Flood Vulnerability Research Articles

Integrating Object-Based Classification and Multivariate Analysis to Evaluate Flood Vulnerability in Unplanned Settlements of Malang City, Indonesia

Integrating Object-Based Classification and Multivariate Analysis to Evaluate Flood Vulnerability in Unplanned Settlements of Malang City, Indonesia

Peri-Urban Floodscapes: Identifying and Analyzing Flood Risk Areas in North Bhubaneswar in Eastern India

Peri-urban catchment areas are increasingly susceptible to floods due to rapid land use transformations and unplanned urban expansion. This study comprehensively examines flood vulnerability in the rapidly developing peri-urban areas of North Bhubaneswar, focusing on significant changes in Land Use/Land Cover (LULC) and hydrological dynamics from 2004 to 2024, utilizing Geographic Information System (GIS) tools. The analysis reveals substantial shifts in land use patterns, with the urban footprint expanding by 71.8%, cropland decreasing by 21.7%, and forest areas by 13.6%. These changes have led to increased impervious surfaces, resulting in higher surface runoff and decreased groundwater recharge, thereby exacerbating flood risks in the region. The GRID-based vulnerability analysis classifies 90 villages within the catchment area based on their vulnerability levels, identifying 20 villages as high-risk areas requiring urgent attention, 44 villages as medium vulnerable, and 26 villages as low vulnerable. These classifications are based on factors such as proximity to drainage networks, slope, geomorphology, and LULC characteristics, with areas near drainage channels and low-lying regions being prone to flooding. The analysis integrates multiple factors to provide a comprehensive assessment of flood risk, highlighting the need for sustainable land use planning, conservation of vegetated areas, and the implementation of advanced flood prevention strategies in the peri-urban areas. Extending this research to other fringe regions could offer further valuable insights, guiding flood prevention and sustainable development strategies for areas undergoing significant land use transformations to effectively mitigate future flood risks.

A GIS Based Quick Assessment Method of Flood Vulnerability: Susurluk Basin Case

A GIS Based Quick Assessment Method of Flood Vulnerability: Susurluk Basin Case

Multi-Approaches for Flash Flooding Hazard Assessment of Rabigh Area, Makkah Province, Saudi Arabia: Insights from Geospatial Analysis

Flash flood hazard assessment is a critical component of disaster risk management, particularly in regions vulnerable to extreme rainfall and climatic events. This study focuses on evaluating the flash flood susceptibility of the Rabigh area, located along the Red Sea coast in Makkah province, Saudi Arabia. Using advanced GIS tools and a spatial multi-criteria analysis approach, the research integrates a variety of datasets, including remotely sensed satellite data, the SRTM Digital Elevation Model (DEM), and topographic indices. The main goal was to produce detailed flood susceptibility maps based on the morphometric characteristics of the region’s drainage basins. These basins were delineated and assessed for their flood vulnerability using three distinct modeling techniques, each highlighting different aspects of flood behavior. The results show that the northern basin (Dulaidila) and the central basins (Rabigh, Algud, and Al Nuaibeaa) exhibit the highest flood risk, with significant susceptibility also observed in the southern basins (Ofoq and Saabar). Other basins in the region display moderate susceptibility levels. A key aspect of this analysis was the overlay of the integrated flood susceptibility map with the Topographic Position Index (TPI), a crucial topographic indicator, which helped refine the understanding of flood-prone areas by linking basin morphometry with in-situ topographic features. This study’s comprehensive approach offers valuable insights that can be applied to other coastal regions where hydrological and climatic data are scarce, contributing to more effective flood risk mitigation and strategic planning.

Analisis Tingkat Kerawanan Banjir di Sub Daerah Aliran Sungai (DAS) Kampar Kiri di Kecamatan Kampar Kiri

This study aims to describe and analyze the flood vulnerability level in the Kampar Kiri Sub-Watershed, Kampar Kiri District, as well as the extent of flood-prone areas impacting settlements in the region. The primary focus of this research is to understand the flood risk of the area and its effects on the existing settlements. The method applied in this study is Multi-Criteria Evaluation (MCE) using a Geographic Information System (GIS) approach. MCE is used to determine the weights of parameters affecting flood vulnerability and to select appropriate methods for analysis. This approach has proven effective and efficient in flood risk analysis, as demonstrated in previous studies. The parameters analyzed include slope, elevation, rainfall, soil type, land use, and distance from rivers, which are calculated using scoring methods and overlaid to produce a flood vulnerability map. The results indicate that the flood vulnerability level in the Kampar Kiri Sub-Watershed is categorized into three classes: not vulnerable, vulnerable, and highly vulnerable. The areas for each class are: not vulnerable with 16,854 hectares, vulnerable with 58,543 hectares, and highly vulnerable with 11,506 hectares. The extent of settlements in the vulnerability zones includes: vulnerable zone with an area of 277 hectares (approximately 20.83%), and highly vulnerable zone with an area of 1,054 hectares (approximately 79.17%). Based on these findings, it is recommended that the Kampar Kiri District government enhance land use planning to reduce flood risks, collaborate with the Kampar Public Works and Spatial Planning Office, and improve infrastructure such as drainage channels and flood protection embankments.

Exploring a GIS-based analytic hierarchy process for spatial flood risk assessment in Egypt: a case study of the Damietta branch

Floods are the most common and costly disasters worldwide, while spatial flood risk assessment is still challenging due to fewer observations and method limitations. In this study, the flood risk zonation in the Nile districts of the Damietta branch, Egypt, is delineated and assessed by integrating remote sensing with a geographic information system, and an analytical hierarchy process (AHP). Twelve thematic layers (elevation, slope, normalized difference vegetation index, topographic wetness index, modified normalized difference water index, topographic positioning index, stream power index, modified Fournier index, drainage density, distance to the river, sediment transport index, and lithology) are used for producing flood susceptibility zonation (FSZ) and six parameters (total population, distance to hospital, land use/land cover, population density, road density, and distance to road) are utilized for producing flood vulnerability zonation. Multicollinearity analysis is applied to identify highly correlated independent variables. Sensitivity studies have been used to assess the effectiveness of the AHP model. The results indicate that the high and very high flood risk classes cover 21.40% and 8.26% of the area, respectively. In 14.07%, 27.01%, and 29.26% of the research area, respectively, flood risk zones classified as very low, low, and moderate are found. Finally, FSZ is validated using the receiver operating characteristics curve and area under curve (AUC) analysis. A higher AUC value (0.741) in the validation findings demonstrated the validity of this AHP approach. The results of this study will help planners, hydrologists, and managers of water resources manage areas that are susceptible to flooding and reduce potential harm.

Flood Vulnerability Mapping of the Kosi River Basin using a Multi-Criteria Decision-Making Approach

Flood Vulnerability Mapping of the Kosi River Basin using a Multi-Criteria Decision-Making Approach

Green spaces and flood vulnerability in urbanising regions: a case study in the Philadelphia metropolitan area

ABSTRACT Flooding continues to be one of the most significant natural hazards, disproportionately impacting vulnerable populations in urbanising regions. This study aims to examine the relationship between green space density and flood vulnerability in the Philadelphia Metropolitan Area by integrating social and geophysical data. The research focuses on identifying areas where high flood vulnerability coincides with low green space availability, as these are critical regions for targeted intervention. Using social vulnerability indicators such as income, age, and housing characteristics, combined with geophysical factors like soil permeability and drainage density, the study assesses flood vulnerability across census block groups. Local Indicators of Spatial Association (LISA) and discriminant analysis were employed to detect spatial clusters with elevated flood risks and low green space density. Results reveal that these high-risk clusters are concentrated in densely urbanised areas such as Norristown, where lower-income and elderly populations are overrepresented. These findings reinforce broader environmental justice concerns, as socially vulnerable communities face compounded risks from both inadequate flood protection and limited access to green spaces. The study emphasises the need for integrated flood management, combining green infrastructure with structural interventions, to guide urban planners and policymakers in reducing flood risks and promoting environmental equity.

