Flood-prone Zones Research Articles

Evaluating Regional Variability in Road Closure Outcomes Due to Rainfall: a Logistic Regression Approach

Abstract. This study investigated the probability of road closure due to flooding. Logistic regression model was developed using the road closure data and the daily rainfall data for Houston, TX, USA during 2017 and 2018. The road network was further divided into flood prone zones. The spatial analysis revealed that the rainfall at the road segment level could be sufficiently represented by that recorded by nearest sensors. Within a 4 d window, the rainfall in the current day and 3 d prior played a more influential role in predicting road closure. The differential outcomes due to distinct regional features were explained. Finally, a watershed delineation approach substantially improved the model's predictive power and sensitivity.

Assessment of flash flood risks in the desert cities: a case study on Sabah Al-Ahmad, Kuwait

ABSTRACT Flash floods from rainstorms pose a severe natural hazard, especially dangerous in arid regions. This has occurred multiple times in Kuwait: in 1967, 1997, 2009, 2013, 2018, and 2020. For example, about 300 mm of rain beset the State of Kuwait on the 14th and 15th of November 2018. Subsequently, Sabah Al-Ahmad City was declared an environmental disaster zone, grappling with extensive water accumulation in its roads, urban zones, and surrounding valleys. This study focuses on (i) monitoring the hydro-geomorphological impacts of the floods on Sabah Al-Ahmad City and (ii) assessing the strategic plan to diminish flood risks, aiming to prevent a recurrence akin to the catastrophic events during rainstorms. (iii) proposing a protection plan for the city. The research employs various methodologies, including drones (UAV), field surveys, hydrological modelling, digital elevation model analysis, and spatial analysis of multi-spectral satellite imagery. These methods are instrumental in comprehending how flash floods impact the sustainable development of urban areas in Kuwait. Our approach is designed to support decision-makers and urban planners, enabling them to proactively identify flood-prone zones before future urban development initiatives in Kuwait.

Towards equitable infrastructure asset management: Scour maintenance strategy for aging bridge systems in flood-prone zones using deep reinforcement learning

Bridges play a critical role in transportation networks; however, they are vulnerable to deterioration, aging, and degradation, especially in the face of climate change and extreme weather events such as floodings. Furthermore, bridges can significantly affect social vulnerability; their damage or destruction can isolate communities, inhibit emergency responses, and disrupt essential services. Maintaining critical bridges in a cost-effective and sustainable manner is crucial to ensure their longevity and protect vulnerable communities. To address the maintenance optimization problem of bridge systems considering the effects of time deterioration, flood degradation, and social vulnerability, this study proposes a deep reinforcement learning algorithm to optimally allocate resources to bridges that are at expected cost of failure due to scour. The algorithm considers the effects of flood degradation with different return periods and is trained using a Markov Decision Process as the environment. The study conducts four flood simulation scenarios using Geographic Information System data. The findings suggest that the deep reinforcement learning algorithm proposes a sequence of repair actions that outperforms the status quo, currently employed by bridge managers. The significance of this study lies in its valuable insights for cities worldwide on how to effectively optimize their limited resources for the maintenance and rehabilitation of critical infrastructure systems to decrease portfolio cost and increase social equity.

‘We live with the floods’ – Intersectional Risk Theory in the analysis of household’s flood risk in Monterrey, Mexico

This contribution presents Intersectional Risk Theory as a suitable methodological approach for a group-centred qualitative analysis of semi-structured interviews with inhabitants of three neighbourhoods in a flood-prone zone in Monterrey, Mexico. Together with place attachment, the notions of Doing, Undoing and Re-Doing risk are useful to interpret daily risk practices at various scales in order to cope, adapt, and survive to hydro-meteorological hazards, allowing to situate individual agency as well as forms of community organization and resistance to face flood and other risks.

