Flash flood detection from CYGNSS data using the RUSBoost algorithm

Abstract

Flash floods can cause massive damages because of their rapid evolution. To reduce or prevent harm caused by a flash flood, it is vital to have information about its formation and spread. Hence, providing real-time surveillance flood is essential. Considering Hurricane Harvey and Hurricane Irma as two case studies, six different data preparation approaches (DPAs) for flood detection based on the Cyclone Global Navigation Satellite System (CYGNSS) data and the Random Under-Sampling Boosted (RUSBoost) classification algorithm are investigated in this article. Taking flood and land as two classes, flash flood detection is tackled as a binary classification problem. Eleven observables are extracted from the delay-Doppler maps (DDMs) for feature selection. These observables, alongside two features from an ancillary data, are considered in feature selection. All the combinations of these observables with and without ancillary data are fed into the classifier with 5-fold cross-validation one by one. Based on the test results, five observables with the ancillary data are selected as a suitable feature vector for flood detection here. Using the selected feature vector, six different DPAs are investigated and compared to find the best one for flash flood detection. Then, the performance of the proposed method is compared with that of a support vector machine (SVM) based classifier. For Hurricane Harvey and Hurricane Irma, the selected method is able to detect 89:00% and 85:00% of flooded points, respectively, with a resolution of 500m - 500 m, and the detection accuracy for non-flooded land points is 97:20% and 71:00%, respectively.