Spatial flood susceptibility mapping using an explainable artificial intelligence (XAI) model Article Swipe
Biswajeet Pradhan
,
Saro Lee
,
Abhirup Dikshit
,
Hyesu Kim
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.gsf.2023.101625
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.gsf.2023.101625
Floods are natural hazards that lead to devastating financial losses and large displacements of people. Flood susceptibility maps can improve mitigation measures according to the specific conditions of a study area. The design of flood susceptibility maps has been enhanced through use of hybrid machine learning and deep learning models. Although these models have achieved better accuracy than traditional models, they are not widely used by stakeholders due to their black-box nature. In this study, we propose the application of an explainable artificial intelligence (XAI) model that incorporates the Shapley additive explanation (SHAP) model to interpret the outcomes of convolutional neural network (CNN) deep learning models, and analyze the impact of variables on flood susceptibility mapping. This study was conducted in Jinju Province, South Korea, which has a long history of flood events. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC), which showed a prediction accuracy of 88.4%. SHAP plots showed that land use and various soil attributes significantly affected flood susceptibility in the study area. In light of these findings, we recommend the use of XAI-based models in future flood susceptibility mapping studies to improve interpretations of model outcomes, and build trust among stakeholders during the flood-related decision-making process.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.gsf.2023.101625
- OA Status
- hybrid
- Cited By
- 158
- References
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- OpenAlex ID
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All OpenAlex metadata
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https://doi.org/10.1016/j.gsf.2023.101625Digital Object Identifier
- Title
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Spatial flood susceptibility mapping using an explainable artificial intelligence (XAI) modelWork title
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articleOpenAlex work type
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enPrimary language
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2023Year of publication
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2023-04-28Full publication date if available
- Authors
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Biswajeet Pradhan, Saro Lee, Abhirup Dikshit, Hyesu KimList of authors in order
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https://doi.org/10.1016/j.gsf.2023.101625Publisher landing page
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hybridOpen access status per OpenAlex
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https://doi.org/10.1016/j.gsf.2023.101625Direct OA link when available
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Flood myth, Computer science, Deep learning, Artificial intelligence, Process (computing), Data mining, Machine learning, Water resource management, Environmental science, Geography, Archaeology, Operating systemTop concepts (fields/topics) attached by OpenAlex
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158Total citation count in OpenAlex
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2025: 83, 2024: 58, 2023: 17Per-year citation counts (last 5 years)
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| referenced_works | https://openalex.org/W3181256602, https://openalex.org/W2907882001, https://openalex.org/W3005703423, https://openalex.org/W2035354641, https://openalex.org/W2985766090, https://openalex.org/W2072509556, https://openalex.org/W2895196240, https://openalex.org/W2404576049, https://openalex.org/W2903266193, https://openalex.org/W2342485655, https://openalex.org/W6783903821, https://openalex.org/W3037310288, https://openalex.org/W3195433497, https://openalex.org/W3000113736, https://openalex.org/W2610838636, https://openalex.org/W2158698691, https://openalex.org/W2156589291, https://openalex.org/W2996713777, https://openalex.org/W2401869809, https://openalex.org/W2106784335, https://openalex.org/W1981316911, https://openalex.org/W2765742909, https://openalex.org/W3087236291, https://openalex.org/W2027386095, https://openalex.org/W154049821, https://openalex.org/W2112796928, https://openalex.org/W2919115771, https://openalex.org/W2331149499, https://openalex.org/W2606804832, https://openalex.org/W2789282353, https://openalex.org/W2766400859, https://openalex.org/W3173991935, https://openalex.org/W2972629016, https://openalex.org/W6734575198, https://openalex.org/W3093442198, https://openalex.org/W2981078235, https://openalex.org/W2282821441, https://openalex.org/W2286383730, https://openalex.org/W2945976633, https://openalex.org/W2800469141, https://openalex.org/W2487898712, https://openalex.org/W2042315239, https://openalex.org/W1975914988, https://openalex.org/W2917112588, https://openalex.org/W2981051507, https://openalex.org/W2915483120, https://openalex.org/W3003401083, https://openalex.org/W2894920195, https://openalex.org/W3013498854, https://openalex.org/W4206280001, https://openalex.org/W3087676330, https://openalex.org/W4402843978, https://openalex.org/W2594352094, https://openalex.org/W4253460748 |
| referenced_works_count | 54 |
| abstract_inverted_index.a | 28, 127, 149 |
| abstract_inverted_index.In | 72, 172 |
| abstract_inverted_index.an | 80 |
