XGBoost-based method for flash flood risk assessment

Abstract

Abstract Flash flood risk assessment, a widely applied technology in preventing catastrophic flash flood disasters, has become the current research hotspot. However, most existing machine learning methods for assessing flash flood risk rely on a single classifier, which is suitable for processing small sets of sample data, but the resulting prediction accuracy and generalization ability are insufficient. Meanwhile, machine learning methods that integrate multiple classifiers are thus far unknown. Extreme Gradient Boosting (XGBoost) is an excellent algorithm for ensemble learning methods which has achieved remarkable results in many fields. It not only optimizes the algorithm but also automatically applies the CPU's multi-threading to perform parallel calculations, thus greatly improving the model training speed and prediction accuracy. Therefore, this article introduces the XGBoost model for the assessment of flash flood risk, and then combines the two input strategies and the Least squares support vector machine (LSSVM) model to verify its optimal effect, thus proposing the XGBoost-based method for flash flood risk assessment. Subsequently, an attribution analysis was implemented to assess the possible errors of this approach; and finally, a county-level flash flood risk map for Yunnan Province, China, was generated based on the proposed method. The results demonstrate that: (1) XGBoost performs well, with an accuracy of 0.84 in the testing period, and its five indices (precision, recall, accuracy, kappa, and F-score) are all higher than those of LSSVM. (2) The XGBoost-based approach provided the reliable flash flood risk maps, which were validated by another flash flood inventory, although some errors may be attributed to critical environmental factors and statistical disaster location accuracy. (3) The high-risk counties (including high-risk and highest-risk) accounted for 40.3%, with the highest-risk counties mainly concentrated in southeastern Yunnan. This article further addresses the limitations of XGBoost (e.g., as a time-consuming greedy algorithm, the non-necessity of multi-threaded optimization). All of the above results indicate that the XGBoost-based method is an effective method for obtaining high-quality county-level flash flood risk maps, which contributes to the theoretical basis for ongoing county-level flash flood prevention in China.