ASSESSMENT OF INUNDATION SUSCEPTIBILITY IN THE CONTEXT OF CLIMATE CHANGE, BASED ON MACHINE LEARNING AND REMOTE SENSING: CASE STUDY IN VINH PHUC PROVINCE OF VIETNAM

Accurate prediction of streamflow plays an important role in water resource management and the continuous assessment of inundation susceptibility in the context of climate change plays a key role in facilitating the construction of appropriate strategies for sustainable development. So far, few studies into inundation susceptibility have explicitly incorporated the effects of climate change into their methodologies. This study aimed to assess inundation susceptibility for Vinh Phuc province in Vietnam, from 2000 to 2020, using machine learning and remote sensing. The algorithms used were support vector machine, catboost, and extratrees. A geo-spatial database of 206 inundation points and 11 conditioning factors (namely elevation, slope, curvature, aspect, distance to river, distance to road, NDVI, NDBI, rainfall, soil type, and TWI) from 2000 to 2020 was developed to be used as the input data. RMSE, MAE, AUC, and R2 were used to assess the fit of the models. The results showed that all the proposed models were a good fit, with AUC values of 0.95 and over. In general, the total area marked as very low risk or low risk has increased, with the high risk and very high-risk areas having decreased over the period studied. This change was mainly concentrated in the city of Vinh Yen where there has been strong urban growth. The models proposed in this study are a promising toolkit to assess inundation susceptibility continuously and can support decision makers involved in sustainable development. Our results highlight the benefits and consequences of planned and unplanned development. Properly planned can reduce the flood risk, while unplanned development can increase the risk. Therefore, by applying the theoretical framework in this study, decision makers or planners can build the most appropriate strategies for flood control in the context of climate change. Our approach in this study represents a theoretical framework for future research not only on inundation management but also natural hazard management, in regions around the world.