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?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.31223/x5p078
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.31223/x5p078
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 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.Traditional statistical tools usually lead to a clear interpretation at the expense of large performance, which is otherwise reached via machine/deep learning solutions, though at the expense of interpretation. Explainable AI is the key to combine both strengths and 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.
Related Topics
Concepts
Context (archaeology)
Interpretation (philosophy)
Computer science
Hazard
Spatial contextual awareness
Artificial intelligence
Process (computing)
Set (abstract data type)
Architecture
Machine learning
Deep learning
Artificial neural network
Data science
Risk analysis (engineering)
Geography
Archaeology
Medicine
Programming language
Chemistry
Operating system
Organic chemistry
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.31223/x5p078
- https://eartharxiv.org/repository/object/5257/download/10363/
- OA Status
- gold
- Cited By
- 4
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4365454145
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4365454145Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.31223/x5p078Digital Object Identifier
- Title
-
On the use of explainable AI for susceptibility modeling: examining the spatial pattern of SHAP valuesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-04-13Full publication date if available
- Authors
-
Nan Wang, Hongyan Zhang, Ashok Dahal, Weiming Cheng, Min Zhao, Luigi LombardoList of authors in order
- Landing page
-
https://doi.org/10.31223/x5p078Publisher landing page
- PDF URL
-
https://eartharxiv.org/repository/object/5257/download/10363/Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://eartharxiv.org/repository/object/5257/download/10363/Direct OA link when available
- Concepts
-
Context (archaeology), Interpretation (philosophy), Computer science, Hazard, Spatial contextual awareness, Artificial intelligence, Process (computing), Set (abstract data type), Architecture, Machine learning, Deep learning, Artificial neural network, Data science, Risk analysis (engineering), Geography, Archaeology, Medicine, Programming language, Chemistry, Operating system, Organic chemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
4Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.defined | 10 |
| abstract_inverted_index.expense | 143, 158 |
| abstract_inverted_index.explore | 175 |
| abstract_inverted_index.extract | 65 |
| abstract_inverted_index.floods) | 16 |
| abstract_inverted_index.hazards | 21 |
| abstract_inverted_index.leading | 29 |
| abstract_inverted_index.looking | 109 |
| abstract_inverted_index.losses. | 34 |
| abstract_inverted_index.mapping | 131 |
| abstract_inverted_index.natural | 4, 20 |
| abstract_inverted_index.network | 83 |
| abstract_inverted_index.overall | 117 |
| abstract_inverted_index.pattern | 93 |
| abstract_inverted_index.process | 204 |
| abstract_inverted_index.reached | 150 |
| abstract_inverted_index.reason, | 37 |
| abstract_inverted_index.results | 89 |
| abstract_inverted_index.spatial | 92 |
| abstract_inverted_index.threats | 25 |
| abstract_inverted_index.through | 90 |
| abstract_inverted_index.usually | 135 |
| abstract_inverted_index.actions. | 211 |
| abstract_inverted_index.controls | 115 |
| abstract_inverted_index.disaster | 206 |
| abstract_inverted_index.dynamics | 40 |
| abstract_inverted_index.globally | 18 |
| abstract_inverted_index.learning | 62, 153 |
| abstract_inverted_index.society, | 28 |
| abstract_inverted_index.spectrum | 9 |
| abstract_inverted_index.advantage | 57 |
| abstract_inverted_index.contained | 6 |
| abstract_inverted_index.interpret | 86 |
| abstract_inverted_index.modeling. | 184 |
| abstract_inverted_index.occurring | 19 |
| abstract_inverted_index.otherwise | 149 |
| abstract_inverted_index.predictor | 113 |
| abstract_inverted_index.processes | 1 |
| abstract_inverted_index.strengths | 169 |
| abstract_inverted_index.values.In | 96 |
| abstract_inverted_index.economical | 33 |
| abstract_inverted_index.fatalities | 31 |
| abstract_inverted_index.mitigation | 208 |
| abstract_inverted_index.phenomenon | 5 |
| abstract_inverted_index.prediction | 104 |
| abstract_inverted_index.prevention | 210 |
| abstract_inverted_index.solutions, | 154 |
| abstract_inverted_index.supporting | 201 |
| abstract_inverted_index.territory. | 78 |
| abstract_inverted_index.understand | 101 |
| abstract_inverted_index.Explainable | 161 |
| abstract_inverted_index.assessment. | 51 |
| abstract_inverted_index.combination | 177 |
| abstract_inverted_index.data-driven | 199 |
| abstract_inverted_index.demonstrate | 187 |
| abstract_inverted_index.explainable | 60 |
| abstract_inverted_index.occurrences | 73 |
| abstract_inverted_index.statistical | 133 |
| abstract_inverted_index.architecture | 84 |
| abstract_inverted_index.hierarchical | 107 |
| abstract_inverted_index.machine/deep | 152 |
| abstract_inverted_index.performance, | 146 |
| abstract_inverted_index.Specifically, | 185 |
| abstract_inverted_index.understanding | 38 |
| abstract_inverted_index.interpretation | 140 |
| abstract_inverted_index.susceptibility | 118, 183 |
| abstract_inverted_index.decision-making | 203 |
| abstract_inverted_index.interpretation, | 200 |
| abstract_inverted_index.interpretation. | 160 |
| abstract_inverted_index.interpretations | 69 |
| abstract_inverted_index.unit.Traditional | 132 |
| abstract_inverted_index.Hydro-morphological | 0 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/13 |
| sustainable_development_goals[0].score | 0.800000011920929 |
| sustainable_development_goals[0].display_name | Climate action |
| citation_normalized_percentile.value | 0.6885005 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |