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

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 XAIbased 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. (c) 2023 China University of Geosciences (Beijing) and Peking University. Published by Elsevier B.V. on behalf of China University of Geosciences (Beijing). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

Floods are natural disasters with severe impacts. Flood susceptibility maps can provide valuable information for reducing flood risks. This study proposes an explainable artificial intelligence (XAI) model, integrating the SHAP model, to interpret CNN deep learning models and analyze the variables influencing flood susceptibility mapping.