Threshold-based inventory for flood susceptibility assessment of the world’s largest river island using multi-temporal SAR data and ensemble machine learning algorithms

Majuli is the world’s largest inhabited river island and is highly prone to flood hazards, resulting in significant damage to houses and agriculturally based livelihoods. Considering its cultural heritage and unique landscape, it is necessary to prepare a flood susceptibility map (FSM) to reduce the annual damage. Therefore, the primary aim of this research is to prepare and improve the precision of FSM using microwave satellite images and six robust ensemble machine learning models. In the three main stages of FSM, each stage contributes to achieving optimal accuracy. In the first stage, a threshold-based flood inventory map has been prepared from six years of multi-temporal SAR images. In the second stage, preliminarily seventeen flood conditioning variables such as elevation, slope, profile curvature, terrain ruggedness index, topographic wetness index, distance from streams, rainfall, land cover land use, normalized difference vegetation index, distance from road, geomorphology and lithology have been prepared, but after utilizing the Boruta algorithm and multicollinearity analysis, twelve key flood-influencing variables have been selected for flood modelling. In the final stage, six robust ensemble machine learning models namely random forest, rotation forest, stochastic gradient boosting, boosted regression tree, deep boost and logit boost have been applied and subsequently compared to determine the best model. The performance of the models is evaluated with various statistical measurements, including the area under curve (AUC), sensitivity, specificity, kappa index and overall accuracy values. The results revealed that the random forest model outperformed the other models in terms of model fitness (AUC = 1) and predictive capability (0.99). Additionally, the very highly vulnerable pixels of the FSM are validated with the twenty flood locations from the field surveys, showing that the accuracy of the FSM is 100%. The FSM indicates that around 50% of the study region has a very high and high susceptibility to future flood occurrences.