Flash Flood Susceptibility Modeling and Magnitude Index Using Machine Learning and Geohydrological Models: A Modified Hybrid Approach

Abstract

In an arid region, flash floods (FF), as a response to climate changes, are the most hazardous causing massive destruction and losses to farms, human lives and infrastructure. A first step towards securing lives and infrastructure is the susceptibility mapping and predicting of occurrence sites of FF. Several studies have been applied using an ensemble machine learning model (EMLM) but measuring FF magnitude using a hybrid approach that integrates machine learning (MCL) and geohydrological models have not been widely applied. This study aims to modify a hybrid approach by testing three machine learning models. These are boosted regression tree (BRT), classification and regression trees (CART), and naive Bayes tree (NBT) for FF susceptibility mapping at the northern part of the United Arab Emirates (NUAE). This is followed by applying a group of accuracy metrics (precision, recall and F1 score) and the receiving operating characteristics (ROC) curve. The result demonstrated that the BRT has the highest performance for FF susceptibility mapping followed by the CART and NBT. After that, the produced FF map using the BRT was then modified by dividing it into seven basins, and a set of new FF conditioning parameters namely alluvial plain width, basin gradient and mean slope for each basin was calculated for measuring FF magnitude. The results showed that the mountainous and narrower basins (e.g., RAK, Masafi, Fujairah, and Rol Dadnah) have the highest probability occurrence of FF and FF magnitude, while the wider alluvial plains (e.g., Al Dhaid) have the lowest probability occurrence of FF and FF magnitude. The proposed approach is an effective approach to improve the susceptibility mapping of FF, landslides, land subsidence, and groundwater potentiality obtained using ensemble machine learning, which is used widely in the literature.

Inundation Assessment of the 2019 Typhoon Hagibis in Japan Using Multi-Temporal Sentinel-1 Intensity Images

“…Compared to existing research for the same event [37], the recall and precision of the flooded area in this study were slightly lower. The average accuracies of our results after masking the high-water channels were 0.82 for the recall, 0.76 for the precision, and 0.79 for the F1-measure, which are the same levels found in existing research.…”

“…We also used the increase of the backscatter to extract inundated buildings in the 2015 Kanto and Tohoku torrential rain event in Japan [13]. The decrease of coherence is another effective way to identify inundated buildings and has been used in several studies [25,[29][30][31][32]37]. However, the coherence change needs three or more complex temporal SAR images under the same acquisition conditions.…”

“…Besides traditional methods, machine learning and statistical models have been used to investigate the probability of being flooded [35][36][37][38]. Different topographical, hydrological, and geological condition factors were applied to the existing machine learning models; then, the best conditioning factors and the models with the best results were adopted to generate flood susceptibility maps [35,36].…”

“…Different topographical, hydrological, and geological condition factors were applied to the existing machine learning models; then, the best conditioning factors and the models with the best results were adopted to generate flood susceptibility maps [35,36]. Moya et al [37] introduced a supervised machine learning classifier to learn from a past event to identify flooded areas during Typhoon Habibis. Using the pre-and co-event coherences, the overall accuracy of the predication was 0.77, and the kappa coefficient was 0.54.…”

Segmentation Scale Effect Analysis in the Object-Oriented Method of High-Spatial-Resolution Image Classification

“…A large tree is established by repeating the process, and the pruning algorithm is used to optimize the generated results. The CART algorithm is suitable for a wide range of image mapping and predicting problems [ 54 , 55 ]. As a widely used machine learning algorithm, the CART is sensitive to the training samples, and in order to obtain ideal classification results, the training samples should be representative of broad sections of the land objects.…”