Integrating Machine Learning, Land Cover, and Hydrological Modeling to Contribute Parameters for Climate Impacts on Water Resource Management

• Published in: Sustainability 2024-10, Vol.16 (20), p.8805
• Main Authors: Heo, Joonghyeok, Lee, Jeongho, Hyun, Yunjung, Park, Joonkyu
• Format: Article
• Language: English
• Subjects: Aquatic resources; Climate change; Climatic changes; Geospatial data; Groundwater; Groundwater recharge; Hydrology; Land use; Machine learning; Management; Neural networks; Pollutants; Pollution; South Korea; Spatial data; Surface water; United Kingdom; Urban areas; Water; Water quality; Water resources management; Water supply; Water temperature; Web portals
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su16208805

The purpose of this study is to establish basic policies for managing the impacts of climate change on water resources using the integration of machine learning and land cover modeling. We predicted future changes in land cover within the water management and assessed its vulnerability to climate change. After confirming this vulnerability, we considered measures to improve climate resilience and presented future water resource parameters. We reviewed the finances available to promote climate projects, noting the major river management funds. The future project will serve as a stepping stone to promote climate resilience projects addressing water resource challenges exacerbated by future climate change. The study examined the results of analyzing changes in land cover maps due to climate change and assessed vulnerability in water management areas until 2050. According to the analysis results, the regulations for our study areas were set lower than those for other water management zones, resulting in a high rate of urbanization. Therefore, the climate resilience project in the water management area should be implemented first, despite the need for a long-term view in adapting to climate change.