Large-scale dynamic flood monitoring in an arid-zone floodplain using SAR data and hybrid machine-learning models

Although the growing number of synthetic aperture radar (SAR) satellites has increased their application in flood-extent mapping, predictive models for the analysis of flood dynamics that are independent of sensor characteristics must be developed to fully extract information from SAR images for flood mitigation. This study aimed to develop hybrid machine-learning models for flood mapping in the Ahvaz region, Iran, based on SAR data. Each hybrid model consists of a support vector machine (SVM) algorithm coupled with one of the following metaheuristic optimization procedures: grey wolf optimization (GWO), differential evolution, and the imperialist competitive algorithm. Sentinel-1 acquired SAR images before and during flooding between 20 March and 26 May of 2019. The goodness-of-fit level and predictive capability of each model were scrutinized using overall accuracy, producer accuracy, and user accuracy. The SVM-GWO approach yielded the highest accuracy with overall accuracies of 96.07% and 93.39% in the training and validation steps, respectively. Furthermore, this hybrid model provided the most accurate classification of water-inundation class based on producer accuracy (96.67%) and user accuracy (95.05%). The results highlight that wetland is the last land-use/land-cover type to return to normal conditions due to the many previously dry oxbow lakes that could trap water for a long time. Furthermore, the nine most suitable sites for flood-protection structures (e.g., embankments and levees) were identified based on floodwater distribution analysis. This work describes a robust, data-parsimonious approach that will benefit flood mitigation studies seeking to identify the most suitable locations for embankments based on spatio-temporal flood dynamics.