Flood Risk Research Articles

Geospatial Assessment of the Occurrence of Flooding In Informal Settlements in Ilala Municipality, Tanzania

This article assesses the occurrence of flooding in informal settlements of Ilala Municipality using quantitative and qualitative approaches. Non-probability sampling technique was used to select participants for participatory mapping. Data collection was done through participatory mapping, remote sensing, document review, and field observation. The weighted sum tool in ArcGIS was used to combine flood factors layers and create flood-prone area maps. Participatory mapping was used to map flood depth before data was converted for map visualization. Results indicated that areas with high to very high risk of flooding are generally decreasing over time. From 1990–2000 areas with high risk of flooding covered 31.1% of the study area, decreasing to 21.1% from 2001–2010, while from 2011–2020 it covered 21.6%. However, flood depths increased over time. From 1990–2000 the highest flood depth was 5ft decreasing to 4ft between 2011–2020. Hence, people living in informal settlements are more vulnerable and mostly affected by floods because of disposing of solid wastes into the river valley, which blocks drainage systems, thereby causing overflow of water. Therefore, regulations guiding human development near the river valleys should be improved and unlawful activities in the river valley be discouraged. Again, storm water drainage systems should be protected and well managed as the levels of storm water have become very high over time. Waste collection contracts should be given to private companies which are flexible and able to use alternative means of accessing unplanned streets to easily collect wastes.

Factor Analysis of Housing Choice in Flood Prone Areas: Focusing on The Effect of Place Attachment on Risk Perception

Coastal regions are particularly susceptible to the effects of climate change because of their geographical proximity to the sea. Despite this vulnerability, these areas continue to be perceived and utilized primarily as recreational spaces where individuals can own and enjoy scenic landscapes rather than as zones at risk from climatic threats. The paradoxical demand for coastal amenities in spite of the risk of flooding is investigated in this study by applying place attachment factors from a sociopsychological angle. We employed the theories of vulnerability, risk perception, and place attachment to build a conceptual framework and formulate hypotheses regarding their relationships. Our analysis, based on a structural equation model, showed that people accept these risks for the benefits of living near the coast because their perception of risk is increased by disaster exposure but lessened by place attachment. These findings highlight the urgent need for urban planning strategies that mitigate the risks associated with climate change.

Flood Risk Reduction: A Government and Community Collaboration Perspective in Urban Communities

Flooding is the responsibility of the government, so it is necessary to anticipate it together with the community through social participation. This paper aims to analyse the role of the government in anticipating floods and to analyse the forms of community social participation in anticipating floods. The research method used is mixed research, qualitative quantitative design (explanatory sequential). Data collection used participant observation techniques, in-depth interviews, questioners, and documentation studies. The data was analysed quantitatively then qualitative research was conducted and the results were verified and ended with a conclusion. The results of the research obtained (1) the role of the government, among others: (i) preparing an early warning system, (ii) preparing evacuation facilities and infrastructure, including evacuation routes, and (iii) forming structural and non-structural flood volunteer teams. (2) Forms of community social participation include: cleaning and increasing the depth of waterways such as sewers and canals, raising the concrete walls of waterways, raising the foundations of houses, and organising the residential environment. The conclusion of this research is that the government's role in flood mitigation in Makassar municipality focuses on things that prevent flooding. Forms of community social participation as a form of flood mitigation such as cleaning waterways, raising the walls of sewers and canals and so on.

Urban Flood Risk Impact on Road Accessibility and Socioeconomic Life in Egor Geographical Area of Edo State, Nigeria

Urban Flood Risk Impact on Road Accessibility and Socioeconomic Life in Egor Geographical Area of Edo State, Nigeria

The Effect of Flood Exposure on Insurance Adoption Among US Households

AbstractDespite increasing exposure to flooding and associated financial damages, estimates suggest more than two‐thirds of flood‐exposed properties are currently uninsured. This low adoption rate could undermine the climate resilience of communities and weaken the financial solvency of the United States National Flood Insurance Program. We study whether repeated exposure to flood events, especially the disaster‐scale floods that are expected to become more frequent in a warming climate, could spur insurance adoption. Using improved estimates of residential insurance take‐up in locations where such insurance is voluntary, and exploiting variation in the frequency and severity of flood events over time, we quantify how flood events impact local insurance demand. We find that a flood disaster declaration in a given year increases the take‐up rate of insurance by 7% in the following year, but that the effect diminishes in subsequent years and is gone after 5 years. This effect is more short‐lived in counties in inland states that do not border the Gulf and Atlantic coasts. We also find that the effect of a flood on take‐up is substantially larger if there was also a flood in the previous year, and that recent disasters are more salient for homeowners whose primary residences are exposed to a disaster declaration compared to non‐primary residences. Overall, these findings suggest that relying on households to self‐adapt to increasing flood risks in a changing climate is insufficient for closing the insurance protection gap.

