Spatial flood susceptibility mapping using an explainable artificial intelligence (XAI) model

• Published in: Di xue qian yuan. Vol. 14; no. 6; pp. 101625 - 20
• Main Authors: Pradhan, Biswajeet, Lee, Saro, Dikshit, Abhirup, Kim, Hyesu
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2023; Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS),School of Civil and Environmental Engineering,Faculty of Engineering & IT,University of Technology Sydney,Sydney,NSW 2007,Australia; Earth Observation Center,Institute of Climate Change,Universiti Kebangsaan Malaysia,43600 UKM,Bangi,Selangor,Malaysia%Geoscience Data Center,Korea Institute of Geoscience and Mineral Resources(KIGAM),124 Gwahang-no,Yuseong-gu,Daejeon 34132,South Korea; Department of Resources Engineering,Korea University of Science and Technology,217 Gajeong-ro,Yuseong-gu,Daejeon 34113,South Korea%Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS),School of Civil and Environmental Engineering,Faculty of Engineering & IT,University of Technology Sydney,Sydney,NSW 2007,Australia%Department of Astronomy,Space Science and Geology,Chungnam National University,99 Daehak-ro,Yuseong-gu,Daejeon 34134,South Korea
• Subjects: Deep learning; Explainable AI; Flood susceptibility; South Korea; Deep learning; South Korea; Explainable AI; Flood susceptibility; Explainable Al
• ISSN: 1674-9871
• DOI: 10.1016/j.gsf.2023.101625

[Display omitted] •An explainable ML model was used for first time for flood susceptibility mapping.•Model achieved an accuracy of 88.4% for Jinju, South Korea.•SHAP plots showed land use and soil attributes to be most important. 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.