Comprehensive Assessment of Flood Socioeconomic Impacts Through Text‐Mining
Abstract In July 2021, Germany experienced its costliest riverine floods in history, with over 189 fatalities and a staggering €33 billion in damages. Following this event, news outlets widely disseminated information on the flood's aftermath. Here, we demonstrate how newspaper data can be inst...
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| Format: | Article |
| Language: | English |
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Wiley
2025-01-01
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| Series: | Water Resources Research |
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| Online Access: | https://doi.org/10.1029/2024WR037813 |
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| author | Mariana Madruga de Brito Jan Sodoge Heidi Kreibich Christian Kuhlicke |
| author_facet | Mariana Madruga de Brito Jan Sodoge Heidi Kreibich Christian Kuhlicke |
| author_sort | Mariana Madruga de Brito |
| collection | DOAJ |
| description | Abstract In July 2021, Germany experienced its costliest riverine floods in history, with over 189 fatalities and a staggering €33 billion in damages. Following this event, news outlets widely disseminated information on the flood's aftermath. Here, we demonstrate how newspaper data can be instrumental in the assessment of flood socioeconomic impacts often overlooked by conventional methods. Using natural language processing tools on 26,113 newspaper articles, we estimate the cascading impacts of the 2021 flood on various sectors and critical infrastructure, including water contamination, mental health, and tourism. Our results revealed severe and lasting impacts in the Ahr Valley, even months after the event. At the same time, we identified smaller‐scale yet widespread impacts across Germany, which are typically overlooked by existing impact databases. Our approach advances current research by systematically examining indirect and intangible flood consequences over large areas. This underscores the value of leveraging complementary text data to provide a more comprehensive picture of flood impacts. |
| format | Article |
| id | doaj-art-6c0b8a7d5b02482cae8930956294249d |
| institution | DOAJ |
| issn | 0043-1397 1944-7973 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Water Resources Research |
| spelling | doaj-art-6c0b8a7d5b02482cae8930956294249d2025-08-20T03:22:12ZengWileyWater Resources Research0043-13971944-79732025-01-01611n/an/a10.1029/2024WR037813Comprehensive Assessment of Flood Socioeconomic Impacts Through Text‐MiningMariana Madruga de Brito0Jan Sodoge1Heidi Kreibich2Christian Kuhlicke3Department of Urban and Environmental Sociology UFZ‐Helmholtz Centre for Environmental Research Leipzig GermanyDepartment of Urban and Environmental Sociology UFZ‐Helmholtz Centre for Environmental Research Leipzig GermanyGFZ‐German Research Centre for Geosciences Potsdam GermanyDepartment of Urban and Environmental Sociology UFZ‐Helmholtz Centre for Environmental Research Leipzig GermanyAbstract In July 2021, Germany experienced its costliest riverine floods in history, with over 189 fatalities and a staggering €33 billion in damages. Following this event, news outlets widely disseminated information on the flood's aftermath. Here, we demonstrate how newspaper data can be instrumental in the assessment of flood socioeconomic impacts often overlooked by conventional methods. Using natural language processing tools on 26,113 newspaper articles, we estimate the cascading impacts of the 2021 flood on various sectors and critical infrastructure, including water contamination, mental health, and tourism. Our results revealed severe and lasting impacts in the Ahr Valley, even months after the event. At the same time, we identified smaller‐scale yet widespread impacts across Germany, which are typically overlooked by existing impact databases. Our approach advances current research by systematically examining indirect and intangible flood consequences over large areas. This underscores the value of leveraging complementary text data to provide a more comprehensive picture of flood impacts.https://doi.org/10.1029/2024WR037813natural language processingfloodsimpactssocietal consequences |
| spellingShingle | Mariana Madruga de Brito Jan Sodoge Heidi Kreibich Christian Kuhlicke Comprehensive Assessment of Flood Socioeconomic Impacts Through Text‐Mining Water Resources Research natural language processing floods impacts societal consequences |
| title | Comprehensive Assessment of Flood Socioeconomic Impacts Through Text‐Mining |
| title_full | Comprehensive Assessment of Flood Socioeconomic Impacts Through Text‐Mining |
| title_fullStr | Comprehensive Assessment of Flood Socioeconomic Impacts Through Text‐Mining |
| title_full_unstemmed | Comprehensive Assessment of Flood Socioeconomic Impacts Through Text‐Mining |
| title_short | Comprehensive Assessment of Flood Socioeconomic Impacts Through Text‐Mining |
| title_sort | comprehensive assessment of flood socioeconomic impacts through text mining |
| topic | natural language processing floods impacts societal consequences |
| url | https://doi.org/10.1029/2024WR037813 |
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