Cutting-Edge strategies for absence data identification in natural hazards: Leveraging Voronoi-Entropy in flood susceptibility mapping with advanced AI techniques

Abstract

One of the non-structural methods for flood management is preparing flood susceptibility mapping (FSM). The performance of flood susceptibility models significantly depends on the learning methods and data sampling. Random sampling is commonly employed for data sampling owing to its practicality and straightforwardness. A purely random method may not be the best choice for non-flood sampling, as floods typically occur in different locations at different times. Therefore, this research aims to propose a new method for determining flood absence points using a combination of Voronoi diagrams and the entropy method. To achieve this, flood susceptibility modeling was conducted using the XGBoost (eXtreme Gradient Boosting) algorithm optimized with the cat swarm optimization (CSO) algorithm, employing the proposed method for absence point determination in the sub-basin of the Khuzestan province of Iran. Therefore, for flood susceptibility modeling, three scenarios for determining no-occurrence points were employed: random sampling (Scenario 1), Voronoi diagram (Scenario 2), and the combination of Voronoi diagram with entropy-based method (Scenario 3). Additionally, three data split ratios (60:40, 70:30, and 80:20) were utilized for partitioning training and validation datasets.

Scenario 1 demonstrates varying area under the curve (AUC) of the receiver operating characteristic (ROC) curve values across different data split ratios, with the 60:40 ratio showing moderate accuracy (AUC = 0.720). Scenario 2 exhibits improved performance with higher AUC values (0.847 to 0.908) and balanced sensitivity–specificity trade-offs. Scenario 3 demonstrates varying performance across different data split ratios, achieving the highest modeling accuracy with an AUC of 0.888 in the 60:40 split. Overall, Scenarios 2 and 3 outperform Scenario 1, showcasing significant accuracy improvements ranging from 23.33 % to 30.1 % across different data split ratios. Using Voronoi diagrams and entropy-based methods notably enhances accuracy in determining no-occurrence points compared to random selection, emphasizing the importance of method selection in flood susceptibility modeling.