Flood Vulnerability Index Research Articles

Risk management using a flood vulnerability index in the Mazovia Province, Poland

ABSTRACT The main purpose of the article is to assess the level of flood vulnerability in municipalities of Mazovia Province in Poland using a flood vulnerability index. The study investigates the following elements: exposure, susceptibility and resilience. The total flood vulnerability index of municipalities showed that the municipality of Wyszków had the highest level of vulnerability, which stems from its highest exposure indicator, its lack of resilience level and its moderate level of susceptibility. Municipalities where vulnerability is primarily affected by exposure should pursue an appropriate policy that restricts land use and development within floodplains. In areas where susceptibility has the greatest impact on flood vulnerability, the community should be more actively involved in the design of risk management plans to enhance their acceptance and increase the likelihood of successful implementation. Municipalities with a lack of resilience should invest in the manpower and equipment necessary to rescue people effectively; effect a swift return to a higher level of adaptation and resilience so that future flood losses are reduced or maybe even avoided.; increase expenditure on civil protection, and verify whether the number and available deployment of emergency services (firefighters, etc.) corresponds to the number and distribution of the population.

Flood Exposure, Vulnerability, and Risk Distribution in Urban Areas: Application of Geospatial Data Analytics and Index Development

Over the past few decades, cities have experienced increased floods affecting property and threatening human life as a result of a warming planet. There is still an incomplete understanding of the flood risk patterns in urban communities with different socioeconomic characteristics. In this study, we produced separate flood exposure and vulnerability indices based on relevant factors, then combined them as a risk index for Houston, Texas and Charleston, West Virginia. We applied statistical methods to extract the most significant social vulnerability factors in each study area. Finally, we mapped significant hot spots or clusters of high flood risk and compared results to socioeconomically disadvantaged populations. Based on the results, high-risk or 1%-annual-chance floodplains cover 23% of the Houston and 7% of Charleston study areas. Within these floodplains, 13% of the total developed land in Houston and 9% in Charleston are situated. In the event of a 1%-annual-chance flood, an estimated 5% of the total population in Houston and 6% in Charleston may require evacuation. Statistically significant flood risk clusters could only be identified in Houston. The implications from this work help to provide an analysis framework for larger urban areas while offering suggestions for its improvement in smaller populated areas.

Flood Susceptibility and Risk Mapping of Kathmandu Valley Watershed, Nepal

Comprehensive flood risk assessment is often constrained by a lack of appropriate data in high-altitude watersheds, particularly in developing countries like Nepal, where institutional capacities are limited for mapping and monitoring flood-prone communities. This study, one of the first of its kind, produced spatial multi-criteria-based flood susceptibility, vulnerability, and risk index maps for the Kathmandu Valley (KV) watershed in Nepal using an Analytical Hierarchy Process (AHP) approach and Geographical Information System (GIS). The result shows that most parts of the KV (around 80%) have moderate to low flood susceptibility around the watershed but susceptibility is prominent in southern areas. Highly flood-susceptible regions (about 14%) are found mainly along the riverbanks. Flood vulnerability, primarily influenced by population density and literacy rate, is moderate to low in most areas of the watershed (around 86%), predominantly higher in the central urban areas, and gradually lower towards the edge of the watershed. Flood risks in the major portion of the watershed (around 72%), mainly in the southern and eastern parts, are estimated as moderate to low risk, whereas higher risk zones are found in the central urban areas. The high contrast in flood vulnerability scores across the watershed has mainly contributed to the variation of flood risk zones, as flood susceptibility scores are fairly distributed over the watershed. The study findings will help policymakers develop location-specific sustainable flood risk management strategies for the flood-vulnerable communities in the KV watershed.

