China has a vast land area, with mountains accounting for 1/3 of the country's land area. Flooding in these areas can cause significant damage to human life and property. Therefore, rainstorms and flood hazards in Huangshan City should be accurately assessed and effectively managed to improve urban resilience, promote green and low-carbon development, and ensure socio-economic stability. Through the Random Forest (RF) algorithm and the Soil Conservation Service (SCS) model, this study aimed to assess and demarcate rainstorm and flood hazard risks in Huangshan City. Specifically, Driving forces-Pressure-State-Impact-Response (DPSIR)'s framework was applied to examine the main influencing factors. Subsequently, the RF algorithm was employed to select 11 major indicators and establish a comprehensive risk assessment model integrating four factors: hazard, exposure, vulnerability, and adaptive capacity. Additionally, a flood hazard risk zoning map of Huangshan City was generated by combining the SCS model with a Geographic Information System (GIS)-based spatial analysis. The assessment results reveal significant spatial heterogeneity in rainstorm and flood risks, with higher risks concentrated in low-lying areas and urban fringes. In addition, precipitation during the flood season and economic losses were identified as key contributors to flood risk. Furthermore, flood risks in certain areas have intensified with ongoing urbanization. The evaluation model was validated by the 7 July 2020 flood event, suggesting that Huangshan District, Huizhou District, and northern Shexian County suffered the most severe economic losses. This confirms the reliability of the model. Finally, targeted flood disaster prevention and mitigation strategies were proposed for Huangshan City, particularly in the context of carbon neutrality and green urbanization, providing decision-making support for disaster prevention and emergency management. These recommendations will contribute to enhancing the city's disaster resilience and promoting sustainable urban development.

Risk assessment is vital for humanities, especially in assessing natural and manmade hazards. Romblon, an archipelagic province in the Philippines, faces frequent typhoons and heavy rainfall, resulting in floods, with the Municipality of Santa Fe being particularly vulnerable to its severe damage. Thus, this research study intends to evaluate the flood risk of Santa Fe spatially using the fuzzy analytical hierarchy process (FAHP), taking into account data sourced from various government agencies and online databases. GIS was utilized to map flood-prone areas in the municipality. Hazard assessment factors included average annual rainfall, elevation, slope, soil type, and flood height. Distance to river, distance to road, types of building structure, mean age, gender ratio, and average annual income were considered parameters of vulnerability assessment. Exposure assessment considered land use, distance to evacuation facility, household number, and population density. Weights for each parameter were determined through pairwise comparison performed by experts. These weights were then incorporated into risk assessment estimation. The developed risk map identifies five high-risk barangays (small local government units). The study's findings will enable local government units to establish flood mitigation programs, implement targeted mitigation measures, and formulate strategic response plans to lower risk and safeguard the residents of Santa Fe effectively.

A hazard is a natural occurrence that might harm humans, animals or the environment. It may cause loss of life, illness or other health consequences, property damage, social and economic crisis or environmental degradation. Various regions around the world are vulnerable to one or more types of disasters. Flooding is one of the worst environmental catastrophes that impacts both civilisation and the environment globally. Various datasets and methods, such as meteorological data, satellite images and GIS, were used to create the hazard assessment map. For a particular region, flood hazards can be developed by integrating an assessment map for several parameter categories. The aim of the study was to evaluate the hazard of flooding and map the areas that will be flooded in Gujarat. This study develops and tests flood-hazard maps to visualise the spatial variation of hazards in Gujarat, India. The parameters for flood-hazard assessment are mainly considered as elevation, slope, aspect, curvature, lithology, soil, land use/cover, drainage density and distance from the river, and rainfall to create a map in the context of a GIS. The acquired data was evaluated using ArcGIS and fuzzy-logic techniques to build a flood hazard map. Five categories have been assigned to the computed flood hazard map: very low, low, moderate, high, and very high. Engineers, planners and local governments may find this study useful in the future when it comes to land use planning and the control of hazards. Flood hazard potential mapping is necessary to manage and mitigate flooding.

Flash floods are a major threat to life and properties in arid regions. In recent decades, Egypt has experienced severe flash floods that have caused significant damage across the country, including the Red Sea region. The aim of this study is to map the flood hazards in flood-prone areas along the Red Sea region using a Geographic Information System (GIS)-based morphometric analysis approach. To evaluate the flood hazard degree, the adopted methodology considers various morphometric parameters such as basin area, slope, sinuosity index, shape factor, drainage intensity, circularity ratio, and curve number. GIS techniques were employed to delineate the watershed and the drainage network. The delineated watershed was used together with the digitized maps of soil and land use types to estimate the curve number and the morphometric parameters for each subbasin. The flood hazard degrees are calculated based on the considered morphometric parameters and distinguished based on a five-degree scale ranging from very low to very high. Results indicate that 47% of the study area has a very high flood hazard degree. Furthermore, morphometric analysis results align with the runoff results simulated by a hydrological model, where, for example, basins with a high to very high hazard degree exhibited high runoff. This suggests the influence of physical characteristics on the hydrological behavior of the watershed and further validates the morphometric analysis presented in this work. The results presented here can help policy planners and decision-makers develop appropriate measures to mitigate flash floods and achieve sustainable development in arid regions.