Flood Detection and Susceptibility Mapping Using Sentinel-1 Time Series, Alternating Decision Trees, and Bag-ADTree Models

Abstract

Flooding is one of the most damaging natural hazards globally. During the past three years, floods have claimed hundreds of lives and millions of dollars of damage in Iran. In this study, we detected flood locations and mapped areas susceptible to floods using time series satellite data analysis as well as a new model of bagging ensemble-based alternating decision trees, namely, bag-ADTree. We used Sentinel-1 data for flood detection and time series analysis. We employed twelve conditioning parameters of elevation, normalized difference’s vegetation index, slope, topographic wetness index, aspect, curvature, stream power index, lithology, drainage density, proximities to river, soil type, and rainfall for mapping areas susceptible to floods. ADTree and bag-ADTree models were used for flood susceptibility mapping. We used software of Sentinel application platform, Waikato Environment for Knowledge Analysis, ArcGIS, and Statistical Package for the Social Sciences for preprocessing, processing, and postprocessing of the data. We extracted 199 locations as flooded areas, which were tested using a global positioning system to ensure that flooded areas were detected correctly. Root mean square error, accuracy, and the area under the ROC curve were used to validate the models. Findings showed that root mean square error was 0.31 and 0.3 for ADTree and bag-ADTree techniques, respectively. More findings illustrated that accuracy was obtained as 86.61 for bag-ADTree model, while it was 85.44 for ADTree method. Based on AUC, success and prediction rates were 0.736 and 0.786 for bag-ADTree algorithm, in order, while these proportions were 0.714 and 0.784 for ADTree. This study can be a good source of information for crisis management in the study area.