Climate change awareness and information utilisation and dissemination in rural areas of Nsanje district

Background: Climate information communication is the heart of climate service delivery which provide knowledge to help guide individuals and other stakeholders to make climate smart decisions. Therefore, this study explored awareness, utilization and dissemination of climate information in rural areas of Nsanje district. Methods: The study used both quantitative and qualitative research design that does not involve the designing of an experiment. This focuses much on the descriptive research design. The advantage of using this research design in this study was that the participants’ accuracy was clearly depicted. Finding: The results showed that 63 percent of the people in rural areas access climate information through radio and 33 percent do not use it at all. Multinomial logistic regression indicate that floods and crop pests are significant predictors of the location of an individual whether lives in flood prone area or not with p-values of 0.02 and 0.04 respectively, and this shows that people understand climate change based on the impacts felt. This also shows that crop pests are more prevalent in flood prone zones. Conclusion: The study concluded that there is a need to adopt climate communication channels that are more interactive and recruit more extension workers who are agents to promote the use of climate information. Novelty/Originality of this article: This study proposes developing a community-based interactive climate communication model that integrates mobile technology and local extension worker networks to improve the accessibility and understanding of climate information in rural areas.

Satellite mapping and demarcation analysis for coastal regulation zones assessment

Rapid urbanization and expanding tourism have led to widespread encroachments, particularly in flood-prone areas, where land degradation is more likely. Conventional methods of on-foot inspections for detecting these encroachments are intricate and time-consuming. A scarcity of data has resulted in undocumented violations, prompting the need for advanced technologies like satellite remote sensing. The current paper focuses on demonstrating the effectiveness of satellite remote sensing in detecting large-scale encroachments near flood-prone zones. Due to frequent undetected illegal constructions, Coastal Regulation Zones (CRZ) are a specific area of interest. This study employs a well-established method of analysis acquired from an extensive literature review to investigate illegal construction and encroachments via remote sensing advances. The research advocates for a practical analysis to combat illegal constructions in CRZs, presenting a comprehensive database through a concise portal for easy accessibility and analysis. The case study in Kerala, India, a region prone to natural disasters like monsoon floods, provides crucial data for measuring the boundaries required to preserve the ecosystem. The research underscores the significance of satellite remote sensing in efficiently addressing environmental threats posed by encroachments, offering insights applicable to regions facing similar challenges. This analysis has proven effective in diverse global settings and offers a standardized approach for demarking CRZ throughout the globe. Its adaptability and reliability across different terrain types and climatic conditions underscore its potential as a valuable tool in mapping encroachments. The current study contributes to the discourse on sustainable urban development, aiming to inform policies promoting resilience and ecological consciousness in rapid urbanization and tourism expansion.

Mapping of Flood Prone Areas in West Lombok Based on Analytic Hierarchy Process and Geographic Information System

The development and management of flood-prone information systems is needed by many parties, especially the public. In this research, the Analytic Hierarchy Process (AHP) presents an approach to detecting flood-prone areas by combining it with a Geographic Information System (GIS). AHP analysis is used to determine the weight and value of each assessment criterion, and then the results are integrated into the GIS system to produce a flood forecast map. The results obtained show that mapping of flood-prone zones can be carried out rationally and consistently based on AHP by considering four criteria, namely land use, soil type, slope, and rainfall. The results of the analysis of the zoning map show that the areas with a high level of flood vulnerability are on the north and west sides. The area around the Lembar port has a high level of vulnerability. Eyat Mayang Village also has a high level of vulnerability. Locations with a high level of flood vulnerability, caused by flooding through rivers located not far from the sea estuary.