| abstract_inverted_index.by | 65 |
| abstract_inverted_index.in | 120, 168, 184 |
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| abstract_inverted_index.to | 6, 23, 68, 94, 190 |
| abstract_inverted_index.we | 75, 177 |
| abstract_inverted_index.The | 31 |
| abstract_inverted_index.and | 10, 46, 106, 160, 196 |
| abstract_inverted_index.are | 1, 61 |
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| abstract_inverted_index.due | 67 |
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| abstract_inverted_index.not | 62 |
| abstract_inverted_index.the | 24, 77, 88, 96, 108, 138, 141, 169, 179, 202 |
| abstract_inverted_index.use | 41, 159, 180 |
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| abstract_inverted_index.SHAP | 154 |
| abstract_inverted_index.This | 116 |
| abstract_inverted_index.area | 139 |
| abstract_inverted_index.been | 38 |
| abstract_inverted_index.deep | 47, 103 |
| abstract_inverted_index.have | 53 |
| abstract_inverted_index.land | 158 |
| abstract_inverted_index.lead | 5 |
| abstract_inverted_index.long | 128 |
| abstract_inverted_index.maps | 17, 36 |
| abstract_inverted_index.soil | 162 |
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| abstract_inverted_index.that | 4, 86, 157 |
| abstract_inverted_index.they | 60 |
| abstract_inverted_index.this | 73 |
| abstract_inverted_index.used | 64 |
| abstract_inverted_index.(CNN) | 102 |
| abstract_inverted_index.(XAI) | 84 |
| abstract_inverted_index.Flood | 15 |
| abstract_inverted_index.Jinju | 121 |
| abstract_inverted_index.Model | 133 |
| abstract_inverted_index.South | 123 |
| abstract_inverted_index.among | 199 |
| abstract_inverted_index.area. | 30, 171 |
| abstract_inverted_index.build | 197 |
| abstract_inverted_index.curve | 145 |
| abstract_inverted_index.flood | 34, 113, 131, 166, 186 |
| abstract_inverted_index.large | 11 |
| abstract_inverted_index.light | 173 |
| abstract_inverted_index.model | 85, 93, 194 |
| abstract_inverted_index.plots | 155 |
| abstract_inverted_index.study | 29, 117, 170 |
| abstract_inverted_index.their | 69 |
| abstract_inverted_index.these | 51, 175 |
| abstract_inverted_index.trust | 198 |
| abstract_inverted_index.under | 140 |
| abstract_inverted_index.using | 137 |
| abstract_inverted_index.which | 125, 147 |
| abstract_inverted_index.(SHAP) | 92 |
| abstract_inverted_index.88.4%. | 153 |
| abstract_inverted_index.Floods | 0 |
| abstract_inverted_index.Korea, | 124 |
| abstract_inverted_index.better | 55 |
| abstract_inverted_index.design | 32 |
| abstract_inverted_index.during | 201 |
| abstract_inverted_index.future | 185 |
| abstract_inverted_index.hybrid | 43 |
| abstract_inverted_index.impact | 109 |
| abstract_inverted_index.losses | 9 |
| abstract_inverted_index.models | 52, 183 |
| abstract_inverted_index.neural | 100 |
| abstract_inverted_index.showed | 148, 156 |
| abstract_inverted_index.study, | 74 |
| abstract_inverted_index.widely | 63 |
| abstract_inverted_index.Shapley | 89 |
| abstract_inverted_index.analyze | 107 |
| abstract_inverted_index.events. | 132 |
| abstract_inverted_index.hazards | 3 |
| abstract_inverted_index.history | 129 |
| abstract_inverted_index.improve | 19, 191 |
| abstract_inverted_index.machine | 44 |
| abstract_inverted_index.mapping | 188 |
| abstract_inverted_index.models, | 59, 105 |
| abstract_inverted_index.models. | 49 |
| abstract_inverted_index.natural | 2 |
| abstract_inverted_index.nature. | 71 |
| abstract_inverted_index.network | 101 |
| abstract_inverted_index.people. | 14 |
| abstract_inverted_index.propose | 76 |
| abstract_inverted_index.studies | 189 |
| abstract_inverted_index.through | 40 |
| abstract_inverted_index.various | 161 |
| abstract_inverted_index.(AUROC), | 146 |
| abstract_inverted_index.Although | 50 |
| abstract_inverted_index.accuracy | 56, 151 |
| abstract_inverted_index.achieved | 54 |
| abstract_inverted_index.additive | 90 |
| abstract_inverted_index.affected | 165 |
| abstract_inverted_index.enhanced | 39 |
| abstract_inverted_index.learning | 45, 48, 104 |
| abstract_inverted_index.mapping. | 115 |
| abstract_inverted_index.measures | 21 |
| abstract_inverted_index.outcomes | 97 |
| abstract_inverted_index.process. | 205 |
| abstract_inverted_index.receiver | 142 |
| abstract_inverted_index.specific | 25 |
| abstract_inverted_index.Province, | 122 |
| abstract_inverted_index.XAI-based | 182 |
| abstract_inverted_index.according | 22 |
| abstract_inverted_index.black-box | 70 |
| abstract_inverted_index.conducted | 119 |
| abstract_inverted_index.evaluated | 136 |
| abstract_inverted_index.financial | 8 |
| abstract_inverted_index.findings, | 176 |
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| abstract_inverted_index.incorporates | 87 |
| abstract_inverted_index.intelligence | 83 |
| abstract_inverted_index.stakeholders | 66, 200 |
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| abstract_inverted_index.displacements | 12 |
| abstract_inverted_index.flood-related | 203 |
| abstract_inverted_index.significantly | 164 |
| abstract_inverted_index.characteristic | 144 |
| abstract_inverted_index.susceptibility | 16, 35, 114, 167, 187 |
| abstract_inverted_index.decision-making | 204 |
| abstract_inverted_index.interpretations | 192 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 99 |
| corresponding_author_ids | https://openalex.org/A5077439959 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I4210142421, https://openalex.org/I88761825 |
| citation_normalized_percentile.value | 0.99902982 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |