Data quality and uncertainty issues in flood prediction: a systematic review
The twenty-first century has witnessed an increase in the frequency and severity of flood events, amplifying the consequences of data quality and uncertainty in flood prediction. This study conducts a systematic literature review to critically examine the challenges associated with the quality and u...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2495738 |
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| _version_ | 1849224370064457728 |
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| author | Jinhui Yu Yichen Li Xiao Huang Xinyue Ye |
| author_facet | Jinhui Yu Yichen Li Xiao Huang Xinyue Ye |
| author_sort | Jinhui Yu |
| collection | DOAJ |
| description | The twenty-first century has witnessed an increase in the frequency and severity of flood events, amplifying the consequences of data quality and uncertainty in flood prediction. This study conducts a systematic literature review to critically examine the challenges associated with the quality and uncertainty of diverse data types related to flood prediction, evaluate existing mitigation strategies, and identify future research directions. The findings reveal that flood prediction relies on various data types, including hydrological data, rainfall patterns, infrastructure characteristics, and topographical information. These datasets often suffer from issues such as incompleteness, inconsistency, and accuracy deficits, further complicated by uncertainties arising from complex spatial features and environmental changes. The literature proposes a range of solutions, including the development of innovative methodologies, model construction, and comparative analysis, to address these challenges. Future research should place greater emphasis on underdeveloped regions, fostering more literature-based studies, exploring the application of artificial intelligence (AI) and the integration of other emerging technologies to better address data challenges, and developing novel data sources, such as real-time dynamic data and integrated datasets. This review offers critical insights into the development and practical application of flood prediction data while charting a course for future research in the field. |
| format | Article |
| id | doaj-art-6cb3efec0f354058b4839937bcc62be5 |
| institution | Kabale University |
| issn | 1753-8947 1753-8955 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | International Journal of Digital Earth |
| spelling | doaj-art-6cb3efec0f354058b4839937bcc62be52025-08-25T11:24:52ZengTaylor & Francis GroupInternational Journal of Digital Earth1753-89471753-89552025-08-0118110.1080/17538947.2025.2495738Data quality and uncertainty issues in flood prediction: a systematic reviewJinhui Yu0Yichen Li1Xiao Huang2Xinyue Ye3College of Civil Engineering and Architecture, Zhejiang University, Hangzhou, People’s Republic of ChinaCollege of Environmental and Resource Sciences, Zhejiang A&F University, Hangzhou, People’s Republic of ChinaDepartment of Environmental Sciences, Emory University, Atlanta, GA, USADepartment of Landscape Architecture & Urban Planning, Texas A&M University, College Station, TX, USAThe twenty-first century has witnessed an increase in the frequency and severity of flood events, amplifying the consequences of data quality and uncertainty in flood prediction. This study conducts a systematic literature review to critically examine the challenges associated with the quality and uncertainty of diverse data types related to flood prediction, evaluate existing mitigation strategies, and identify future research directions. The findings reveal that flood prediction relies on various data types, including hydrological data, rainfall patterns, infrastructure characteristics, and topographical information. These datasets often suffer from issues such as incompleteness, inconsistency, and accuracy deficits, further complicated by uncertainties arising from complex spatial features and environmental changes. The literature proposes a range of solutions, including the development of innovative methodologies, model construction, and comparative analysis, to address these challenges. Future research should place greater emphasis on underdeveloped regions, fostering more literature-based studies, exploring the application of artificial intelligence (AI) and the integration of other emerging technologies to better address data challenges, and developing novel data sources, such as real-time dynamic data and integrated datasets. This review offers critical insights into the development and practical application of flood prediction data while charting a course for future research in the field.https://www.tandfonline.com/doi/10.1080/17538947.2025.2495738Data qualitydata uncertaintyflood predictionflood modellingsystematic review |
| spellingShingle | Jinhui Yu Yichen Li Xiao Huang Xinyue Ye Data quality and uncertainty issues in flood prediction: a systematic review International Journal of Digital Earth Data quality data uncertainty flood prediction flood modelling systematic review |
| title | Data quality and uncertainty issues in flood prediction: a systematic review |
| title_full | Data quality and uncertainty issues in flood prediction: a systematic review |
| title_fullStr | Data quality and uncertainty issues in flood prediction: a systematic review |
| title_full_unstemmed | Data quality and uncertainty issues in flood prediction: a systematic review |
| title_short | Data quality and uncertainty issues in flood prediction: a systematic review |
| title_sort | data quality and uncertainty issues in flood prediction a systematic review |
| topic | Data quality data uncertainty flood prediction flood modelling systematic review |
| url | https://www.tandfonline.com/doi/10.1080/17538947.2025.2495738 |
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