Publication

Abstract : Floods are among nature’s most destructive disasters because they create extremely extensive damage to structures, the environment, and people. Therefore, it is important to determine the causes of floods as well as areas that are vulnerable to flooding, which can be done by performing a flood susceptibility model. This research identified flood-prone locations in the Periyar River Basin using historical flood records from 2000 to 2020 and some of the conditioning features. The ten variables considered in the present study include elevation, slope, aspect, flow direction, drainage density, rainfall, Normalized Difference Water Index (NDWI), Stream Power Index (SPI), Sediment Transport Index (STI), and Topographic position Index (TPI). In order to create a flood susceptibility map and examine the correlation between flood incidence, the logistic regression (LR), support vector machine, naive Bayes, random forest, AdaBoost, gradient boost, and extreme gradient boost models were developed and validated. The model accuracies were measured using the receiver operating characteristic curve (ROC) and the area under the curve (AUC). In addition to this, some other indices like precision, recall, sensitivity, specificity, F1 score, and overall accuracy matrices are used for model evaluation. The results demonstrated that every model can identify flood-prone locations with reasonable accuracy. However, compared to other models, the random forest model showed a better performance and prediction rate (AUC = 94). Furthermore, all models indicated that low-lying places near water bodies and in the western region of the study area had the largest probability of flooding. According to the study, machine learning techniques are a useful tool for mapping and predicting flood-prone areas and for creating flood mitigation strategies and plans.