Flood vulnerability map of the Bagmati River basin, Nepal: a comparative approach of the analytical hierarchy process and frequency ratio model

AbstractThe analytical hierarchy process (AHP) and frequency ratio model (FR), along with the integration of GIS, have proven to be successful approaches for assessing flood-prone areas. However, in Nepal flood vulnerability mapping based on GIS decision analysis is limited. Thus, this study focused on comparing the data-driven FR method and expert knowledge-based AHP technique in a GIS environment to prepare a flood vulnerability map for the Bagmati River basin, helping to explore the gap in flood vulnerability mapping methodologies and approaches. By combining all class-weighted contributing factors, like elevation, precipitation, flow accumulation, drainage density, soil, distance from the river, land use land cover, normalized difference vegetative index, slope and topographic wetness index, the study evaluated the efficiency of FR and AHP in assessing flood vulnerability maps. An inventory map of floods containing 107 flood points was created. Subsequently, the flood vulnerability maps generated using FR and AHP models revealed that 9.30% and 11.36% of regions were in highly vulnerable areas, respectively. Receiver operating characteristics validated the model outcomes, indicating that the FR model’s accuracy of 91% outperformed the AHP model’s 84% accuracy. The study findings will assist decision-makers in enacting sustainable management techniques to reduce future damage in the Bagmati basin.

Musculoskeletal Health Problems Among the Loom Weavers of Sirajganj District, Bangladesh

Land use change is increasing in West Nusa Tenggara province, especially in Bima district. This condition occurs because people who make land use change patterns that are utilized as agricultural areas for corn commodities. The limited agricultural land in Bima Regency with the potential for good selling prices of corn commodities is a major factor in land use change, this has an impact on flood disaster vulnerability. This study aimed to determine the condition of the livelihood assets of seasonal corn farmers in the Bima district. The research method used a descriptive presentative statistical approach. The sample in this study amounted to 97 respondents who were taken in 8 districts with flood vulnerability, namely Sanggar, Madapangga, Palibelo, Woha, Belo, Monta, Sape, and Lambu. Random sampling was used to determine the respondents. The results showed that the condition of the livelihood assets of seasonal corn farmers in Bima District from 8 flood-prone villages is dominated by human resources, where the average percentage value is 83%. This is followed by physical assets at 81% and natural and financial resources at 71%, while the lowest is social assets with a percentage value of 69%. The farmer livelihood assets with high percentage values in each flood-prone area are human resources in Sanggar, natural resources in Palibelo, financial and physical in Sape, and social assets in Palibelo and Woha. The pentagon of livelihood assets does not form a triangle, so it can be concluded that there is inequality between assets. The lack of agricultural land owned by farmers encourages the use of land cover as corn farmland. This can be seen from the use of land by farmers that is not their own, resulting in environmental damage that causes flood vulnerability. Keywords: Land use change, Livelihood assets, Flood Disaster

‘Don’t call us vulnerable!’: Rethinking flood vulnerability and community-led governance

ABSTRACT Traditional views on flood vulnerability often present a simplistic government-community relationship, portraying communities as passive recipients of aid. This perspective overlooks the dynamic agency of urban residents in shaping their own governance structures. Recent research challenges this binary thinking by revealing how communities actively manage flood risks. However, the intricate interplay between government absence and community-led actions, and their role in everyday governance, remains underexplored. Drawing on scholarship in flood vulnerability and community-led governance, this paper examines the experiences and management strategies of flood-prone residents in Dansoman, Accra, using interviews, focus groups, and “follow-along participant observation.” Three key themes emerged: “anticipate and navigate,” “collaborate and compensate for,” and “rupture and replace.” These themes illustrate how residents acquiesce to, reconfigure, or challenge government roles in flood management, establishing self-governance through community-led initiatives. These efforts often legitimize and strengthen community resilience, sometimes even usurping and replacing state authority. The findings underscore the need to recognize community agency in managing flood risks and compensating for insufficient government support through solidarity and self-help. Policymakers can enhance flood management by incorporating these community-driven insights, fostering more effective, inclusive, and sustainable strategies that reflect the fluid, evolving relationships between government and communities.

Smart hotspot detection using geospatial artificial intelligence: A machine learning approach to reduce flood risk

This study employs Geospatial Artificial Intelligence (GeoAI) and the Random Forest Machine Learning (ML) algorithm to enhance flood hazard assessments in Portugal. It utilizes NASA's LP DAAC (2023) Digital Elevation Model (DEM) and slope data from EPIC WEBGIS PORTUGAL DATA, offering detailed topographical insights for environmental planning. Additionally, it incorporates data on proximity to water bodies from the Portuguese Environment Agency and the European Environment Agency, and soil characteristics from EPIC WEBGIS PORTUGAL DATA, facilitating a thorough examination of flood risks. This approach prioritizes long-term land features over short-term weather patterns, providing a comprehensive understanding of flood vulnerability. The study processes data at a 1 km x 1 km resolution, adapting TIFF maps for compatibility with the Random Forest model. The produced flood hazard maps identify potential flood hotspots at both national and city levels, crucial for urban planning. These maps aid in assessing the vulnerability of key infrastructure and assets, such as transport networks and buildings. The research highlights the importance of integrating additional data on assets and socioeconomic factors to enhance urban resilience. It sets the stage for future research aimed at improving predictive accuracy and underscores the necessity of extensive geospatial analytics in managing infrastructure risks.

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 (>360cm) 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% (4.89km2) and 0.84% (8.16km2) annual rates respectively, subsequently the food risk zones categorized as a moderate, high and very high potential risk, have been spreading out with a yearly rate of 0.07% (0.65km2), 0.13% (1.31km2) and 0.03% (0.33km2) respectively. It decreases the area under low and very low-risk zones by 0.17% (1.63km2) and 0.07% (0.65km2) 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.

Flood vulnerability analysis using geographic information system in the core zone of the Lore Lindu biosphere reserve, Indonesia

Floods are caused by the accumulation of several factors, such as global warming, climatological characteristics, hydrology, and physical conditions of an area. The purpose of this study was to map the level of flood vulnerability in the core zone of the Lore Lindu Biosphere Reserve using geographic information system (GIS) based spatial analysis with scoring and overlay. The research parameters consisted of rainfall, elevation, slope, soil type, land cover, and distance from the river. This research was conducted in the core zone divided into 13 subdistricts and 2 regencies. The results of the classification of flood vulnerability levels are divided into 4 (four) categories: not prone, low vulnerability, moderate vulnerability, and high vulnerability. The results of the analysis show that the core zone of the Lore Lindu biosphere reserve is dominated by a non-hazardous site with an area of 145,018’28 ha (67.23%), a low vulnerability zone of 65,430.10 ha (30.33%), a moderate vulnerability zone of 5,025.29 ha (2.33%), and a high vulnerability zone of 245.99 ha (0.11). Areas with a high level of vulnerability are at low elevations with flat to gentle slopes. Most land use is dominated by water, mining, and open land without vegetation and is located around rivers.

Assessing boro rice yield variances across varied seedling ages to address early flash flood vulnerabilities in the Haor area of Bangladesh

Haor is one of the most flood-prone areas of Bangladesh and it underlies in water for several months of a year. Boro rice cultivation in this area is challenging due to the flash flood. An experiment was conducted to examine the suitability and productivity of four rice varieties transplanted in different seedling ages. This experiment was carried out at Sunamganj district for two consecutive years of Boro season in 2021-22 and 2022-23. Four Boro rice varieties namely Binadhan-10, Binadhan-17, Binadhan-24, BINA dhan25, and three seedling ages (i.e., 30, 35 and 40 days old seedlings) were used as treatment in the experiment. It was conducted through a split-plot design with three replications placing seedling ages in the main plot and crop varieties in the subplot. Transplanting of 30 days seedlings of Binadhan-24 produced the highest mean grain yield of 7.8 t ha-1 and 8.2 t ha-1 while 35 days seedlings produced 7.6 t ha-1 and 7.7 t ha-1 respectively, in two consecutive years, the highest among all varieties. The maturity period among crop varieties varied due to the changes in seedling age as lower seedling age showed an earlier maturity period. So, transplanting of 30-35 days seedlings can be recommended for Binadhan-24 cultivation in the haor area. Bangladesh J. Nuclear Agric, 38(1): 41-52, 2024

Flood Susceptibility Mapping Using Analytic Hierarchy Process and Geographic Information System in Baitarani Basin of Odisha, India

Baitarani, the 6th largest river basin in Odisha, eastern India, faces water related challenges in its catchments. As one of the most frequent environmental disasters, floods are consistently cited in hotspots of the eastern region in India. Hence, in this study, flood susceptibility maps were generated for Baitarani River basin by using analytical hierarchy process technique and geographic information system. Eleven thematic maps of topographic ruggedness index, topographic wetness index, stream power index, elevation, lithology, soil, slope, drainage density, distance from drainage network, land use/land cover and normalized difference vegetation index were prepared for flood vulnerability mapping. The themes were assigned suitable weights depending on relative influence of each of the themes on flood risk. The results indicated that 87% of the basin falls under intermediate flood susceptibility zone, while 10% is classified under high flood hazard zone. Areas in the lower elevation catchment, particularly those with flat topography near the coastline, were identified as being highly susceptible to flooding. The impacts of severe flooding in the basin are most pronounced in specific blocks in Jajpur district (Jajpur, Dasarathpur, and Korei blocks), Bhadrak district (Dhamnagar and Bhandaripokhari blocks), and Anandapur block in the Keonjhar district of Odisha. Additionally, changes in land use/land cover during 1995-2020 revealed 8% decline in agricultural land and 13.93% increase in fallow land in the area. The outcomes of this research can help researchers, planners and managers in managing natural resources, find locations that could be vulnerable to flooding, and lessen the damage.