Flood Vulnerability Index Based on Indicator Approach in Assessing Flood Risk in Selangor, Malaysia

Floods are one of the most common and damaging natural disasters, claiming lives, and causing significant economic damage. Urbanization can increase the risk of flooding due to increased water runoff and volume. This study focuses on a flood vulnerability assessment approach as a planning tool by combining the Flood Vulnerability Index (FVI) with variables available in urban development areas. This index was created by combining the variables of three components: exposure, susceptibility, and resilience. FVI is a critical element in risk management and flood damage assessment. Vulnerability is described in FVI research as the amount of damage that can be predicted under conditions of exposure, susceptibility, and resilience. Vulnerability studies frequently use an indicator-based vulnerability assessment to estimate the complexity and degree of risk. FVI reflects the vulnerability of urban areas. Secondary data were used to obtain variables for the social and economic components. A quantitative method was used to calculate the correlation of each variable with flooding via the Statistical Package for the Social Sciences (SPSS). The indicators are categorized as exposure, susceptibility, and resilience, and these indices were created using normalization, weighting, and aggregation of the indicators. To determine the flood vulnerability in Selangor, a small number of indicators were selected to quantitatively assess the flood risk in Selangor. Based on calculated FVI, the vulnerability of the districts in Selangor has been ranked and categorized from high to very low. The result shows Gombak and Klang are the most vulnerable districts in Selangor to flooding because of their high exposure to flooding. The results of this study could serve as a reference tool for future flood risk mitigation efforts in Selangor.

Flood risk assessment in Kogi State Nigeria through the integration of hazard and vulnerability factors

Annually, Kogi State in Nigeria experiences significant flooding events, leading to serious fatalities, the destruction of livelihoods, and damage to vital infrastructure. This study presents a multi-faceted approach and methodology to generate a state-wide flood risk map by analyzing both flood vulnerability and flood hazard factors. Seven flood hazard factors (drainage length, distance to river, elevation, slope, rainfall, distance from confluence/dam area, and geomorphology) and vulnerability factors (population density, female population, land cover, road length, distance from hospitals, literacy rate, and employment rate) were ranked and weighted based on their contributions to flooding within the state using the Fuzzy Analytical Hierarchy Process (FAHP). From these, the Flood Hazard Index (FHI), Flood Vulnerability Index (FVI), and Flood Risk Index (FRI) were derived. Results showed that Kabba, Idah, Olamabor, Kotonkar, and the southern part of Ajaokuta LGAs exhibit high flood vulnerability due to dense populations, remoteness from roads and critical infrastructure, and considerable distances from healthcare facilities. Likewise, LGAs exhibiting a very high FHI occur along the geographic zones bounded by the confluence of the Niger and Benue rivers, specifically along the Lokoja area, and the Kotonkar, Kogi, Bassa, and Ibaji LGAs. Five flood risk classes—very low, low, moderate, high, and very high FRI classes—occupy 26.82, 31.12, 22.07, 15.26, and 4.71% of the area, respectively. Out of 295 villages, 65 villages are spread across the high flood risk zone. The safest LGAs include Ankpa, Omala, Dekina, Ijumu, Kabba, and Mopa-Muro LGAs.

Monitoring spatiotemporal changes in urban flood vulnerability of Peninsular Malaysia from satellite nighttime light data

Urban flood vulnerability monitoring requires a large amount of socioeconomic and environmental data collected at regular time intervals. However, collecting such a large volume of data poses a significant constraint in assessing changes in flood vulnerability. This study proposed a novel method to monitor spatiotemporal changes in urban flood vulnerability from satellite nighttime light (NTL) data. Peninsular Malaysia was chosen as the research region as floods are the most devastating and recurrent phenomena in the region. The study developed a flood vulnerability index (FVI) based on socioeconomic and environmental data from a single year. This FVI was then linked to NTL data using an Adaptive neuro-fuzzy inference system (ANFIS) machine learning algorithm. The model was calibrated and validated with administrative unit scale data and subsequently used to predict FVI at a spatial resolution of 10 km for 2000–2018 using NTL data. Finally, changes in estimated FVI at different grid points were evaluated using the Mann-Kendall trend method to determine changes in flood vulnerability over time and space. Results showed a nonlinear relationship between NTL and flood vulnerability factors such as population density, Gini coefficient, and percentage of foreign nationals. The ANFIS technique performed well in estimating FVI from NTL data with a normalized root-mean-square error of 0.68 and Kling-Gupta Efficiency of 0.73. The FVI revealed a high vulnerability in the urbanized western coastal region (FVI ∼ 0.5 to 0.54), which matches well with major contributing regions to flood losses in Peninsular Malaysia. Trend assessment showed a significant increase in flood vulnerability in the study area from 2000 to 2018. The spatial distribution of the trend indicated an increase in FVI in the urbanized coastal plains, particularly in rapidly developing western and southern urban regions. The results indicate the potential of the technique in urban flood vulnerability assessment using freely available satellite NTL data.