Effective Storm Water Management in The Urban Area – A Case Study

Urbanization and climate change have negative effects on the changes of natural precipitation and runoff regime, which results with more frequent floods and landslides. Besides of the increased quantity of the stormwater that drainage channels have to collect, neg- ative consequences of urbanization are also evident through increased pollution of runoff stormwater. Stormwater has immense impacts on urban flooding and water quality, leaving the marginalized and the impoverished disproportionately impacted by and vulnerable to stormwater hazards. However, the environmental health concerns of socially and econom- ically marginalized individuals are largely underestimated. Therefore stormwater manage- ment should be adapted to these changes so that the negative effects of new hydrological conditions of precipitation and runoff and stormwater pollution in the urban areas are mitigated. By 2050 the world population will grow to about 9 billion contributing to deep changes in urban areas structure. This would increase the effect of water deficiency and along with projected climate changes the impact of urban flooding, urban heat islands or drought. Stormwater threats can be mitigated with the application of stormwater management meth- ods named differently such as ‘Sustainable (Urban) Drainage Systems’ (SUDS), ‘Low Impact Development’ (LID) or ‘Best Management Practices’ (BMPs). This method includes differ- ent techniques such as Green roof, Rain barrels and cisterns, Infiltration basin, Detention Ponds, Retention Ponds, Green walls, Porous pavements, Bioretention basins, Bioswales. The aim of the research includes a study of the effectiveness of urban stormwater manage- ment techniques for the case study area. The concept of urban stormwater management techniques refers to the ability to provide and manage this primary resource in quantitative and qualitative terms in order to satisfy the future needs of population. The storm wa- ter management techniques provides several benefits: mitigation of the urban heat island effects, reduction of air pollution, additionally, It induces important hydraulic advantages acting as an effective tool for reducing flood risk in urban area with runoff reduction, attenuation and delay of the peak flow. The results confirm that bioretention basin, Permeable pavement, Grassed swale, Infiltration basin are effective techniques for the reduction of total runoff volume of rainwater falling on their area.

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

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

Archetypes of local governance for flood risk reduction decision-making under uncertain climate change futures

Urban coastal communities are among the most vulnerable to flood hazards, with these threats set to increase under climate change. Knowledge of how uncertainty shapes passive or proactive local decision-making processes of climate adaptation remains sparse – with significant potential to inform resilient and sustainable futures. To understand these decision dynamics, we developed a serious game to simulate the local governance roles, resources, and assets involved in managing flood risk under uncertain climate scenarios. Using data on the actions of game workshops conducted in the Philippines, we identify three distinct archetypes of collective adaptation decision-making: (1) Hierarchical Alliances; (2) Passive Enthusiasts and (3) Deliberative Strategists. We then examine how these archetypes respond to situational, temporal, choice, and outcome uncertainties present in climate change scenarios, uncovering six strategies used to act, reduce, supress, and forestall uncertainties: (a) resource sharing; (b) coordination; (c) early action; (d) mobilising values; (e) transferring accountability; and (f) risk balancing. Our results show how different archetypes perform in adaptation decisions, offering new guidance on how to structure local governance mechanisms in flood planning under climate change impacts.

A Rate of Change and Center of Gravity Approach to Calculating Composite Indicator Thresholds: Moving from an Empirical to a Theoretical Perspective

A composite indicator (CI) is the mathematical aggregation of sub-dimension (local) indicators used to provide an overall score for the multidimensional concept being measured. CIs are widely used to assess the benefits or risks in human endeavors, such as by creating life satisfaction indices or disaster risk indicators. One important aspect of the development of CIs is setting up value thresholds for taking action, such as in determining the minimum acceptable level of life satisfaction in a community or the maximum acceptable flood risk value beyond which people should be ordered to evacuate from the area in danger. The analytic hierarchy/network process (AHNP) is widely used for the development of CIs. In a review of 111 AHP/ANP CI studies, fewer than 10% discussed any threshold. This means that about 90% of the developed CIs were theoretically sound but lacked the actionable thresholds necessary to be of practical use. Furthermore, for the few studies that set thresholds, the values were typically set arbitrarily or using inadequate statistical approaches. To address this important concern, this study first discusses the most commonly used approaches to setting up thresholds, as well as their inadequacies, and proposes the development of AHP/ANP CI thresholds using a mathematical approach based on the rate of change and center of gravity (RCCG) concepts. Using this approach, a virtual reference alternative, i.e., a threshold profile (TP) made up of the local thresholds of each indicator, is calculated. The key advantage of the proposed method is that it not only provides a non-arbitrary way to set up a CI threshold; more importantly, it is independent of the data and/or alternatives to be evaluated; that is, a threshold calculated with the proposed approach constitutes an absolute reference value, outside the dataset.

