Climate change is causing a range of environmental impacts, including increased flood frequency and intensity, posing significant risks to human populations and transportation infrastructure. Assessing flood risk under climate change is critical, but it is challenging due to uncertainties associated with climate projections and the need to consider the interactions between different factors that influence flood risk. Geographic Information Systems (GISs) are powerful tools that can be used to assess flood risk under climate change by gathering and integrating a range of data types and sources to create detailed maps of flood-prone areas. The primary goal of this research is to create a comprehensive GIS-based flood risk map that includes various climate change scenarios derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6) models. This goal will leverage the Analytic Hierarchy Process (AHP) methodology to better understand the impacts of these climate change scenarios on the transportation network. Furthermore, this study aims to evaluate the existing flood risk map and assess the potential impacts of prospective climate scenarios on the levels of flood risk. The results showed that the northern and coastal regions of the United Arab Emirates (UAE) are at higher risk of flooding, with the majority of the population living in these areas. The projections for future flood risk levels indicate that under the SSP245 scenario, flood risk levels will generally be low, but some areas in the northern and eastern regions of the UAE may still face high to very high flood risk levels due to extensive urbanization and low-lying coastal regions. Under the SSP585 scenario, flood risk levels are projected to be significantly higher, with a widespread distribution of very high and high flood risk levels across the study area, leading to severe damage to infrastructure, property, and human lives. The recent publication of the CMIP6 models marks a significant advancement, and according to the authors’ knowledge, there have been no studies that have yet explored the application of CMIP6 scenarios. Consequently, the insights provided by this study are poised to be exceptionally beneficial to researchers globally, underscoring the urgent necessity for holistic sustainable flood risk management approaches for geography, planning, and development areas. These approaches should integrate both sustainable transportation infrastructure development and risk mitigation strategies to effectively address the anticipated impacts of flooding events within the study region.

The 7.8 M_w Turkey-Syria earthquake of 2023 caused massive destruction in several cities near the earthquake epicenter. However, there is a potential for significant land subsidence to occur across a broader region. Land subsidence, which can lead to significant infrastructure damage and ground deformation, necessitates detailed investigation. This research uses an advanced machine-learning technique to analyze the spatial distribution of earthquake-induced land subsidence and the extent of surface deformation. Sentinel-1 Synthetic Aperture Radar (SAR) data were processed to detect surface deformation near the epicenter and quantify the affected region's vertical displacement. An extreme learning machine model was developed using nine parameters, including slope, curvature, sediment thickness, soil thickness on slopes, peak ground acceleration, hydrologic soil, Vs30, land cover, and landslide probability. The model accurately predicted land subsidence susceptibility (accuracy of 85%) established correlations with ground deformation and observed vertical displacement. The results demonstrate that the deformation phase value ranges between 2.66 to −2.63, and the vertical displacement analysis suggests that a large portion of areas subsided downward up to 75 cm. Effectiveness of extreme learning machine in rapid land subsidence assessment, providing critical insights for disaster response and urban planning in seismically active areas. This study offers a useful solution for post-earthquake land subsidence analysis and lays the groundwork for integrating artificial intelligence with land subsidence research.