Flood vulnerability assessment in rural and urban informal settlements: case study of Karonga District and Lilongwe City in Malawi

Flood vulnerability assessment (FVA) informs the disaster risk reduction and preparedness process in both rural and urban areas. However, many flood-vulnerable regions like Malawi still lack FVA supporting frameworks in all phases (pre-trans-post disaster). Partly, this is attributed to lack of the evidence-based studies to inform the processes. This study was therefore aimed at assessing households’ flood vulnerability (HFV) in rural and urban informal areas of Malawi, using case studies of Traditional Authority (T/A) Kilupula of Karonga District (KD) and Mtandire Ward in Lilongwe City (LC). A household survey was used to collect data from a sample of 545 household participants. Vulnerability was explored through a combination of underlying vulnerability factors (UVFs)-physical-social-economic-environmental and cultural with vulnerability components (VCs)-exposure-susceptibility and resilience. The UVFs and VCs were agglomerated using binomial multiple logit regression model. Variance inflation factor (VIF) was used to check the multicollinearity of variables in the regression model. HFV was determined based on the flood vulnerability index (FVI). The data were analysed using Multiple Correspondence Analysis (MCA), artificial neural network (ANN) and STATA. The results reveal a total average score of high vulnerability (0.62) and moderate vulnerability (0.52) on MCA in T/A Kilupula of Karonga District and Mtandire Ward of Lilongwe City respectively. The FVI revealed very high vulnerability on enviroexposure factors (EEFs) (0.9\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.9$$\\end{document}) in LC and(0.8\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(0.8$$\\end{document}) in KD, followed by ecoresilience factors (ERFs) (0.8) in KD and(0.6\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(0.6$$\\end{document}) in LC and physioexposure factors (PEFs) (0.5)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.5)$$\\end{document} in LC besides 0.6 in KD for the combined UVFs and VCs. The study concludes that the determinants of households’ flood vulnerability are place settlement, low-risk knowledge, communication accessibility, lack of early warning systems, and limited access to income of household heads. The study recommends that an FVA framework should be applied to strengthen the political, legal, social, and economic responsibilities of government for building the resilience of communities and supporting planning and decision-making processes in flood risk management.

Effect of Metro rail extension on flood risks of Bangkok Metropolitan Authority outskirt due to climate and land use land cover changes

ABSTRACT The Bangkok Metropolitan Administration's expansion, driven by economic growth, includes extending Metro systems and altering land use. This transformation, especially noticeable near these routes, has led to shifts in land cover – reduced green spaces, increased impervious surfaces, and decreased water storage. These changes, coupled with denser population and higher waste-generation, have compromised drainage efficiency, amplifying flood vulnerability. This study focuses on Pink Line's 34.5-kilometre stretch from Khae Rai intersection along Tiwanon Road to Min Buri, aiming to evaluate Flood Vulnerability Index (FVI) near the Metro line, identify critical influencing factors and propose mitigation strategies. The analysis divided the study area into five zones based on characteristics, evaluating FVI using population density (PO), drainage efficiency (DE), impervious ratio (IR), garbage management (GB), and pond area ratio (PA). FVI values ranged from 0.41 to 0.55. Sensitivity analysis showed minor FVI impacts at Lat Pla Khao and Ram Intra km.4 stations due to DE and IR, with reduced FVI at Khu Bon and Eastern Ring Road stations from PA and GB improvements. PA and IR enhancements consistently lowered FVI, while fluctuations were observed with GB and DE changes, with notable impacts at Min Buri Market Station. Understanding factor sensitivity aids in planning local mitigation-strategies.

