Research on urban flood risk has highlighted the need for more comprehensive flood risk assessments in low-income and vulnerable communities. This study aims to examine the causes, impacts and existing flood risk management measures in the Somali region of Ethiopia. The study used a mixed research methodology, including a cross-sectional survey, to collect original qualitative and quantitative data.. In addition to flood risk and vulnerability assessment, the study evaluated urban flood risk management measures through soil protection service curve number, production distribution network and supply chain risk management methods.The results suggest that flooding in Dolo-Ado is increasing due to heavy rainfall and flooding, as well as inadequate flood control measures and geographical location. Soil Conservation Service Curve Number analysis shows that the arid landscape of Dolo-ado is predominantly shrub and barren with significant differences in land cover types. The low infiltration capacity, high runoff potential and frequent heavy rainfall are the main factors contributing to the area's high soil vulnerability to flash floodsConsequently, qualitative results also confirm that this has resulted in extensive infrastructure damage, displacement, loss of livelihoods, ecosystem disruption and disruption to community life, as well as water and health problems. In addition, flood risks are more severe for vulnerable urban communities, impacting services, the economy and the environment. Therefore, inadequate preventive measures for effective supply chain management are urgent and crucial for resilience. This study implies that urban planning and policies should be changed and prioritize the integration of production distribution networks and flood risk management in the supply chain to effectively mitigate floods. Climate change-responsive and integrated urban planning, improved drainage systems, early warning, emergency planning and community engagement are critical for flood preparedness, adaptation and resilience and require further research and modeling techniques.

Flood hazard has resulted in the loss of thousands of lives and large-scale damage to properties. This study has explored, analyzed, and categorized the flood hazard and risk levels of Arba Minch City in South Ethiopia by integrating geospatial and Analytical Hierarchy Process techniques. Data were acquired from DEM with 12.5 m resolution, Landsat 8 OLI, ortho-rectified, and surveyed data from the Municipality. Slope, Elevation, Rainfall, Aspect, Curvature, Topographic Wetness Index, Topographic Roughness Index, Drainage Density, Distance from River, Soil Types, Land Use Land Cover, and Population Density parameters were used. Standard classification criteria were set based on literature and experts' judgment. Data were rasterized, resampled, and reclassified into five classes through the natural break method and readjustment. The flood hazard map was produced using the weighted overlay technique with hazard levels of low (7.39%), moderate (56.13%), and high (36.48%). Whereas, very low and very high remained nil. The flood risk levels were produced ascendingly as 2.4%, 17.3%, 17%, 44%, and 19.4%, respectively. The validity of the model was confirmed by the ROC-AUC Value of 0.923 being fitted with flood damage sites of Shara, Limat, Airport, Agriculture Research Center, Konso Sefer, Ashewamado, Gurba, and Arba Minch University campuses. Slope, elevation, rainfall, aspect and curvature were the top priority flood hazard parameters. The hazard map, population density, and land use land cover inputs have significant weights for flood risks. Thus, the study findings urge that the stakeholders should take integrated and consistent flood risk reduction and management measures.

Floods pose a significant risk for Bangladesh due to the country's geographical and climatic conditions. Traditional methods of predicting flood risk often fail to do justice to the complex dynamics of flood vulnerability in this region. This report provides a comprehensive overview of the use of advanced machine learning (ML) algorithms for flood risk prediction in Bangladesh. It addresses four primary areas of research: (a) factors influencing floods considered in ML-based studies, (b) performance metrics of ML models, and (c) research gaps and future challenges in ML-based flood risk prediction. This review identified 42 unique factors that influence flooding, with precipitation, distance from the river, elevation, orientation, land use and land cover, and soil type emerging as the most important. ML models showed high predictive performance with an accuracy of 82% to 95%, depending on the algorithm and dataset used. However, there are still problems with data quality and regional variability that affect the reliability of the models. To improve flood forecasting, integrating real-time data, combining ML with physical models and promoting stakeholder engagement are crucial. Future research should focus on improving data quality, combining ML and physical models, and integrating future climate projections to refine flood hazard mapping. By considering these aspects, this study contributes to improving flood risk assessment and sustainable flood management strategies in Bangladesh, which could reduce socio-economic losses and environmental damage -in high-risk areas by 20-30.

Throughout human history, floods have resulted in significant environmental and economic damages globally, and these impacts are expected to increase with climate change. Despite the occurrence of significant floods in Iran in recent decades, the precise impact of intense rainfall and antecedent soil moisture (ASM) on flood occurrence and how this relationship differs at different locations remains an area that requires further investigation. In this study, we analyzed annual maximum floods at 963 catchments across Iran from 1972 to 2019 (47 years). Moreover, we evaluated the relationship between catchment area and the main factor responsible for flood generation. Our analysis reveals a shift in the dominant factor driving flood generation from rainfall to ASM as the catchment area expands. The correlation between the topographic wetness index and the ratio of the relative significance of ASM (S) and daily rainfall (R) (i.e., Soil moisture-to-Rainfall Ratio; SRR = SR.), is positive, while the correlation between the magnitude of annual floods and SRR ratio is negative. A quantitative estimation of potential flood probability in ungauged catchments in Iran is facilitated by establishing a framework based on the relationship between easily measurable catchment attributes and the primary phenomenon of flood generation.

The 2022 flood events in Quetta, Pakistan, caused severe damage to the economy, properties, and lives. Therefore, flood risk mapping to identify flood-prone areas is essential for planners and decision-makers to take critical protective measures to control the effects of flooding. This study focuses on mapping flood-prone regions in the Quetta district of Pakistan using an analytical hierarchy process (AHP) and a geographic information system (GIS). The factors influencing flood used in the present study were topographic witness index (TWI), elevation, slope, land use, land cover, precipitation, stream distance, drainage density, and soil type. Weights and ranks were allocated separately to all factors through AHP and were interpreted in a GIS environment. The produced flood hazard model of the study area depicted four zones. These zones ranged from low (19.49%), moderate (43.34%), high (28.30%), to very high (8.87%). The model was further validated through previous flood events in the study area. Around 90% of flood hazard events in the past took place mainly in the produced model's very high and high zones, which is why the current model is reliable. Finally, integrating geospatial approaches with AHP in flood hazard mapping is a quick, reliable, and affordable method that may be utilized in the area.

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.

Compound floods induced by co-occurring multiple drivers may exacerbate the flood impacts and lead to larger flood damage. Exploring future changes in compound flood risk is imperative for flood management and disaster reduction. This study attempts to investigate future changes in compound flood risk across the Yangtze River Basin during 2030 similar to 2100. Future river flow was projected using an improved hydrological model and pairwise series of extremes of rainfall and river flow were extracted from both observed and projected series. Subsequently, stochastic pairs of rainfall and river flow characterizing compound floods were proportionally sampled from their bivariate joint distributions. The damage from each compound flood was obtained from the flood damage function constructed by Random Forests (RF). Further, the expected annual damage (EAD) was calculated to investigate future changes in compound flood risk. Results show that: (1) Future annual maximums of rainfall and river flow are expected to increase by 14.51 % similar to 66.13 % and 1.72 % similar to 55.73 % in the mainstream and northern tributaries, while future annual peak discharge in the southern tributaries (except for the Dongting Lake Basin) is expected to decrease by 4.18 % similar to 12.30 %. A similar spatial distribution of future changes is also found in the bivariate joint distribution of rainfall and river flow. (2) The high coefficient of determination (R-2) of 0.84 indicates the satisfactory simulation and projection capacity of the constructed flood damage function. The positive stepped relationship between flood damage and rainfall or river flow reflects the superposition of multiple flood damage processes. (3) The Han River Basin, the Jialing River Basin, and the two-lakes (the Dongting and Poyang Lakes) area face great threat from compound floods in both historical and future periods. Future compound flood risk is expected to increase by 13.43 % similar to 46.04 % in these regions except for the Poyang Lake Basin, while future risk is expected to increase by 2.03 % similar to 46.04 % in the whole basin. The findings help improve the understanding of future flood risk variations in the Yangtze River Basin and provide essential information for damage reduction.

Floods are a widespread natural disaster with substantial economic implications and far-reaching consequences. In Northern Pakistan, the Hunza-Nagar valley faces vulnerability to floods, posing significant challenges to its sustainable development. This study aimed to evaluate flood risk in the region by employing a GIS-based Multi-Criteria Decision Analysis (MCDA) approach and big climate data records. By using a comprehensive flood risk assessment model, a flood hazard map was developed by considering nine influential factors: rainfall, regional temperature variation, distance to the river, elevation, slope, Normalized difference vegetation index (NDVI), Topographic wetness index (TWI), land use/land cover (LULC), curvature, and soil type. The analytical hierarchy process (AHP) analysis assigned weights to each factor and integrated with geospatial data using a GIS to generate flood risk maps, classifying hazard levels into five categories. The study assigned higher importance to rainfall, distance to the river, elevation, and slope compared to NDVI, TWI, LULC, curvature, and soil type. The weighted overlay flood risk map obtained from the reclassified maps of nine influencing factors identified 6% of the total area as very high, 36% as high, 41% as moderate, 16% as low, and 1% as very low flood risk. The accuracy of the flood risk model was demonstrated through the Receiver Operating Characteristics-Area Under the Curve (ROC-AUC) analysis, yielding a commendable prediction accuracy of 0.773. This MCDA approach offers an efficient and direct means of flood risk modeling, utilizing fundamental GIS data. The model serves as a valuable tool for decision-makers, enhancing flood risk awareness and providing vital insights for disaster management authorities in the Hunza-Nagar Valley. As future developments unfold, this study remains an indispensable resource for disaster preparedness and management in the Hunza-Nagar Valley region.