Geospatial Analysis of Flood Susceptibility in Nigeria’s Vulnerable Coastal States: A Detailed Assessment and Mitigation Strategy Proposal

This study employs advanced geospatial analytical techniques to evaluate the vulnerability of Nigeria’s coastal states and their constituent local government areas to flood hazards, which represent a critical and escalating risk within the coastal hazard paradigm intensified by climate change phenomena. The study’s objective is to utilize geospatial data to delineate and quantify the intensity and distribution of flood susceptibility, thus establishing a foundational framework for developing comprehensive disaster management strategies in response to the challenges posed by climate variability. The research uses satellite imagery and geographic information system (GIS)-based hydrological modeling to delineate regions susceptible to flooding, synthesizing topographical and hydrological data to stratify areas into discrete flood susceptibility categories. The findings indicate that the Delta coastal State of Nigeria contains extensive medium to high-risk flood zones spanning 8304.57 km2. While the Bayelsa coastal State of Nigeria presents critical areas at high to very high flood risk, encompassing 5506.61 km2 at high risk and 1826.88 km2 at very high risk, this highlights the urgent necessity for immediate and strategic mitigation measures. This research highlights the critical importance of geospatial technology in shaping disaster management and enhancing community resilience against increasing flood frequencies. As Nigeria’s coastal regions face escalating flood susceptibility, advanced geospatial methods are vital for assessing and mitigating these climate-induced threats, contributing to climate-resilient planning and aligning with Sustainable Development Goal 13: Climate Action. The study’s geospatial approach delivers precise flood risk evaluations and guides targeted mitigation efforts, marking significant progress in managing coastal hazards in a changing climate.

Observed beach nourishment development in a semi-enclosed coastal embayment

Beaches are important coastal features that provide vital ecosystem services; however, these systems are threatened by coastal erosion, sea-level rise, and coastal squeeze. Beach nourishments are a commonly applied coastal protection measure to mitigate erosion and flood risks while maintaining or enhancing recreational values. Nourishments vary in scale from mega-nourishments to small-scale nourishments, where the latter has typically been less studied due to the limited resources for monitoring. Meanwhile, with rising sea levels, the implementation of small-scale nourishments is expected to increase, and there is a need for more knowledge about the morphological evolution and technical lifetime of these interventions. In this study, the morphological responses of small-scale beach nourishment (total volume 20,000 m3 which amounts to 30 m3 added per m alongshore) are observed and quantified over various timescales, considering the initial adjustment, long-term development, and event-driven response. The investigated nourishment is implemented in a partly sheltered coastal embayment in the semi-enclosed Baltic Sea, which has a complex interaction between waves and water levels. Furthermore, the beach is surrounded by hard structures, a rock revetment at the back, and a harbor mole and a groin, influencing the longshore and cross-shore sediment transport. The results show that substantial reduction in subaerial volume can be attributed to specific events. In addition, we observed considerable spatial variation in the sediment re-distribution induced by hard structures and variability in the nearshore bathymetry. The lifetime of the beach nourishment is just over two years. The nourished material remains in the system at the end of the lifetime but is not available for beach recovery. Still, the added subaerial volume has eroded at the midsection, and the protective beach width has been reduced, leaving the rock revetment exposed with reduced protection for the hinterland. The energy conditions at the site are highly episodic, which impacts morphological evolution, and observations indicate that the development is event-driven.

