Improving the Accuracy of Flood Susceptibility Prediction by Combining Machine Learning Models and the Expanded Flood Inventory Data

Abstract

Sufficient historical flood inventory data (FID) are crucial for accurately predicting flood susceptibility using supervised machine learning models. However, historical FID are insufficient in many regions. Remote sensing provides a promising opportunity to expand the FID. However, whether the FID expanded by remote sensing can improve the accuracy of flood susceptibility modeling needs further study. In this study, a framework was proposed for improving the accuracy of flood susceptibility prediction (FSP) by combining machine learning models and the expanded FID using Sentinel-1A radar images. Five widely used machine learning models were employed to verify the accuracy of the proposed method by taking Wuhan City as a case study, including the random forest (RF), gradient boosting decision tree (GBDT), k-nearest neighbor (KNN), support vector machine (SVM), and artificial neural network (ANN) models. Sentinel-1A images from time points before, during, and after flood events were used to expand the FID for training the machine learning models. The results showed that the performance of the machine learning models for predicting flood susceptibility was improved greatly by considering the expanded FID, being improved by approximately 1.14–19.74% based on the area under the receiver operating characteristic curve (AUC). Among the used machine learning models, taking into account all the statistical indicators, the ANN showed the best performance, while the SVM showed the best generalization performance in Wuhan City. According to the results of the ANN model, approximately 19% of the area in Wuhan City, mainly distributed near rivers and lakes, is at a high flood susceptibility level. This study provides an essential reference for flood susceptibility analyses in regions with limited flood sampling data.