Flood Vulnerability Zones Research Articles

Flood Risk Assessment and Zoning for Niamey and Lokoja Metropolises in Niger and Nigeria

With the increasing frequency of floods in recent decades, particularly in West Africa, many regions have faced unusual and recurrent flooding events. Communities in flood-prone areas experience heightened insecurity, loss of property, and, in some cases, serious injuries or fatalities. Consequently, flood risk assessment and mitigation have become essential. This comparative study between Niamey and Lokoja employs Geographic Information Systems (GIS) and the Analytical Hierarchy Process (AHP) to delineate flood susceptibility, vulnerability, and risk zones. The study utilized a comprehensive range of thematic layers, with weight percentages assigned to each parameter as follows: 29% for elevation, 24% for slope, 15% for the Topographic Wetness Index (TWI), 9% for drainage density, 9% for distance from rivers, 4% for both precipitation and the Normalized Difference Water Index (NDWI), and 2% each for the Normalized Difference Vegetation Index (NDVI) and soil type. To validate these weightings, a consistency ratio was calculated, ensuring it remained below 10%. The findings reveal that 32% of the Niamey study area is at risk of flooding, compared to approximately 15% in Lokoja. The results highlight a very high flood potential, particularly in areas near the Niger River, with this potential decreasing as elevation increases. Given the current prevalence of extreme weather events in West Africa, it is crucial to employ effective tools to mitigate their adverse impacts. This research will assist decision-makers in quantifying the spatial vulnerability of flood-prone areas and developing effective flood risk assessment and mitigation strategies in the region.

Geospatial analysis of alarmingly increasing flood vulnerability and disaster risk within the northeast himalaya region of India

Geoenvironmetally the eastern part of the Himalaya region is highly vulnerable to flood and other natural disasters as it consists of fragmented and tectonically active geology and geomorphology, very high monsoon rainfall (>360 cm) and subsequent runoff, rugged hilly terrain with high ranges of elevation and slope, dense drainage density etc. Other hand, the unplanned developmental activities keep going and scaling up this vulnerability and risk to floods and other disasters. Addressing this burning issue, a geospatial technology-based case study of the Kohima district, Nagaland state (India), is presented here. The geospatial "technology-based" analyses employed in the study are thematic GIS mapping of flood hazard, vulnerability and risk controlling factors; and performing overlay operation using the AHP model in GIS software to generate spatio-temporal map layers of flood vulnerability and disaster risk. Results reveal that the region is under a high rate of monsoon climate change (increasing temperature, rainfall, rainy days, rainfall events and flood events with an annual rate of 0.35 %, 1.12 %, 0.36 %, 2.67 % and 4 % respectively), land use degradation (increasing built-up area with 0.60 %, annual rate decreasing forest, shrubs and water bodies with 0.80 % accumulated annual rates respectively) and demographic changes (increasing urban as well and rural population density with 0.53–2.10 % and 0.55–2.14 % respectively). Accumulated impacts of climate change, land use degradation and demographic changes causing an increase in flood hazard, vulnerability and disaster risk. Flood hazard zones and vulnerability zones extending with 0.50 % (4.89 km2) and 0.84 % (8.16 km2) annual rates respectively, subsequently the flood risk zones categorized as a moderate, high and very high potential risk, have been spreading out with a yearly rate of 0.07 % (0.65 km2), 0.13 % (1.31 km2) and 0.03 % (0.33 km2) respectively. It decreases the area under low and very low-risk zones by 0.17 % (1.63 km2) and 0.07 % (0.65 km2) annual rates respectively. Following up on these annual rates, the spatial distribution of flood hazard, vulnerability and risk zones for the next decade (2031–2040) have also been projected, revealing alarming situations, if flood disaster risk reduction (F-DRR) measures were not implemented in timely. It is strongly believed that the proposed study will be very useful for district-level planners and administrators to implement sustainable development planning, for the scientific fraternity to enhance their research work in the field of flood disaster management and for individuals for their safety in terms of life and property.

Urban Flood Vulnerability Assessment in Freetown, Sierra Leone: AHP Approach

This study presents a comprehensive flood vulnerability assessment for Freetown, Sierra Leone, spanning the period from 2001 to 2022. The objective of this research was to assess the temporal and spatial changes in the flood vulnerability using Geographic Information System (GIS) tools and AHP-based Multi-Criteria Decision-Making (MCDM) analysis. This study identified the flood-vulnerable zones (FVZs) by integrating critical factors such as the rainfall, NDVI, elevation, slope, drainage density, TWI, distance to road, distance to river, and LULC. The analysis reveals that approximately 60% of the study area is classified as having medium to high vulnerability, with a significant 20% increase in the flood risk observed over the past two decades. In 2001, very-high-vulnerability zones covered about 68.84 km2 (10% of the total area), with high-vulnerability areas encompassing 137.68 km2 (20%). By 2020, very-high-vulnerability zones remained constant at 68.84 km2 (10%), while high-vulnerability areas decreased to 103.26 km2 (15%), and medium-vulnerability zones expanded from 206.51 km2 (30%) in 2001 to 240.93 km2 (35%). The AHP model-derived weights reflect the varied significance of the flood-inducing factors, with rainfall (0.27) being the most critical and elevation (0.04) being the least. A consistency ratio (CR) of 0.068 (< 0.1) confirms the reliability of these weights. The spatial–temporal analysis highlights the east and southeast regions of Freetown as consistently vulnerable over the years, while infrastructure improvements in other areas have contributed to a general decrease in very-high-vulnerability zones. This research highlights the urgent need for resilient urban planning and targeted interventions to mitigate future flood impacts, offering clear insights into the natural and human-induced drivers of the flood risk for effective hazard mitigation and sustainable urban development.

Nivar cyclone-related urban flood vulnerability in Velachery municipality, Chennai

ABSTRACT The Velachery Municipality is highly vulnerable to natural disasters such as Storms, and floods due to its geo-environmental settings. It is evident that the Nivar cyclone has severely hit and caused flooding in the Velachery region during the fourth week of November 2020. Along with the increasing urbanization, it is also expected to experience severe impacts of climate change soon. Urbanization exerts additional environmental stress and amplifies the impact of natural disasters. The study area has been divided into 3 clusters based on the geographical location. The survey has been carried out in 180 households and data was statically analyzed. Indicators such as exposure, sensitivity, and adaptive capacity were grouped and aligned with available data. Data were aggregated using a composite index and Urban Flood Vulnerability was compared to Cluster wise. The results reveal an extreme variability in terms of flood magnitude and frequency in Velachery. The vulnerability spider diagram suggests that Cluster 2 is more vulnerable in terms of less capacity to cope, and Cluster 3 is more vulnerable in terms of Exposure, Health, Economic susceptibility, physical susceptibility, less capacity to anticipate, cope and recover. Overall Cluster 3 has higher flood vulnerability compared to other clusters. The identification of flood-vulnerable zones has been done in the study and recommended flood resilience measures for Velachery Municipality, Chennai.

A novel approach to flood risk assessment: Synergizing with geospatial based MCDM-AHP model, multicollinearity, and sensitivity analysis in the Lower Brahmaputra Floodplain, Assam

Floods persist as a recurring and daunting peril in the Brahmaputra plain of Assam. Notwithstanding advancement, Bongaigaon is a highly flood-afflicted district in the lower part of this region, inflicting significant damage to both lives and property almost every consecutive year. Hence, the delineation of precise and reliable flood risk susceptibility zones within the district constitutes the foremost concern of the study. The present work considered a total of sixteen multi-collinearity free parameters integrating with the GIS- based Multi-Criteria Decision Making-Analytical Hierarchy Process (MCDM-AHP) model for identifying potential flood hazard zones (FHZ), flood vulnerability zones (FVZ), and flood risk zones (FRZ) for the region. The result revealed that over 28% of the district's total area falls under the high to very high flood risk zone. Srijangram circle covers the highest flood risk zone with 343.19 sq. km. The FHZ map of the district demonstrated reliability exceeding 90% in ROC-AUC and below 40% in MSE and RMSE. Additionally, sensitivity analyses depict the role indicators in the predictive model, placed as a virgin gap for study in the region. Moreover, a multivariate correlation statistic is used to examine the potential risk zones and temporal flood effects on different Revenue Circles (RC), showing R2 over 0.6 in each category. The robustness of this model manifests sensible findings, aiding in fortifying sustainable flood management strategies to mitigate risks at different levels. Strategies adopted here contain greater potential for current as well as future trends in similar domains of research. This study may provide invaluable insights for decision-makers, thinkers, administrators, and developers working in this region.

Integrated assessment of flood susceptibility and exposure rate in the lower Niger Basin, Onitsha, Southeastern Nigeria

BackgroundVarious methods have been utilized to investigate and mitigate flood occurrences, yet there is a paucity of literature on factors, such as soil compositions, that contribute to persistent flooding in river basins like the Lower Niger catchment, specifically at Onitsha. Furthermore, the study seeks to furnish essential geospatial data concerning flood vulnerability, flood risks, and exposure rates in the Lower Niger Catchment area, situated in Onitsha, southeastern Nigeria.Materials and methodsSoil samples were collected from 10 specific locations identified through GPS and ground-truthing techniques. Additionally, satellite imagery from the Landsat Enhanced Thematic Mapper (ETM +) was utilized, with supervised classification employed to extract feature classes. Analysis operations were conducted using IDRISI software, resulting in the creation of digital elevation models (DEMs), susceptibility maps, and flood-risk zones.ResultsAnalysis revealed that the predominant soil composition in the study area comprises sandy (84.8%), silt (8.1%), and clayey (7.1%) soils. Utilizing these soil characteristics alongside relevant aerial data, exposure rates were determined at various scales to delineate the most flood-vulnerable zones in the basin. It was found that certain areas, accommodating a population exceeding 79,426 across 2,926.2 ha, were particularly susceptible to flooding. Notably, major markets such as Bridgehead, Textile, and Biafra were identified as highly susceptible, with varying degrees of risk. The prevalence of sandy soil, which facilitates increased rainwater infiltration but is also prone to rapid saturation and runoff, likely contributes to the heightened susceptibility to flooding in these areas.ConclusionGeospatial analysis employing remote sensing data indicates the high susceptibility and exposure to flooding in the lower Niger River Basin around Onitsha. Urgent mitigation efforts are imperative, necessitating the establishment of zoned areas equipped with effective drainage systems to safeguard vulnerable populations.

Assessing Flood Vulnerability Zones and Their Driving Factors to Guide Community-Based Resilience Planning Across Ngororero District, Rwanda

Assessing Flood Vulnerability Zones and Their Driving Factors to Guide Community-Based Resilience Planning Across Ngororero District, Rwanda

Metropolitan Flood Risk Characterization Using Remote Sensing, GIS, and Fuzzy Logic (RS-GIS-Fl) Approach: Suleja, Nigeria

Climate change exacerbates extreme events like floods, droughts, heatwaves, cyclones, hurricanes, tornadoes, and wildfires, affecting human populations and environments worldwide. Despite varying impacts across regions, no area is immune to climate change consequences. To mitigate these effects, this study employs Geographic Information System (GIS), Remote Sensing, and Fuzzy Logic System to map flood vulnerability zones in Suleja Local Government, Niger State, North Central Nigeria. The research integrates satellite and GIS datasets to prepare Suleja's Flood Zonation Mapping. Rainfall data from Nigeria Meteorological Agency (NiMET), Climate Hazards Group InfraRed Precipitation with Station Data (CHRPS), Global Satellite Mapping of Precipitation (GSMaP), and parameters like Slope, Elevation, Nearness to Water Bodies, Land Use Land Cover, and Drainage Density are utilized to identify flood vulnerability. The flood vulnerability map categorizes the area into five zones: Very Low Risk (40.9%), Low Risk (18.9%), Medium Risk (27.6%), High Risk (7.1%), and Very High Risk (5.5%). Considering the stochastic nature of flood characteristics, this study emphasizes the importance of integrating hydro-climatic and physical catchment parameters, leveraging bias-adjusted satellite data to minimize uncertainty. Effective flood management requires quantifying risk, susceptibility, and hazard components to establish a robust control framework. This research contributes to the development of data-driven flood mitigation strategies in vulnerable regions like Suleja. By harnessing GIS, Remote Sensing, and Fuzzy Logic System, policymakers and stakeholders can make informed decisions to protect communities and infrastructure from flood risks.

Flood Vulnerability Assessment Across Alaknanda River Basin using GIS-based Combined Analysis of Geomorphometric Approach and MCDM-AHP

Abstract Floods are one of the most devastating natural disasters, causing land destruction, economic loss, and massive disruptions to humans. The present study was conducted in the Alaknanda river basin with medium to high drainage density and stream frequency. The flood vulnerable zones (FVZ) are identified using geomorphometric analysis, and flood vulnerability zones are identified through analytical hierarchy processes (AHPs). Three factors, such as linear, relief, and aerial, have been used in the geomorphometric analysis of the Alaknanda basin. The weights were assigned on a scale of 1 for flood-induced factors such as slope (0.386), rainfall (0.192), drainage density (0.129), LULC (0.096), geology (0.077), soil (0.064), and DEM (0.055) have been determined using 7×7 decision matrix of multi-criteria decision making- analytical hierarchy processes (MCDM-AHP) model accounting for their varying importance from high to low priorities. A consistency ratio (CR) of 0.083 (less than 0.1) signifies the acceptance of the weights derived in the case. The high shape index, form factor, and rotundity factor indicate the basin allows quick drainage of surface runoff with a lesser time of concentration; as a result, sharp peaks of the hydrograph will be obtained at the outlet of the basin. However, most of the basin area is under the moderate vulnerability category, while the entire river banks along the valley are the most vulnerable. Additionally, a small-scale sociological study indicates that for a population that resides close to highly susceptible locations, temporary relocation is the most common strategy.

Risk Assessment and Mapping of Flash Flood Vulnerable Zones in Arid Region, Fujairah City, UAE-Using Remote Sensing and GIS-Based Analysis

A flash flood is the most common natural hazard that endangers people’s lives, the economy, and infrastructure. Watershed management and planning are essential for reducing flood damages, particularly in residential areas, and mapping flash flood-sensitive zones. Flash flooding is an interface dynamic between geoterrain system factors such as geology, geomorphology, soil, drainage density, slope, and flood, rather than only water movement from higher to lower elevation. Consequently, the vulnerability to flash floods necessitates an awareness of and mapping topographical features. A flash flood vulnerable zones (FFVZ) map is essential for thorough flash flood risk assessment and management to minimize its detrimental effects, particularly in residential areas, especially in cities like Fujairah with seven wadis flowing into the city and even though it has two main dams and fifteen breaker dams. So, in this work, eight satellite image-derived parameters rainfall, elevation, slope, land use/land cover (LULC), drainage density, geology, geomorphology, and soil were combined to predict the flash flood-vulnerable zones using a weighted overlay technique based on geographic information systems (GIS). Each element of the thematic maps is ranked and weighted according to how vulnerable it is to flash floods in the study area, with 55 km2 being classified as a very highly vulnerable area, 78 km2 as a high-risk area, 9.3 km2 as a moderate risk area, 70 km2 as a low vulnerable area, and 257 km2 as a very low vulnerable area. In addition, places with a very high vulnerability level include the Fujairah Airport, Fujairah Port, some residential neighborhoods in the city’s center, oil storage areas, two hospitals, and universities. Additionally, from 1990 to the present, Landsat and Sentinel 2 data showed consistent changes in vegetation and built-up areas. Therefore, in addition to helping policy and decision-makers make the best choices about the efficacy of the study area’s protective structures against the risk of flash floods in the future, the results can also be a valuable source of information.

Micro-spatial flood risk assessment in Nagaon district, Assam (India) using GIS-based multi-criteria decision analysis (MCDA) and analytical hierarchy process (AHP).

Nagaon is one of the highly flood-prone districts of Assam, India that recurrently experiences devastatingfloods resulting in the loss of lives and property and wreaking havoc on the district's socioeconomic infrastructure. Identification and mapping of spatial patterns of flood hazards, flood vulnerability, and flood risk zones (FRZs) of the district are, therefore, crucial for flood management and mitigation. The present study, therefore, attempts to delineate the FRZs of more than 930 villages in the Nagaon district by integrating the flood hazard and vulnerability layers in the geospatial environment using the multi-criteria decision analysis and analytical hierarchy process techniques. Here, seven flood hazard and vulnerability indicators are considered to derive each layer separately. The results indicate that about 15.14% of the district's total villages are in the very high FRZ, 27.93% in the high, 46.62% in the moderate, and 10.3% in the low FRZ. Further, bivariate correlation analysis is used to evaluate the results with the percentages of the population, cropland, and animals affected by floods at different temporal scales in order to ensure that the revenue circles with a higher percentage of area under high and very high FRZs genuinely have higher percentages of flood-affected cropland, people, and livestock. The significance of this research is evident in its pragmatic findings that could aid the stakeholders in managing and reducing flood risk at micro-spatial scales.

Bipolar intuitionistic fuzzy graph based decision-making model to identify flood vulnerable region.

Bipolar intuitionistic fuzzy graphs (BIFG) are an extension of fuzzy graphs that can effectively capture uncertain or imprecise information in various applications. In graph theory, the covering, matching, and domination problems are benchmark concepts applied to various domains. These concepts may not be defined precisely using a crisp graph when the vertices and edges are more uncertain. Therefore, this study defines the covering, matching and domination concepts in bipolar intuitionistic fuzzy graphs (BIFG) using effective edges with certain important results. To define these concepts when the effective edges are absent, some novel approaches are discussed. To illustrate the domination concepts, the applications in disaster management and location selection problems are discussed. Further, a BIFG-based decision-making model is designed to identify the flood-vulnerable zones in Chennai, where the city's most and least vulnerable zones are identified. From the proposed model, Kodambakkam ([Formula: see text]) is the most susceptible zone in Chennai. Finally, a comparative analysis is done with the existing techniques to show the efficiency of the model.

Flood Vulnerability Assessment of Communities in Akwa Ibom, Nigeria

Floods are natural events, however, due to various anthropogenic activities coupled with climate change, flooding tends to be both human-induced and natural events. Geographical Information Systems (GIS) techniques can spatially process different physical-environmental parameters in flood risk assessment and the present study determine the extent of vulnerability among the communities in Akwa Ibom State, Nigeria. From the analysis, spatial extent of 932.26 km2 (13.48%) showed low flood vulnerability level while a spatial extent of 3411.37 km2 (49.33%) and 2572.09 km2 (37.19%) indicated moderate and high flood vulnerability levels respectively. At the Local Government Areas (LGAs) level, the low vulnerability covers 932.27 km2 which represented 13.48% of the entire LGAs in Akwa Ibom State, the medium vulnerability covers a spatial extent of 3411.37 km2 which represented 49.33% of the entire state while the high vulnerability covers a spatial extent of 2572.09 km2 which represented 37.19% of the LGAs in the state. The outcome further revealed that Two (2) LGAs are found within the low vulnerability, nineteen (19) LGAs are found within the medium vulnerability while ten (10) LGAs are found within the high vulnerability. At the community level, the analysis indicated that among the 795 communities in the state, 138 of the communities which represent 17.36% of the entire communities have low vulnerability to flood hazards, 429 communities at 53.96% have medium vulnerability level while 228 communities which represent 28.68% of the entire communities in the state have high flood vulnerability level. The generated maps and identified LGAs and communities at different flood vulnerability zones are useful in all steps of disaster management (prevention, mitigation, preparedness, operations, relief and recovery) and should be considered during initial planning.

Flood vulnerability assessment in the Jamuna river floodplain using multi-criteria decision analysis: A case study in Jamalpur district, Bangladesh

Flood is one of the most common hazards in many countries, affecting both life and properties. The recent climatic changes have brought a rise in both flood frequency and intensity making this phenomenon more devastating to people. Especially, the riparian communities of Jamalpur along the Jamuna river suffer at least once every year accounting for this natural hazard. In a scenario like this, precaution is a crucial task to fight against this natural phenomenon. Therefore, this study attempts to identify the flood vulnerable zones of Jamalpur district situating beside Jamuna river with a multi-criteria analysis. Assessment of flood vulnerability included consideration of physical indicators like rainfall, drainage density, distance from river, slope, land use land cover, elevation, soil classes as well as social indicators like population density, female density, literacy rate, number of shelters, unmetalled road and number of health personnel. All these parameters were analyzed in Geographic Information System (GIS) and assigned weightage with a multi-criteria decision-making technique namely- Analytic Hierarchy Process (AHP). Finally, a flood vulnerability map was generated for the seven Upazilas of Jamalpur district. Very high, high, moderate and low vulnerable zones were identified after overlaying all physical and social indicator maps under consideration. 45.96% portion of a total of 2115.2 sq. Km. Area was under very high and highly vulnerable zones in the final vulnerability map of Jamalpur. Such type of assessment process is significant for flood mitigation projects with a view to providing the greatest concern to the most vulnerable zones. The vulnerability maps can also help the policymakers to provide emergency aids and other privileges to those who are in most need of it.

Climate change and urban flooding in South-South Nigeria from 1990 - 2020

Flooding is one of the disasters that are common in our towns and villages. It occurs as a result of heavy or continuous rainfall over a period of time. Occurrence of flood damages our valuable infrastructure such as buildings, roads, bridges, and schools, churches etc. The study area being a coastal region is confronted with frequent flooding and the extent of damage, and contributing factors has not been fully studied, particularly in applying remote sensing and geographic information system. Also, very little information is known about the combine impacts of urbanization and climate change on urban flooding in the area. Hence this study was aimed at evaluating the impact of climate change and urban development on urban flooding in South – South Nigeria using geospatial techniques. Datasets utilized for the study includes; land use/ land cover derived from Landsat satellite data, soil map, slope model, soil infiltration rate, impervious surface derived from vegetation indices, distance from river, precipitation, and surface temperature data. These datasets were processed into common coordinate system. Landsat data of four epochs (1990 – 2020) was used to map urbanization. Accordingly, the variations in climate variables were investigated using NiMET reported and the satellite derived values. Flood vulnerability zones was determined using multi-criteria analysis implemented in ArcGIS weighted overlay tool. The study observed that urbanization increases from 1990 to 2020, which also increase impervious surfaces, making the area more vulnerable to flooding. In addition, wetland and vegetation cover are being replaced by built-up thereby decreasing the ability of wetland to retained flood water. The lowest rainfall was in 1990 with value 293.70mm and the highest was in 2000 with value 454.90mm. There is general variation of rainfall and temperature from 1990 – 2020 due to climate change. Total built-up at risk was 8873.55ha with most of them situated along the river banks. Geospatial technology is suitable for urban flood vulnerability modeling. For further study, it is recommended that high spatial resolution image should be use to map trends in urbanization. In addition, drainage density should be integrated in flood vulnerability modeling.

Flood Hazard and Management in Cambodia: A Review of Activities, Knowledge Gaps, and Research Direction

Cambodia is located in one of the most severe flood-vulnerable zones in mainland Southeast Asia. Flooding is the country’s most recurrent and impactful hazard among other natural hazards. This hazard alone, observed in many river basins, has been inflicting huge damages on livelihoods, social infrastructure, and the country’s economy. This study aims to review the current status of flood hazards, impacts, driving factors, management capacity, and future research directions on floods in Cambodia. The findings of this study suggested that there is still a lack of flood-related studies on flood hazard mapping, risk and damage assessment, and future flood analysis in Cambodia. The existing related studies mainly focused on the Tonle Sap Basin and its tributaries, the Lower Mekong Basin, the whole Mekong River Basin, and some of the tributaries of the Mekong River in Cambodia. The fundamental driving factors of the current flooding in Cambodia are impacts of climate change, land-use change, water infrastructure development, and weather extremes. The applications of mathematical and statistical tests and indices, conceptual and physically-based modeling, artificial intelligence and machine learning, and remote sensing are recommended to focus on future research directions on flood in Cambodia in the areas of land-use change, existing and planned operation of water infrastructure, flood hazard and damage assessment, and flood forecasting. The outcomes from these studies and applications would improve the understanding of flood hazard characteristics, reinforce flood management, and achieve flood damage reduction.

Flood Susceptibility Mapping Using an Analytic Hierarchy Process Model Based on Remote Sensing and GIS Approaches in Akre District, Kurdistan Region, Iraq

In recent decades, floods have been the most common, complex, and destructive natural calamities worldwide. Hence, for inclusive flood risk assessment, creating flood susceptibility mapping to demarcate flood-vulnerable zones is fundamental for decision makers. To assess flood-prone locations in the Akre, Iraqi Kurdistan Region, fundamental for susceptibility mapping was undertaken using geographic information systems, remote sensing, and an analytic hierarchy process model. To assess flood susceptibility, the geographic information systems framework used 15 ideal causative factors for flooding: altitude, slope, distance to streams, flow accumulation, drainage density, rainfall, soil type, lithology, curvature, topographic wetness index (TWI), topographic roughness index stream power index, stream transport index, land use/land cover, and normalized difference vegetation index. The factors contributing to flooding were optimally weighted with respect to the proposed model. The final flood susceptibility map was reclassified into five different classes of susceptibility to flooding: very low (16.64% of the study area); low (19.53%); moderate (38.92%); high (17.83%); and very high (7.08%). The area under curve values for the predicted rate and success rate of the AHP model were 0.956 and 0.971, respectively. Therefore, the results were accurate and reliable. The AHP model is a powerful method for fundamental for susceptibility mapping to mitigate the serious impacts of flooding and assist scholars, local governments and policymakers in future master planning.

Flood vulnerable zones mapping using geospatial techniques: Case study of Osogbo Metropolis, Nigeria

Several Areas within Osogbo, the capital city of Osun State in Nigeria, have suffered severely from incessant flood occurrences, which necessitated flood vulnerability mapping. This research aimed to map out flood vulnerable areas within Osogbo Metropolis using geospatial techniques to facilitate fitting planning and proffer lasting solutions to the frequent damage that humans and their properties suffer from flooding within the city. Satellite data of Osogbo Metropolis were processed and analyzed in a GIS environment for flood vulnerable zones mapping. Eight (8) flood causative factors were combined in this study using Analytic Hierarchy Process (AHP). The factors are Stream Proximity (SP), Drainage Density (DD), Elevation, Slope, Stream Power Index (SPI), Topographic Wetness Index (TWI), Normalized Difference Vegetation Index (NDVI), and Land Use/Land Cover (LULC). Results revealed that approximately 24% of the total area of study falls within the high flood vulnerable zones, while 21% and 55% fall within the moderate and low flood vulnerable zones, respectively. A close agreement between the flood vulnerable zones and previous flood incidences across the study area established the accuracy of the methodology. This study's established flood vulnerable zones will guide the respective policymakers during discussions on preventing flood disasters within the Osogbo metropolis.

Analyzing flash flood risk in a section of ntawogba creek, Port Harcourt city, Rivers State

Flood is a natural disaster and occurred in different forms. It is also a factor of climate change. Flash flood is a type of flood that occurs within few hours of intensive precipitation. It poses serious threat to urban areas, particularly in less developed countries. Geospatial technology has been very useful in mapping spatial extent and quantified damages associated with flash flood events. Proximity analysis and AHP has been widely utilized in modeling flash flood vulnerable zones. Little was known about MCA ranking method which is considered appropriate for few parameters in making complex decisions. This study utilized MCA ranking method to analyzed flash flood risk within Ntawogba Creek, Port Harcourt, Rivers State. Rainfall, slope model, impervious surface and distance to river were used in the decision making. The datasets were processed into common spatial referenced system. Each criterion was scaled from 5 to 1, with 5 being very high risk and 1 very low risk area. Weights were assigned to the criteria based on the importance, with higher weight to criterion that is more important in the decision making process. The weights are; distance to water channel 48, impervious surface 25, slope 22 and rainfall 5. The results obtained showed area of 93.0ha as very high flash flood risk representing 6% of the area. This zone was located along Ntawogba Creek and on major roads. Total area of high risk zone was 864.1ha representing 56%, moderate risk and low risk zone was 452.2ha and 134.7ha with 29.3 5 and 8.7% respectively. For further study, drainage density and soil infiltration should be included in the flash flood risk analysis.

Flood vulnerability of a few areas in the foothills of the Western Ghats: a comparison of AHP and F-AHP models.

Flooding is one of the most destructive natural catastrophes that can strike anywhere in the world. With the recent, but frequent catastrophic flood events that occurred in the narrow stretch of land in southern India, sandwiched between the Western Ghats and the Arabian Sea, this study was initiated. The goal of this research is to identify flood-vulnerable zones in this area by making the local self governing bodies as the mapping unit. This study also assessed the predictive accuracy of analytical hierarchy process (AHP) and fuzzy-analytical hierarchy process (F-AHP) models. A total of 20 indicators (nine physical-environmental variables and 11 socio-economic variables) have been considered for the vulnerability modelling. Flood-vulnerability maps, created using remotely sensed satellite data and geographic information systems, was divided into five zones. AHP and F-AHP flood vulnerability models identified 12.29% and 11.81% of the area as very high-vulnerable zones, respectively. The receiver operating characteristic (ROC) curve is used to validate these flood vulnerability maps. The flood vulnerable maps, created using the AHP and F-AHP methods, were found to be outstanding based on the area under the ROC curve (AUC) values. This demonstrates the effectiveness of these two models. The results of AUC for the AHP and F-AHP models were 0.946 and 0.943, respectively, articulating that the AHP model is more efficient than its chosen counterpart in demarcating the flood vulnerable zones. Decision-makers and land-use planners will find the generated vulnerable zone maps useful, particularly in implementing flood mitigation plans.