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.