From data to decisions: evaluating flood vulnerability in the Sindh watershed through Analytical Hierarchy Process

Floods are recurrent global catastrophes causing substantial disruptions to human life, extensive land degradation, and economic losses. This study aims to identify flood-triggering watershed features and employ a Multi-Criteria Decision-Making (MCDM) approach based on the Analytical Hierarchy Process (AHP) model to delineate flood-prone zones. Weights for various flood-influencing factors (slope, rainfall, drainage density, land-use/land-cover, geology, elevation, and soil) were derived using a 7 × 7 AHP decision matrix, reflecting their relative importance. A Consistency Ratio (CR) of 0.089 (within acceptable limits) confirms the validity of the assigned weights. The analysis identified approximately 128.51 km2 as highly vulnerable to flooding, particularly encompassing the entire stretch of riverbanks within the watershed. Historically, snow avalanches and flash floods have been the primary water-related disasters in the region, posing significant threats to critical infrastructure. In this context, this model-based approach facilitates the proactive identification of susceptible areas, thereby promoting improved flood risk mitigation and response strategies.

A Novel GIS-SWMM-ABM Approach for Flood Risk Assessment in Data-Scarce Urban Drainage Systems

Urbanization and climate change pose a critical challenge to stormwater management, particularly in rapidly developing cities. These cities experience increasingly impervious surfaces and more intense rainfall events. This study investigates the effectiveness of the existing drainage system in Lahore, Pakistan, a megacity challenged by rapid urbanization and the impacts of climate change. To address the lack of predefined storm patterns and limited historical rainfall records, we employed a well-established yet adaptable methodology. This methodology utilizes the log-Pearson type III (LPT-III) distribution and alternating block method (ABM) to create design hyetographs for various return periods. This study applied the stormwater management model (SWMM) to a representative community of 2.71 km2 to assess its drainage system capacity. Additionally, geographic information systems (GISs) were used for spatial analysis of flood risk mapping to identify flood-prone zones. The results indicate that the current drainage system, designed for a 2-year return period, is inadequate. For example, a 2-year storm produced a total flood volume of 0.07 million gallons, inundating approximately 60% of the study area. This study identified flood risk zones and highlighted the limitations of the system in handling future, more intense rainfall events. This study emphasizes the urgent need for infrastructure improvements to handle increased runoff volumes such as the integration of low-impact development practices. These nature-based solutions enhance infiltration, reduce runoff, and improve water quality, offering a sustainable approach to mitigating flood risks. Importantly, this study demonstrates that integrating LPT-III and ABM provides a robust and adaptable methodology for flood risk assessment. This approach is particularly effective in developing countries where data scarcity and diverse rainfall patterns may hinder traditional storm modeling techniques. Our findings reveal that the current drainage system is overwhelmed, with a 2-year storm exceeding its capacity resulting in extensive flooding, affecting over half of the area. The application of LPT-III and ABM improved the flood risk assessment by enabling the creation of more realistic design hyetographs for data-scarce regions, leading to more accurate identification of flood-prone areas.

Time traveling through the floodscape: assessing the spatial and temporal probability of floods and susceptibility zones in the Lower Damodar Basin.

The flood of Damodar river is a well-known fact which is used to the whole riverine society of the basin as well as to the eastern India. The study aims to estimate the spatio-temporal probability of floods and identify susceptible zones in the Lower Damodar Basin (LDB). A flood frequency analysis around 90years hydrological series is performed using the Log-Pearson Type III model. The frequency ratio model has also been applied to determine the spatial context of flood. This reveals the extent to which the LDB could be inundated in response to peak discharge conditions, especially during the monsoon season. The findings indicate that 36.64% of the LDB falls under high to very high flood susceptibility categories, revealing an increasing downstream flood vulnerability trend. Hydro-geomorphic factors substantially contribute to the susceptibility of the LDB to high magnitude floods. A significant shift in flood recurrence intervals, from biennial occurrences in the pre-dam period to decadal or vicennial occurrences in the post-dam period, is observed. Despite a reduction in high-magnitude flood incidents due to dam and barrage construction, irregular flood events persist. The effect of flood in the LDB region is considered to be either positive as well as negative in terms of wholistic sense and impact. The analytical results of this research could serve to identify flood-prone zones and guide the development of flood resilience policies, thereby promoting sustainability within the LDB floodplain.

Climate Change-Related Drivers of Migration in East Africa: An Integrative Review of the Literature

Despite the growing academic concern and policy interest in climate change drivers of migration, scientific evidence on the topic remains contested, with divergent claims of positive and negative contributions of climate change on human migration and displacement. This review seeks to assess the climate change-related drivers of migration with a focus on three East African countries: Tanzania, Kenya, and Uganda. They are among the most vulnerable countries in Africa, with one of the most mobile populations. The study used the available literature on climate change and migration as the data source. About 22 research articles on climate change and migration were sourced from the Web of Science and Scopus databases and critically reviewed. The review generally indicates that climate change effects such as frequent drought and flood disasters, livestock pests, and crop diseases influence migration and displacement in the region. On the other hand, rising sea levels pose imminent risks that can cause migration if adaptation measures are not taken. The review has also revealed that responses to drought or livestock and crop diseases, be it localized adjustment or out-migration, are determined by households' adaptive capacity. For rapid onset climate events such as floods, the most vulnerable are the less fortunate living in poorly constructed structures in flood-prone zones. This review proposes strengthening the climate policy framework by mainstreaming climate change migration in East African nations’ adaptation plans. It also calls for promoting alternative livelihood activities as an adaptation strategy in areas prone to climate change to lessen human-forced migration and displacement.

Efficiency Evaluation of Surface Water Collection Infrastructure during Floods: Information Analysis and Zoning Management

Objective: Flood events, occurring as natural and unexpected phenomena, have become increasingly prevalent in recent decades. Assessing the probability of flood risk and creating flood zoning maps in vulnerable areas, particularly urban regions, are crucial for mitigating flood damages and managing such events. This research aims to determine the flood-prone zones along the Darakeh River, located in Tehran, the capital city of Iran, by integrating the hydraulic model HEC-RAS with Geographic Information System (GIS). Methods: After establishing the physiographic characteristics of the watershed, including area, perimeter, elevation, slope, and time of concentration, influential factors on flood occurrences such as permeability, hydrological soil groups, runoff potential, curve number, land use, and vegetation cover were identified. This information, along with other hydraulic data and flow specifications such as boundary conditions, roughness coefficients, and design discharge, was incorporated into the HEC-RAS model. The model was executed for different return periods of 2, 5, 10, 25, 50, and 100 years. Subsequently, the hydraulic model outputs were transferred to the GIS model to obtain the flood-prone zones along the Darakeh River for the aforementioned return periods. Results: As observed, in the middle section of the Khoshke Channel, which has a lower gradient, the floodplain exhibits a wider extent. Furthermore, along the course of the river and after passing through a stretch of the Khoshke Channel with a reduced gradient, the floodplain's area increases up to the location of the western flood channel. Conclusion: It is concluded that areas with lower river slopes exhibited larger flood-prone zones, highlighting the necessity of increased attention to prevent significant damages in urban areas during flood events.

Unveiling and modelling the flood risk and multidimensional poverty determinants using geospatial multi-criteria approach: Evidence from Jigawa, Nigeria

Floods are recurring natural disasters that pose major challenges to communities worldwide, especially in regions where poverty is widespread. This research focuses on analysing the relationship between flood risk and poverty factors in Jigawa. It uses geospatial data, historical flood records and remote sensing to locate flood-prone areas. The integration of these data sets creates a comprehensive model for measuring flood-prone zones and their impact on multidimensional poverty. The flood risk zone included 3% and 13% as extremely high and high. In addition, 40% of the population is particularly vulnerable to flood disasters, leading to multidimensional poverty in the region. At the same time, poverty determinants such as agricultural activities, income level, education, access to health care, housing conditions, and road networks were analysed using statistical methods of principal component analysis (PCA). Findings from this research will go a long way toward understanding the complex interactions between natural disasters (flood) and poverty and will shed light on the mechanisms that perpetuate vulnerability in impoverished regions. By identifying the specific determinants of poverty that exacerbate the impact of flooding, this study provides evidence-based insights into possible avenues for targeted interventions and risk-reduction strategies. In addition, this study underscores the importance of using geospatial technology for disaster risk management and poverty alleviation. These findings will help policymakers and local authorities implement more efficient and adaptive disaster preparedness and response policies, thereby building the resilience of communities facing the intersecting challenges of poverty and flood risk.

