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  • Presentation | H41V: Water and Society: Leveraging Digital Twins, AI, and Other Emerging Technologies to Support Management and Governance of Coupled Human-Water Systems II Poster
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  • H41V-1484: Explainable AI Reveals Time-Varying Effects of Media Coverage on Household Water Savings During a Drought
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  • Board 1484‚ Hall EFG (Poster Hall)
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Author(s):
Eunmi Lee, Pohang University of Science and Technology (First Author, Presenting Author)
Seunghui Choi, Pohang University of Science and Technology
Sangeun Lee, KRIHS Korea Research Institute for Human Settlements
Hyeoncheol Yoon, National Disaster Management Research Institute
Newsha Ajami, Lawrence Berkeley National Laboratory
Xiaogang He, National University of Singapore
Jonghun Kam, Pohang University of Science and Technology


Public participation in household water conservation is widely encouraged to reduce the impacts of drought, yet effective communication strategies remain limited. This study investigates how drought-related news coverage influences household water-saving behavior during a recent drought in South Korea.


We found that households in metropolitan areas conserved significantly more water than those in non-metropolitan regions, and that these water-saving efforts in metropolitan areas persisted even after the drought had subsided. Using an explainable AI-based scenario analysis, we show that increasing drought-related news articles by 100 during the onset stage can raise household water-saving rates by about 15% (yielding USD 0.5 million in benefits) in metropolitan areas and 5% (yielding USD 0.3 million in benefits) in non-metropolitan areas. However, these effects decline significantly during the later drought alert stage.


Our findings highlight the behavioral and economic impact of timely media coverage in early drought stages, especially in metropolitan areas, and emphasize the need for coordinated government-media efforts to sustain water conservation. This study provides insights for targeted, time-sensitive communication strategies to improve drought resilience, leveraging explainable AI to quantify and interpret behavioral responses.




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