dc.description.abstract The critical advancements in sustainable water resource management and associated challenges utilize diverse methodologies, including machine learning and innovative approaches, to address the complex dynamics of water resources. The pre- vious studies of sustainable urban wetland resource management examine various re- gions and water systems, underlining the significance of informed decision-making, sustainable practices, and advanced mathematical paradigms. Hence, a systematic re- view of primary research was conducted to provide insight from a wetland conserva- tion lens through leveraging AI and machine learning for sustainable urban wetland resource management. The process of article screening was executed by adopting search keywords such as "Machine Learning", "Responsible Governance", "Sustain- ability", "Sustainable Technology", "Water Resource Management" and "Wetland Conservation" using the Web of Science database. The peer-reviewed articles pub- lished in English from 2018 to 2023 were included in content analysis and thematic analysis. The study's findings revealed the potential of machine learning and data- driven insights in enhancing water resource management. A hybrid model accurately predicts daily flow rates, demonstrating the transformative power of technological innovation. Societal involvement in decision-making reinforces the role of responsi- ble governance in sustainable water management. Innovative "soft sensor" ap- proaches for real-time phosphorus removal monitoring promise significant cost sav- ings in wastewater treatment. It explores the crucial connection between technology and sustainability, highlighting societal actors and innovative governance frame- works. In conclusion, informed decision-making, interdisciplinary collaboration, and responsible governance are vital for addressing water management challenges, paving the way for a more sustainable and water-secure future. en_US
dc.language.iso en en_US
dc.publisher Rajarata University of Sri Lanka en_US
dc.subject Machine learning en_US
dc.subject Sustainable technology en_US
dc.subject Water resource management en_US
dc.subject Wetland conservation en_US
dc.title LEVERAGING AI AND MACHINE LEARNING FOR SUSTAINABLE URBAN WETLAND RESOURCE MANAGEMENT: INSIGHT FROM A WETLAND CONSERVATION LENS en_US
dc.type Article en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search RUSL-IR


Browse

My Account