Pluvial flood susceptibility mapping for data-scarce urban areas using graph attention network and basic flood conditioning factors

Abstract

Pluvial floods are destructive natural disasters in cities. With high computational efficiency, machine learning models are increasingly used for flood susceptibility mapping. However, limited flooded or nonflooded samples constrain models’ predictive capability and introduce uncertainty in feature engineering. This study introduces a semi-supervised graph-structured model, Graph Attention Network (GAT), to address data scarcity and enable the use of only basic flood conditioning factors as inputs. GAT uses nodes and edges to represent spatial units and their relative spatial relationships. Based on its graph structure and attention mechanism, GAT automatically extracts high-order features from inputs of labeled and unlabeled units for modeling. In the metropolitan area of Dalian, China, GAT outperformed other models in flooded-nonflooded sample classification and exhibited a rational flood susceptibility distribution pattern, with only four basic flood conditioning factors and less than 1.2% of units for training. GAT can be an effective tool for practical urban flood management.