Urban flood vulnerability Knowledge-Graph based on remote sensing and textual bimodal data fusion

Flood disasters inflict extensive, serious damage on cities and society, significantly constraining urban construction and development. There is an urgent demand to reduce urban flood vulnerability, to explore the evolution mechanism of urban flood vulnerability, and to guide the construction and improvement of urban flood control resilience. An objective and accurate knowledge graph of urban flood vulnerability intuitively, quantitatively, and conveniently expresses the logical relationship between vulnerability indexes. This provides a theoretical and data-based foundation for enhancing urban flood resistance and improving the safety of urban flood control and drainage systems. To address this issue, first, the fusion extraction of text and remote sensing dual-mode data is achieved through technical means such as neural networks. Second, by using multiclass natural language processing (NLP) models, we create an objective index system for urban flood vulnerability that avoids subjective human influence. Finally, we construct an objective weight model group, calculate weights, and then, we establish a vulnerability knowledge graph. The results indicate that (1) by utilizing multidimensional remote sensing images and by adopting the robotic satellite (RoboSat) semantic segmentation model, we achieve high-precision extraction of the remote sensing parameters, such as those for urban roads, terrain, and buildings. Thus, we successfully transform remote sensing data into text data (accuracy is approximately 1 m). (2) We have confirmed the effectiveness of the subjective–objective combined weight method. (3) We introduce a novel approach to create an urban flood vulnerability index system based on bimodal objective data fusion. (4) Utilizing the flood vulnerability knowledge graph, we assess vulnerability levels within the primary urban areas of Zhengzhou City, and we propose governance strategies tailored to the current vulnerability status of each district.

Measuring vulnerability to assess households resilience to flood risks in Karonga district, Malawi

AbstractMany parts of Malawi are prone to natural hazards with varying degrees of risk and vulnerability. This study aimed at obtaining baseline data for quantifying vulnerability of the households to flood risks in Karonga District in northern Malawi, specifically in Group Village Headman Matani Mwakasangila of Traditional Authority Kilupula. The study used cross-sectional survey, and data were collected using a structured questionnaire. This study applied Flood Vulnerability Index and statistical methods to quantify and analyse vulnerability of households in the aspects of exposure, susceptibility and resilience characteristics. Proportional Odds Model also known as Ordered Logistic Regression was used to identify factors that determine vulnerability of households to flood risks. The results show that households headed by females and elders of age (at least 61 years) were the most vulnerable to floods because of their limited social and livelihood capacities, resulting from being uneconomically active group. Households with houses built of mud, thatched and very old with no protective account for high vulnerability due to the fact that most of them are constructed using substandard materials. The level of vulnerability was increasing with an increase in the number of households exposed and susceptible to floods. With an increase in resilience to floods, vulnerability level was decreasing. The results further revealed a predictive margins of vulnerability levels which were not significantly different among the villages. However, villages with more exposed, susceptible and not resilience households were most vulnerable to floods. This study recommends that vulnerability assessment should be included in Disaster Risk Reduction planning and implementation in order to make DRR more efficient and realistic. This would further strengthen the disaster risk management to be more proactive as well as increase resilience of households to flood risks.

A GIS-Based Flood Risk Assessment Using the Decision-Making Trial and Evaluation Laboratory Approach at a Regional Scale

This paper introduces an integrated methodology that exploits both GIS and the Decision-making Trial and Evaluation Laboratory (DEMATEL) methods for assessing flood risk in the Kosynthos River basin in northeastern Greece. The study aims to address challenges arising from data limitations and provide decision-makers with effective flood risk management strategies. The integration of DEMATEL is crucial, providing a robust framework that considers interdependencies among factors, particularly in regions where conventional numerical modeling faces difficulties. DEMATEL is preferred over other methods due to its proficiency in handling qualitative data and its ability to account for interactions among the studied factors. The proposed method is based on two developed causality diagrams. The first diagram is crucial for assessing flood hazard in the absence of data. The second causality diagram offers a multidimensional analysis, considering interactions among the criteria. Notably, the causality diagram referring to flood vulnerability can adapt to local (or national) conditions, considering the ill-defined nature of vulnerability. Given that the proposed methodology identifies highly hazardous and vulnerable areas, the study not only provides essential insights but also supports decision-makers in formulating effective approaches to mitigate flood impacts on communities and infrastructure. Validation includes sensitivity analysis and comparison with historical flood data. Effective weights derived from sensitivity analysis enhance the precision of the Flood Hazard Index (FHI) and Flood Vulnerability Index (FVI).

