On the use of explainable AI for susceptibility modeling: Examining the spatial pattern of SHAP values Article Swipe
Nan Wang
,
Hongyan Zhang
,
Ashok Dahal
,
Weiming Cheng
,
Min Zhao
,
Luigi Lombardo
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.gsf.2024.101800
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.gsf.2024.101800
Hydro-morphological processes (HMP, any natural phenomenon contained within the spectrum defined between debris flows and flash floods) are globally occurring natural hazards which pose great threats to our society, leading to fatalities and economical losses. For this reason, understanding the dynamics behind HMPs is needed to aid in hazard and risk assessment. In this work, we take advantage of an explainable deep learning model to extract global and local interpretations of the HMP occurrences across the whole Chinese territory. We use a deep neural network architecture and interpret the model results through the spatial pattern of SHAP values. In doing so, we can understand the model prediction on a hierarchical basis, looking at how the predictor set controls the overall susceptibility as well as doing the same at the level of the single mapping unit. Our model accurately predicts HMP occurrences with AUC values measured in a ten-fold cross-validation ranging between 0.83 and 0.86. This level of predictive performance attests for an excellent prediction skill. The main difference with respect to traditional statistical tools is that the latter usually lead to a clear interpretation at the expense of high performance, which is otherwise reached via machine/deep learning solutions, though at the expense of interpretation. The recent development of explainable AI is the key to combine both strengths. In this work, we explore this combination in the context of HMP susceptibility modeling. Specifically, we demonstrate the extent to which one can enter a new level of data-driven interpretation, supporting the decision-making process behind disaster risk mitigation and prevention actions.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.gsf.2024.101800
- OA Status
- hybrid
- Cited By
- 50
- References
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- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391481698Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.gsf.2024.101800Digital Object Identifier
- Title
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On the use of explainable AI for susceptibility modeling: Examining the spatial pattern of SHAP valuesWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-02-02Full publication date if available
- Authors
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Nan Wang, Hongyan Zhang, Ashok Dahal, Weiming Cheng, Min Zhao, Luigi LombardoList of authors in order
- Landing page
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https://doi.org/10.1016/j.gsf.2024.101800Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.gsf.2024.101800Direct OA link when available
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Context (archaeology), Computer science, Interpretation (philosophy), Artificial intelligence, Spatial contextual awareness, Deep learning, Hazard, Set (abstract data type), Artificial neural network, Machine learning, Data science, Geography, Archaeology, Programming language, Organic chemistry, ChemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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50Total citation count in OpenAlex
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2025: 38, 2024: 12Per-year citation counts (last 5 years)
- References (count)
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116Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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