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…

• UO
  University of Technology Sydney
• MO
  Ministry of Science, ICT and Future Planning
• KI
  Korea Institute of Geoscience and Mineral Resources
• NR
  National Research Foundation of Korea
• MO
  Ministry of Science and ICT, South Korea

  Award: 2023R1A2C1003095
• FO
  Faculty of Engineering and Information Technology, University of Technology Sydney