Simulation of the Hydraulic Model HEC-RAS Coupled with GIS and Remote Sensing to Study the Effect of River Cross-section Width in Detecting Flood-prone Areas

Abstract Flooding is one of the extreme hydrological phenomena. It is a recurring natural disaster that causes loss of life and property in many parts of the world, particularly during the monsoon season. It is important to address such issues for local government and policymakers to manage the flood properly. One such flood management activity is to develop a flood-prone area that depicts the spatial and temporal extent of flood accurately. The integration of the Hydrologic Engineering Centre - River Analysis System (HEC-RAS) model and geospatial tools have emerged as a crucial approach for identifying and mapping flood-prone areas. The successful application of the HEC-RAS model generally depends on the topographical data, which represents the channel and floodplain geometry. In floodplain geometry, discrete cross-sections play a vital role to develop the floodplain map, particularly in the flat topographical region. To extract these data it needs a high-quality digital elevation model (DEM), such as light detection and ranging (LiDAR). However, due to a lack of high-resolution topographical data, flood hazard mapping in developing countries is rare. In common practice, the centerline of the river is considered the flow path for the channel. The orientation of the cross-sections is perpendicular to this line and extends to reach the limits of the floodplain. But, it is difficult to define the limits and it may depend on the magnitude of the flood. Hence, in this study, the HEC RAS model coupled with ArcGIS has been applied to the Ganga River, which traverses through the Bihar state of India to study the effect of cross-sectional width to define the floodplain. The Bihar state is facing substantial hardships from annual flooding events with approximately 16.5% of India’s flood-prone area. The extreme flood values for 5, 10, and 25 years of return period have been determined and the influence of the three different cross-sectional widths to mapping the floodplain has been investigated. This novel perspective adds dimension to the understanding of flood dynamics and its implications for flood risk assessment. In this analysis, it has been observed that, with an increase in the width of the cross-section, the floodplain area also getting increased. In this topographical region, keeping a fixed flow path will underestimate the flood-prone area. The width of the flow path depends on the topography of the region and the river flow. The outcomes of this analysis provide valuable insights into the flood-inundated areas for the specified return periods, enabling the identification and prioritization of flood-prone zones.

Pluvial Flood Susceptibility in the Local Community of the City of Gospić (Croatia)

Pluvial flooding (PF), resulting from intense short-duration rainfall events, is challenging in urban areas amidst climate change and rapid urbanization. Identifying flood-prone zones and implementing collaborative mitigation strategies with the local population are crucial aspects of PF management. This study aims to enhance the understanding of urban PF in Croatia by collecting historical PF data, creating the GIS-MCDA susceptibility model, and conducting a risk perception survey for the study area of Gospić. Susceptibility zones were generated utilizing topographical, environmental, and hydrological criteria using the AHP method. To examine the risk perception, a face-to-face survey was conducted among 5% of the city’s population (N = 64). Five factors were defined: (F1) risk awareness, (F2) anthropogenic and (F3) natural causes of PF, (F4) potential consequences, and (F5) preparedness. The reliability of the questionnaire was very high (>0.71). Most respondents believe they are ill-equipped to defend against flooding independently and express a lack of confidence in the measures taken by local authorities. The highly susceptible zones encompass not only agricultural areas but also residential zones of city. Among all respondents, 36% live in a flood-prone area and half of them have no flood insurance or other mitigation measures. Incorporating locals’ suggestions and problems, mitigation measures were proposed. Results from this research can be a starting point for further research in Croatia and can provide guidelines for decision-makers in implementing a risk mitigation strategy.