Design and Implementation of an Interactive Visualization Dashboard for Monitoring the Flood Vulnerability and Mapping

This study aims to build a web-based interactive visualization dashboard from granular flood vulnerability index estimation maps using data from satellite imagery. The approach used to build this visualization dashboard is a two-dimensional (2D) approach created with the qgis2web python plugin facilitated with a JavaScript leaflet. Raw data from satellite imagery consisting of indicators of the causes of flooding are extracted in comma-separated value (CSV) format. Furthermore, the data is integrated based on its spatial attributes and stored in Geographic JavaScript Object Notation (GeoJSON) format to produce a visualization of the flood vulnerability index map. In web views, dashboards are built by utilizing hypertext markup language (HTML), cascading style sheets (CSS), and JavaScript (JS). This interactive dashboard has several useful features in helping the process of monitoring the flood vulnerability of an area such as zoom, "show me where I am", measure distance, search, legend, and change year. Thus, the flood vulnerability estimation map dashboard is expected to assist the government in monitoring areas with extreme flood vulnerability and support the decision-making process related to mitigation of areas that have high flood vulnerability.

Assessment of Flash Flood Vulnerability Index in a tropical watershed region: a case study in Ciliwung Hulu watershed, Indonesia

Flash floods, an unpredicted swift climatological disaster, frequently occur in Indonesia. However, there are limited vulnerability assessments, especially in urban and vital regions such as Bogor District. The study aims to assess the vulnerability index of Ciliwung Hulu Watersheds as one of the most susceptible areas in the district. Flash flood vulnerability index (FFVI) is selected to be calculated as the indicator. Data were obtained from the official government offices and processed using the FFVI formula referring to the work of Nasiri et al., (2019) and Perka BNPB No. 2/ 2012 and then mapped using ArcGIS 10.3. The results and the maps show that the study area is categorized as highly to very highly vulnerable to flash flood disasters. The attained results help facilitate the governance interplay processes in building a more disaster-ready management plan and to construct a more resilient society.

Assessment of flood mitigation strategy based on integrated approach of remote sensing and coastal vulnerability geospatial modeling at the coastal plain of Suriname

Suriname is the smallest South American nation with a low-lying coastal plain that is vulnerable to inundation from the Atlantic Ocean and inland rivers, as well as pluvial flooding primarily due to rainfall. Paramaribo, the capital of Suriname, has the highest population density, and its demographics extend into the surrounding districts of Wanica and Commewijne. Suriname has experienced flood disasters almost annually, which has exacerbated in recent years, posing a significant socioeconomic challenge. The country must balance the need for flood disaster adaptation and climate resilience with the potential impact on its resources and well-being of settlement areas. Policymakers and other stakeholders are working to address environmental impacts on the coast, but there is still a need for a comprehensive approach to monitor and manage flood impacts. This research has three objectives. The first is to analyze flood frequency events from 2021 to 2023 using multi-temporal satellite image processing from Sentinel-1 SAR (Synthetic Aperture Radar). The second is to generate a Coastal Flood Vulnerability Index (CFVI) for floods using a geospatial multi-criteria analysis approach based on exposure, sensitivity, and adaptive capacity components. The third objective is to assess the mitigation strategy for floods in settlement areas based on an integrated analysis of CFVI and stakeholder perception. The research methodology uses a multi-criteria analysis regarding settlement areas and ranking each component by expert opinion in an equation derived from Intergovernmental Panel on Climate Change (IPCC) assessment report at district level. The CFVI indices rely on secondary data acquisition from national and global datasets or referenced works. Interviews were conducted to better understand the stakeholder’s perspectives that are at a strategic or governing level, and to evaluate the existence of flood early-warning and other adaptation capabilities. A flood mitigation strategy is then suggested for the most vulnerable district by CFVI score.

