Work

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

Year: 2024

Type: article

Abstract: 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.

Source: Sustainability

DOI: 10.3390/su16208805

Authors: Joonghyeok Heo, Jeongho Lee, Yunjung Hyun, Joonkyu Park

Institutions: The University of Texas of the Permian Basin, Korea Environment Institute, Seoil University

Language: English

Topic: Water resources management and optimization

Subfield: Ocean Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Climate action, Clean water and sanitation, Life on land, Sustainable cities and communities

Open Access status: gold

Estimated APC paid: $2,728