Differential synthetic aperture radar (SAR) interferometry for detection land subsidence in Derna City, Libya

Abstract The country of Libya, situated on the Mediterranean fault zone, has a distinctive geodynamic regime due to the interplay between the Eurasian and African plates, which governs its tectonic evolution. In addition to its seismological significance, Libya is characterized by numerous subsidence and slope instabilities in regions with steep terrain. These geological phenomena have significant consequences for the built environment, as they pose an immediate danger to entire towns and essential infrastructure. Furthermore, infrequent weather phenomena, such as intense precipitation and thunderstorms, when coupled with the geological characteristics of some regions and the presence of seismically active terrain, have the potential to trigger landslide and land subsidence, resulting in significant harm to vital infrastructure. The current study utilizes the DInSAR technology to identify distinct subsidence occurrences that were induced by intense precipitation in coastal regions of Libya, specifically in Derna. These areas experienced significant flooding resulting in collapses during September 2023. A total of six pairs of co-event Interferometric Synthetic Aperture Radar (SAR) were utilized to generate displacement maps in the vertical, north-east, and north-west directions for the purpose of analysing the deformations. The aforementioned activities are conducted via Sentinel-1A images, which is freely accessible through the Copernicus program. Additionally, flood-prone zones were defined using Sentinel-1 GRD imagery. The Interferometric processing revealed multiple areas of subsidence. Subsidence rates of up to −14 cm were found in Derna city’s urban cores after flood. The findings suggest that subsidence may have an effect on the flood-proneness of the region of Derna City as Ground subsidence also occurred in the period immediately before the earthquake, at a rate of −14 cm.

Flood susceptibility assessment of the Agartala Urban Watershed, India, using Machine Learning Algorithm.

Frequent floods are a severe threat to the well-being of people the world over. This is particularly severe in developing countries like India where tropical monsoon climate prevails. Recently, flood hazard susceptibility mapping has become a popular tool to mitigate the effects of this threat. Therefore, the present study utilized four distinctive Machine Learning algorithms i.e., K-Nearest Neighbor, Decision Tree, Naive Bayes, and Random Forest to estimate flood susceptibility zones in the Agartala Urban Watershed of Tripura, India. The latter experiences debilitating floods during the monsoon season. A multicollinearity test was conducted to examine the collinearity of the chosen flood conditioning factors, and it was seen that none of the factors were compromised by multicollinearity. Results showed that around three-fourths of the AUW area was classified as moderate to very high flood-prone zones, while over 20 percent was between low and very low flood-prone zones. The models applied performed well with ROC-AUC scores greater than 70 percent and MAE, MSE, and RMSE scores less than 30 percent. DT and RF algorithms were suggested for places with similar physical characteristics based on their outstanding performance and the training datasets. The study provides valuable insights to policymakers, administrative authorities, and local stakeholders to cope with floods and enhance flood prevention measures as a climate change adaptation strategy in the AUW.

Assessment of the Potential Flood Hazard of the Larbaâ Wadi, Rural Center of Sebt Boukellal, Taza, Morocco

Flood risk management often requires the use of geomorphological features to identify flood zones, and the use of hydraulic models to predict inundation dynamics and related impacts on the surrounding area. In this study, we used a hydraulic river simulation model to identify potential flood-prone zones on a small scale. It concentrated on a 2.5-kilometer section of the Larbaâ Wadi, which crosses the rural center of Sebt Boukellal. For estimating the peak discharge that occurs in the return periods of 10, 20, 50, and 100 years of the drainage area, we used the Rational method. Standard tables to estimate Manning’s coefficient and direct field measurements to feed the model. Model simulation has shown stability of the steady state, which witnesses the accuracy of the estimated and measured characteristics of the river system. During the calibration phase, we compared the model outputs to the observed floods and made adjustments to align the simulation with the field observations. Indeed, the 50-year flood remarkably matched the extent of the flood that occurred on September 27, 2000. The obtained results have shown that even for a 10year return period, the overflow affects properties within the floodplain. The 100-year flood exceeded the river’s capacity, causing water to spill onto the rural center’s streets and cultivated fields. The water level reached an elevation of 552.14 meters at Sebt Boukellal’s marketplace. These results were consistent with recent floods and confirm previous observations, indicating that the model precisely predicted the river’s behavior. The findings have shown that floods spanned large regions and suggested urgent intervention to protect lives and properties.

