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An Explainable Ensemble Machine Learning Framework for Flood Susceptibility Mapping Using Social Media Data: A Case Study of Guangzhou, China.

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Title: An Explainable Ensemble Machine Learning Framework for Flood Susceptibility Mapping Using Social Media Data: A Case Study of Guangzhou, China.
Authors: Zhou, Yuhan1,2 (AUTHOR), Lu, Haipeng1,2 (AUTHOR) hplu@njnu.edu.cn, Liu, Sicen1,2,3 (AUTHOR), Zhang, Shuliang1,2,3 (AUTHOR)
Source: Remote Sensing. May2026, Vol. 18 Issue 10, p1495. 24p.
Subjects: Ensemble learning, Sampling (Process), Flood risk, Floods, Twitter (Web resource)
Geographic Terms: China, Guangzhou (China)
Abstract: Highlights: What are the main findings? An interpretable ensemble machine-learning framework integrating social media–derived flood inventories, optimized non-flood sampling, and GeoShapley-based explainability achieved strong flood susceptibility mapping performance in Guangzhou, with an AUC of 0.893 and a precision of 0.859. The flood susceptibility map produced in this study indicates that areas with High and Very-high susceptibility together cover about 26% of the study area (1897.23 km2). Interpretability analysis identifies the nighttime light index, impervious surface percentage, and population density as the most strongly associated positive factors in the model. What are the implications of the main findings? A non-flood sampling strategy that jointly considers sample similarity and diversity can significantly improve model performance and generalization ability in flood susceptibility mapping. By improving both predictive accuracy and model interpretability, the proposed framework provides scientific support for flood risk identification, spatial planning, and targeted urban flood mitigation strategies. With the intensification of global climate change and rapid urbanization, urban flooding poses an increasing threat to urban safety and sustainable development. Flood susceptibility mapping (FSM) serves as a practical approach for recognizing areas that may be vulnerable to flooding and is therefore essential for flood mitigation and urban planning. In this study, an interpretable ensemble machine-learning framework for urban FSM was developed using social media data. First, the spatial locations of flood events were extracted from social media posts and news reports to construct a flood inventory. Subsequently, a non-flood sample selection strategy, termed Similarity- and Diversity-Based Representative Sampling (SDRS), was proposed to ensure both sample similarity and diversity. Based on these samples, a heterogeneous bagging-based ensemble machine learning model was established for flood susceptibility assessment. To enhance model interpretability, the GeoShapley method was introduced to quantify the contributions of key conditioning factors and reveal their directional effects. The findings indicated that the proposed SDRS strategy delivered the best performance, yielding an AUC of 0.893 and a test-set precision of 0.859. The resulting susceptibility map exhibited a clear south-to-north decreasing gradient, with High- and Very-high-susceptibility zones accounting for approximately 26% of the study area (1897.23 km2). The interpretability analysis further indicated that the Nighttime Light Index (NLI), Impervious Surface Percentage (ISP), and population density were among the most strongly associated positive factors in the model, with a Global Spatial Share of 7.18%. These findings demonstrate that the proposed framework can reliably recognize areas vulnerable to flooding and offer a scientific basis for urban flood management in Guangzhou. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: An Explainable Ensemble Machine Learning Framework for Flood Susceptibility Mapping Using Social Media Data: A Case Study of Guangzhou, China.
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. May2026, Vol. 18 Issue 10, p1495. 24p.
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  Data: <searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br /><searchLink fieldCode="DE" term="%22Sampling+%28Process%29%22">Sampling (Process)</searchLink><br /><searchLink fieldCode="DE" term="%22Flood+risk%22">Flood risk</searchLink><br /><searchLink fieldCode="DE" term="%22Floods%22">Floods</searchLink><br /><searchLink fieldCode="DE" term="%22Twitter+%28Web+resource%29%22">Twitter (Web resource)</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink><br /><searchLink fieldCode="DE" term="%22Guangzhou+%28China%29%22">Guangzhou (China)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? An interpretable ensemble machine-learning framework integrating social media–derived flood inventories, optimized non-flood sampling, and GeoShapley-based explainability achieved strong flood susceptibility mapping performance in Guangzhou, with an AUC of 0.893 and a precision of 0.859. The flood susceptibility map produced in this study indicates that areas with High and Very-high susceptibility together cover about 26% of the study area (1897.23 km2). Interpretability analysis identifies the nighttime light index, impervious surface percentage, and population density as the most strongly associated positive factors in the model. What are the implications of the main findings? A non-flood sampling strategy that jointly considers sample similarity and diversity can significantly improve model performance and generalization ability in flood susceptibility mapping. By improving both predictive accuracy and model interpretability, the proposed framework provides scientific support for flood risk identification, spatial planning, and targeted urban flood mitigation strategies. With the intensification of global climate change and rapid urbanization, urban flooding poses an increasing threat to urban safety and sustainable development. Flood susceptibility mapping (FSM) serves as a practical approach for recognizing areas that may be vulnerable to flooding and is therefore essential for flood mitigation and urban planning. In this study, an interpretable ensemble machine-learning framework for urban FSM was developed using social media data. First, the spatial locations of flood events were extracted from social media posts and news reports to construct a flood inventory. Subsequently, a non-flood sample selection strategy, termed Similarity- and Diversity-Based Representative Sampling (SDRS), was proposed to ensure both sample similarity and diversity. Based on these samples, a heterogeneous bagging-based ensemble machine learning model was established for flood susceptibility assessment. To enhance model interpretability, the GeoShapley method was introduced to quantify the contributions of key conditioning factors and reveal their directional effects. The findings indicated that the proposed SDRS strategy delivered the best performance, yielding an AUC of 0.893 and a test-set precision of 0.859. The resulting susceptibility map exhibited a clear south-to-north decreasing gradient, with High- and Very-high-susceptibility zones accounting for approximately 26% of the study area (1897.23 km2). The interpretability analysis further indicated that the Nighttime Light Index (NLI), Impervious Surface Percentage (ISP), and population density were among the most strongly associated positive factors in the model, with a Global Spatial Share of 7.18%. These findings demonstrate that the proposed framework can reliably recognize areas vulnerable to flooding and offer a scientific basis for urban flood management in Guangzhou. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs18101495
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 24
        StartPage: 1495
    Subjects:
      – SubjectFull: Ensemble learning
        Type: general
      – SubjectFull: Sampling (Process)
        Type: general
      – SubjectFull: Flood risk
        Type: general
      – SubjectFull: Floods
        Type: general
      – SubjectFull: Twitter (Web resource)
        Type: general
      – SubjectFull: China
        Type: general
      – SubjectFull: Guangzhou (China)
        Type: general
    Titles:
      – TitleFull: An Explainable Ensemble Machine Learning Framework for Flood Susceptibility Mapping Using Social Media Data: A Case Study of Guangzhou, China.
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            NameFull: Zhou, Yuhan
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            NameFull: Lu, Haipeng
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            NameFull: Liu, Sicen
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            NameFull: Zhang, Shuliang
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            – D: 15
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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