A Novel Hybrid Deep-Learning Approach for Flood-Susceptibility Mapping

Flood-susceptibility mapping (FSM) is crucial for effective flood prediction and disaster prevention. Traditional methods of modeling flood vulnerability, such as the Analytical Hierarchy Process (AHP), require weights defined by experts, while machine-learning and deep-learning approaches require extensive datasets. Remote sensing is also limited by the availability of images and weather conditions. We propose a new hybrid strategy integrating deep learning with the HEC–HMS and HEC–RAS physical models to overcome these challenges. In this study, we introduce a Weighted Residual U-Net (W-Res-U-Net) model based on the target of the HEC–HMS and RAS physical simulation without disregarding ground truth points by using two loss functions simultaneously. The W-Res-U-Net was trained on eight sub-basins and tested on five others, demonstrating superior performance with a sensitivity of 71.16%, specificity of 91.14%, and area under the curve (AUC) of 92.95% when validated against physical simulations, as well as a sensitivity of 88.89%, specificity of 93.07%, and AUC of 95.87% when validated against ground truth points. Incorporating a “Sigmoid Focal Loss” function and a dual-loss function improved the realism and performance of the model, achieving higher sensitivity, specificity, and AUC than HEC–RAS alone. This hybrid approach significantly enhances the FSM model, especially with limited real-world data.

Dynamic vulnerability assessment framework for urban flood disasters considering real-time population changes

ABSTRACT Flood vulnerability assessment is an important means to cope with urban flood disasters. However, urban population mobility and land-use changes bring significant uncertainty to vulnerability assessments. In order to respond to the refined and dynamic needs of urban flood disaster prevention and control, a multi-scale and multi-categorized urban flood dynamic vulnerability assessment framework that considers real-time population changes was proposed in this study. The framework used the comprehensive function of exposure, sensitivity, and coping ability under population dynamic changes to characterize urban flood dynamic vulnerability, and constructed urban flood disaster vulnerability assessment models at both grid and regional scales. The results show that population mobility has a significant impact on the vulnerability of urban flood disasters. The vulnerability of flood disasters in Zhengzhou City is highest at 8:00 and 18:00, and high-vulnerability areas are mainly concentrated in residential areas in the central part of the city and main transportation channels. From 10:00 to 15:00, the high-vulnerability areas migrate to public service areas in the eastern part of Zhengzhou City. In the evening, the vulnerability degree of flood disasters is relatively low. The research results can provide a theoretical basis for urban flood managers to prevent and control flood disasters.

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.

Mapping Agricultural Land Potential Based on the Geographic Information System (GIS) in Muna Regency, Southeast Sulawesi Province

This research aims to determine the potential of agricultural land in Muna Regency based on parameters, namely level of slope, lithology, soil type, rainfall, and level of flood vulnerability. This research was carried out in Muna Regency, Southeast Sulawesi Province, for four months, from February to May 2023. The data collected consisted of secondary data obtained from agencies related to this research and primary data obtained through surveys and direct observation by taking coordinate points for each parameter observed with the help of GPS. The data analysis technique uses overlay analysis, namely overlapping land potential index parameter maps using ArcMap software. The research results show that the area of agricultural land in Muna Regency is 205,769 hectares, distributed over three land potential index classes, namely, the medium land potential class with 50,538 hectares, or 24.6%. second: low land potential class covering 149,345 hectares, or 72.6%; and third: very low land potential class covering 5,886 hectares, or 2.9%. Based on this percentage data, the potential for land for agricultural development in Muna Regency is in the medium potential category. Thus, the actions or solutions needed to increase agricultural productivity are (a) spatial planning of conservation farming systems; (b) development of crop and livestock integration; and (c) location-specific commodity selection.