Urban flood hazard assessment in Awka metropolis using multi-criteria decision process

Urban flooding is an escalating global threat, and Awka Metropolis in Anambra state Nigeria is no exception. The city's rapid urbanization and inadequate infrastructure have left it increasingly vulnerable to flooding, exacerbated by the impacts of climate change. Anticipated flood effects include disruption of daily life, infrastructure damage, and threats to public safety, necessitating urgent attention. This study presents a comprehensive assessment of urban flood hazards in Awka Metropolis, utilizing an integrated approach combining the Analytical Hierarchy Process (AHP) and Flood Hazard Index (FHI) to gauge the city's flood vulnerability. The primary objectives were to identify key geophysical factors contributing to flooding, categorize these factors by their vulnerability, determine their reliability indices, delineate flood-prone zones, and generate a spatial flood extent map. The methodology encompassed data acquisition, pre-processing, Flood Modeling (evaluating slope, Elevation, Drainage Network, and flow accumulation), the application of Analytical Hierarchy Process, and the utilization of the Flood Hazard Index. The results classified Awka Metropolis into three flood risk zones: high (837.84 hectares, 14.70%), moderate (2182.65 hectares, 38.30%), and low (2678.45 hectares, 46.99%). These findings provide indispensable insights into the distribution of flood hazards across the city, enabling urban planners and authorities to strategically allocate resources, prioritize mitigation initiatives, and formulate targeted flood risk reduction strategies. Importantly, the incorporation of stakeholder input enhances the study's practical relevance and applicability in real-world urban planning and disaster management scenarios. In conclusion, this research offers a robust and multidimensional framework for assessing urban flood hazards in Awka Metropolis. The synergy of the Analytical Hierarchy Process and Flood Hazard Index equips urban planners, policymakers, and disaster management agencies to proactively tackle the mounting urban flooding challenges. The study's outcomes contribute to the development of sustainable flood risk reduction measures, ultimately enhancing the resilience of Awka Metropolis to future flood events.

Modelling on assessment of flood risk susceptibility at the Jia Bharali River basin in Eastern Himalayas by integrating multicollinearity tests and geospatial techniques

AbstractClimate change and anthropogenic factors have exacerbated flood risks in many regions across the globe, including the Himalayan foothill region in India. The Jia Bharali River basin, situated in this vulnerable area, frequently experiences high-magnitude floods, causing significant damage to the environment and local communities. Developing accurate and reliable flood susceptibility models is crucial for effective flood prevention, management, and adaptation strategies. In this study, we aimed to generate a comprehensive flood susceptibility zone model for the Jia Bharali catchment by integrating statistical methods with expert knowledge-based mathematical models. We applied four distinct models, including the Frequency Ratio model, Fuzzy Logic (FL) model, Multi-criteria Decision Making based Analytical Hierarchy Process model, and Fuzzy Analytical Hierarchy Process model, to evaluate the flood susceptibility of the basin. The results revealed that approximately one-third of the Jia Bharali basin area fell within moderate to very high flood-prone zones. In contrast, over 50% of the area was classified as low to very low flood-prone zones. The applied models demonstrated strong performance, with ROC-AUC scores exceeding 70% and MAE, MSE, and RMSE scores below 30%. FL and AHP were recommended for application among the models in areas with similar physiographic characteristics due to their exceptional performance and the training datasets. This study offers crucial insights for policymakers, regional administrative authorities, environmentalists, and engineers working in the Himalayan foothill region. By providing a robust flood susceptibility model, the research enhances flood prevention efforts and management, thereby serving as a vital climate change adaptation strategy for the Jia Bharali River basin and similar regions. The findings also have significant implications for disaster risk reduction and sustainable development in vulnerable areas, contributing to the global efforts towards achieving the United Nations' Sustainable Development Goals.