Flood vulnerability assessment of Thailand's flood-prone Pathum Thani province and vulnerability mitigation strategies

This research assesses the flood vulnerability of Thailand's flood-prone province of Pathum Thani using a comprehensive vulnerability assessment framework. The assessment framework incorporates three key components: exposure, sensitivity, and adaptive capacity, consisting of 10, 12 and 11 flood vulnerability indicators, respectively. The flood vulnerability components and the flood vulnerability indicators are statistically validated by confirmatory factor analysis to determine the factor loadings and reliability of the components and indicators. The flood vulnerability questionnaire corresponding to the flood vulnerability indicators is subsequently developed and applied to the flood-prone districts of the province. The results show that proximity to rivers (with an indicator score of 0.685), household debt levels (0.612), land use patterns (0.617), and the proportion of low-income households (0.621) significantly contribute to the flood exposure of the province (with an exposure index score of 0.531). Larger household size (with an indicator score of 0.901), disruptions in public utility services (0.747), and workplace absenteeism due to flooding (0.741) contribute to the province's higher flood sensitivity (with a sensitivity index score of 0.633). Drainage capacity of natural and man-made waterways (0.571) contributes to low to moderate levels of flood adaptive capacity. The flood vulnerability of seven administrative districts of Pathum Thani, as measured by the flood vulnerability index scores (0.454–0.608), range from moderate to high. Local authorities need to invest in flood warning and response systems, prioritize infrastructure development and encourage community engagement to reduce the flood vulnerability.

Understanding Flood Vulnerability Using Index-based Approach: A Study of Chilmari Upazila, Kurigram, Bangladesh

The lives and livelihoods of the people of Bangladesh are regularly threatened by natural disasters and climate dangers. Floods are the most common occurrence that puts people and their properties at risk. This study was conducted to assess the flood vulnerability of Chilmari upazila, which is located in Kurigram district of Bangladesh. Using a random sampling technique, primary data was obtained from 395 households in six unions of Chilmari upazila through a structured questionnaire survey. The Flood Vulnerability Index (FVI), an index-based approach, has been used to assess flood vulnerability. A set of 23 indicators has been fixed to assess four components of floods: physical, economic, social and environmental. Following this procedure, an FVI score was calculated for each of the unions (administrative unit of upazila) of Chilmari. Ashtamir Char, Nayerhat and Chilmari were designated as ‘very vulnerable’ based on the FVI study. Since these unions have relatively low levels of adjustability, coping ability and resilience, their sensitivity and vulnerability are high. Two of the unions of Chilamri are found to be highly vulnerable to floods and another to be generally vulnerable to floods. The results of the FVI analysis have been used to produce a flood vulnerability map of Chilmari upazila. As FVI consists of multi-dimensional indicators, the analysis can be used to pursue specific flood-mitigation measures in Chilmari.

Flood risk assessment of Wuhan, China, using a multi-criteria analysis model with the improved AHP-Entropy method.

Floods are one of the most frequent global natural hazards resulting in significant human and economic losses. Therefore, assessing and mapping flood hazard levels is essential to reduce the severity of future flood disasters. This study developed an integrated methodology to evaluate flood risk using an improved Analytic Hierarchy Process (AHP) and Entropy Weight (AHP-EW) method based on cosine similarity (COS-AHP-EW). This method has more scientific results because it combines subjective and objective information. The proposed method's viability was then tested in Wuhan, China. Fourteen flood-inducing indicators were identified for the flood hazard, vulnerability, and restorability index system, with the indicator weights calculated using the COS-AHP-EW. This study utilized the Jenks method to develop the Wuhan flood risk map. We observed that the very high risk and high-risk areas covered 2.43% and 11.54% of the total study area and were mainly distributed in the highest economic and urbanization development and low-permeability districts, respectively. The validation with the historical waterlogging points reflected the accuracy and reliability of the COS-AHP-EW. The superiority of the proposed method was further verified by comparing it with single-evaluation methods (AHP and Entropy Weight) and another combined weight method (combined AHP-EW based on ideal point theory, namely, Ideal-AHP-EW). The comparison results indicated that the COS-AHP-EW was more accurate at predicting the risk in flood-prone area. Flood risk maps generated using the COS-AHP-EW could be applied to improve flood risk assessments, and the proposed method could be extended to other study areas to provide reliable flood management information.

