Floods have received global significance in contemporary times due to their destructive behavior, which may wreak tremendous ruin on infrastructure and civilization. The present research employed an integration of the Geographic information system (GIS) and Analytical Hierarchy Process (AHP) method for identifying the flood susceptibility zonation (FSZ), flood vulnerability zonation (FVZ), and flood risk zonation (FRZ) of the humid subtropical Uttar Dinajpur district in India. The study combined a large number of thematic layers (N = 12 for FSZ and N = 9 for FVZ) to achieve reliable accuracy and included the multicollinearity analysis of these variables to overcome the issues related to highly correlated variables. According to the findings, 27.04, 15.62, and 4.59% of the area were classified as medium, high, and very high FRZ, respectively. The ROC-AUC, MAE, MSE, and RMSE of the model exhibited a good prediction accuracy of 0.73, 0.15, 0.16, and 0.21, respectively. The performance of the AHP model has been evaluated using sensitivity analyses. It also highly recommends that persistent improvement in this subject, such as sensitivity studies on modifying criteria thresholds, changing the relative significance of criteria, and changing the desired matrix, will permit GIS and MCDA to be progressively adapted to real hazard-management issues. Graphical Abstract

Floods are the most common and expensive natural calamity, affecting every country. Flooding in the Shebelle River Basin (SRB) in southern Somalia has posed a significant challenge to sustainable development. The main goal of this study was to analyze flood hazard, vulnerability and risk in the part of SRB using GIS-based Multi-Criteria Decision Analysis (MCDA). The flood hazard map was constructed using seven important causative factors: elevation, slope, drainage density, distance to river, rainfall, soil and geology. The results demonstrate that very low, low, moderate, high, and very high flood hazard zones correspond to 10.92%, 24.97%, 29.13%, 21.93% and 13.04% of the area of SRB, respectively. The flood vulnerability map was created using five spatial layers: land use/land cover, population density, distance to road, Global man-made impervious surface (GMIS), and Human built-up area settlement extent (HBASE). In addition, the results of the flood susceptibility and vulnerability maps were used to create a flood risk map. The results demonstrate that for the Shebelle River Basin, 27.6%, 30.9%, 23.6%, 12.1%, and 5.7% area correspond to very low, low, moderate, high, and very high flood risk zones, respectively. The Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) of the flood hazard model exhibited a good prediction accuracy of 0.781. The majority of the basin is at risk of flooding in the very low, low, and moderate ranges; however, some tiny areas are at risk of flooding in the high and very high ranges. Flood hazard, vulnerability and risk maps should be provided and distributed the authorities responsible for flood protection so that people are aware flood risk locations.

The Himalayan region, characterized by its substantial topographical scale and elevation, exhibits vulnerability to flash floods and landslides induced by natural and anthropogenic influences. The study focuses on the Himalayan region, emphasizing the pivotal role of geographical and atmospheric parameters in flash flood occurrences. Specifically, the investigation delves into the intricate interactions between atmospheric and surface parameters to elucidate their collective contribution to flash flooding within the Nainital region of Uttarakhand in the Himalayan terrain. Pre-flood parameters, including total aerosol optical depth, cloud cover thickness, and total precipitable water vapor, were systematically analyzed, revealing a noteworthy correlation with flash flooding event transpiring on October 17th, 18th, and 19th, 2021. Which resulted in a huge loss of life and property in the study area. Contrasting the October 2021 heavy rainfall with the time series data (2000–2021), the historical pattern indicates flash flooding predominantly during June to September. The rare occurrence of October flash flooding suggests a potential shift in the area's precipitation pattern, possibly influenced by climate change. Robust statistical analyses, specifically employing non-parametric tests including the Autocorrelation function (ACF), Mann–Kendall (MK) test, Modified Mann–Kendall, and Sen's slope (q) estimator, were applied to discern extreme precipitation characteristics from 2000 to 201. The findings revealed a general non-significant increasing trend, except for July, which exhibited a non-significant decreasing trend. Moreover, the results elucidate the application of Meteosat-8 data and remote sensing applications to analyze flash flood dynamics. Furthermore, the research extensively explores the substantial roles played by pre and post-atmospheric parameters with geographic parameters in heavy rainfall events that resulted flash flooding, presenting a comprehensive discussion. The findings describe the role of real time remote sensing and satellite and underscore the need for comprehensive approaches to tackle flash flooding, including mitigation. The study also highlights the significance of monitoring weather patterns and rainfall trends to improve disaster preparedness and minimize the impact of flash floods in the Himalayan region.