Simulation of dam-break flood and risk assessment: a case study of Chengbi River Dam in Baise, China

Abstract When a reservoir is damaged, it will bring destruction to people's lives and the regional economy. Flood simulation and risk assessment are two effective ways to mitigate flood risk. Flood risk is assessed by using flood hazard and vulnerability indices. However, one of the key concerns is how to quantify hazards and vulnerabilities more rationally. To this end, this study introduces a new quantitative method for flood risk assessment. Three schemes – full dam breach (S1), 1/2 dam breach (S2), and 1/3 dam breach (S3) – were proposed for flood simulation. HEC-RAS 2D was used to simulate the evolution process of dam-break floods. This study used a new quantification approach to calculate flood risk based on simulation results. The results show the following: (1) The inundation process is similar under the three schemes, but the degree differs. The greater the degree of dam break, the greater the inundation depth, maximum flow velocity, and inundation duration. (2) High-risk areas decrease with decreased dam break degree. Under the three schemes, the flood risk areas of Longjing Street account for 65.37, 71.41, and 66.22% of the total risk areas, respectively, which are the areas most affected by dam-break floods.

Assessing Vulnerability in Flood Prone Areas Using Analytic Hierarchy Process—Group Decision Making and Geographic Information System: A Case Study in Portugal

A flood vulnerability index was constructed by coupling Geographic Information System (GIS) mapping capabilities with an Analytic Hierarchy Process (AHP) Group Decision-Making (GDM) resulting from a paired comparison matrix of expert groups to assign weights to each of the standardised criteria. A survey was sent to 25 flood experts from government organisations, universities, research institutes, NGOs, and the private sector (56% academics and 44% non-academics). Respondents made pairwise comparisons for several criteria (population, socio-economic, buildings, and exposed elements) and sub-criteria. The group priorities were obtained by combining the Consistency Ratio (CR) and Euclidean Distance (ED) measures to assess the weight of each expert and obtain a final weight for each criterion and sub-criteria. In Portugal, 23 flood-prone areas were considered, and this work contributes with a tool to assess the flood vulnerability and consequently the flood risk. The flood vulnerability index was calculated, and the relevance of the proposed framework is demonstrated for flood-prone areas, in mainland Portugal. The results showed that in all five hydrographic regions, flood-prone areas with very high vulnerability were found, corresponding to areas with a high probability of flooding. The most vulnerable areas are Ponte de Lima in the North, Coimbra, and Pombal in the Centre; Loures in the Tagus and West Region; Setúbal and Alcácer do Sal in the Alentejo Region and Monchique in the Algarve Region. This methodology has the potential to be successfully applied to other flood-prone areas, combining the opinions of stakeholders validated by a mathematical model, which allows the vulnerability of the site to be assessed.

Assessing Urban Flood Hazard Vulnerability Using Multi-Criteria Decision Making and Geospatial Techniques in Nabadwip Municipality, West Bengal in India

The flood hazard risks and vulnerability in the urban areas alongside major rivers of India have been gradually increasing due to extreme climatic events. The present study is intended to assess flood hazard vulnerability and potential risk areas and aims to ascertain the management strategies in Nabadwip Municipality, a statutory urban area of West Bengal. The multi-criteria decision making (MCDM) of selected criteria and geospatial techniques have been employed to determine the urban flood vulnerability in the study area. The study has been conducted using secondary datasets including relevant remotely sensed data and participant observation. The potential flood-affected zones have been determined using the normalized difference flood index (NDFI) and flood vulnerability index (FVI). The analysis of the standardized precipitation index (SPI) of 20 years of monthly precipitation shows the variability of seasonal rainfall distribution in the study area. Furthermore, the spatial distribution of the composite Ibrahim index of socio-economic development accents that the urban development of the study area was uneven. The municipal wards situated in the central and northeastern portions of Nabadwip Municipality were extremely vulnerable, whereas the western and southwestern wards were less vulnerable. It is also revealed from the strengths–weaknesses–opportunities–challenges (SWOC) of the principal management strategies of the flood situation analysis that the unplanned sewerage system is one of the most effective weaknesses in the area. All-embracing and integrative flood management strategies need to be implemented in the study area considering the intra-regional vulnerability and development for the resilient and sustainable development of the study area.