Predicting peak inundation depths with a physics informed machine learning model

Timely, accurate, and reliable information is essential for decision-makers, emergency managers, and infrastructure operators during flood events. This study demonstrates that a proposed machine learning model, MaxFloodCast, trained on physics-based hydrodynamic simulations in Harris County, offers efficient and interpretable flood inundation depth predictions. Achieving an average R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} of 0.949 and a Root Mean Square Error of 0.61 ft (0.19 m) on unseen data, it proves reliable in forecasting peak flood inundation depths. Validated against Hurricane Harvey and Tropical Storm Imelda, MaxFloodCast shows the potential in supporting near-time floodplain management and emergency operations. The model’s interpretability aids decision-makers in offering critical information to inform flood mitigation strategies, to prioritize areas with critical facilities and to examine how rainfall in other watersheds influences flood exposure in one area. The MaxFloodCast model enables accurate and interpretable inundation depth predictions while significantly reducing computational time, thereby supporting emergency response efforts and flood risk management more effectively.

The permanent flood risk of culverts and the impact of increasing debris blockage

The permanent flood risk of culverts and the impact of increasing debris blockage

Assessing dynamic congestion risks of flood-disrupted transportation network systems through time-variant topological analysis and traffic demand dynamics

Amid escalating climate change and extreme weather events, urban road networks face increasingly severe flood risks. Current congestion risk analyses on transportation systems, largely grounded in static network characteristics, fall short of capturing the dynamic impacts of evolving flood events on network topology. A notable gap in integrating dynamic flood models with detailed temporal network property measurements impedes a deep understanding of the cascading mechanisms of traffic disruptions caused by floods. The research introduces an innovative framework for assessing dynamic congestion risks within transportation networks under flooding conditions. This framework comprises four distinct layers. The Data and Context layer collates and contextualizes primary data, encompassing road networks, commuter behaviour patterns and hydrological risks. This layer establishes a baseline for understanding the structure of the transportation system and its environmental vulnerabilities. The Computation layer focuses on calculating context-based betweenness centrality values to capture the importance of road segments within the network, setting a static indicator. Concurrently, this layer examines the dynamic exposure flag under flood conditions, providing a dynamic indicator that accounts for the fluctuating susceptibility of road networks to flooding. The Integration layer bridges the topological analysis and the functional performance of the transportation system. It creates a dynamic series that describes the topological properties of the network, reflecting its vulnerability to disruptions. Simultaneously, the time-variant traffic demand series are tracked to capture the system’s operational response to flood-induced conditions. Finally, the Evaluation layer synthesizes data and analyses from the previous modules. This synthesis involves a novel dual assessment: generating a time-variant vulnerability index series and evaluating the time-variant congestion risk map on the transportation system under flooding. The London case study is developed based on this proposed framework to identify critical areas of congestion risks. This study offers valuable data-driven insights for urban planning and disaster management, aiding policymakers, planners and emergency teams to enhance urban resilience against flood-induced transport disruptions.

Deep Neural Network-Based Flood Monitoring System Fusing RGB and LWIR Cameras for Embedded IoT Edge Devices

Floods are among the most common disasters, causing loss of life and enormous damage to private property and public infrastructure. Monitoring systems that detect and predict floods help respond quickly in the pre-disaster phase to prevent and mitigate flood risk and damages. Thus, this paper presents a deep neural network (DNN)-based real-time flood monitoring system for embedded Internet of Things (IoT) edge devices. The proposed system fuses long-wave infrared (LWIR) and RGB cameras to overcome a critical drawback of conventional RGB camera-based systems: severe performance deterioration at night. This system recognizes areas occupied by water using a DNN-based semantic segmentation network, whose input is a combination of RGB and LWIR images. Flood warning levels are predicted based on the water occupancy ratio calculated by the water segmentation result. The warning information is delivered to authorized personnel via a mobile message service. For real-time edge computing, the heavy semantic segmentation network is simplified by removing unimportant channels while maintaining performance by utilizing the network slimming technique. Experiments were conducted based on the dataset acquired from the sensor module with RGB and LWIR cameras installed in a flood-prone area. The results revealed that the proposed system successfully conducts water segmentation and correctly sends flood warning messages in both daytime and nighttime. Furthermore, all of the algorithms in this system were embedded on an embedded IoT edge device with a Qualcomm QCS610 System on Chip (SoC) and operated in real time.

Natural Hazards and Their Environmental Impact: Flood Risks in the Systemic Management of Non-Hazardous Municipal Waste

Natural Hazards and Their Environmental Impact: Flood Risks in the Systemic Management of Non-Hazardous Municipal Waste

Optimizing flood predictions by integrating LSTM and physical-based models with mixed historical and simulated data

The current flood forecasting models heavily rely on historical measured data, which is often insufficient for robust predictions due to practical challenges such as high measurement costs and data scarcity. This study introduces a novel hybrid approach that synergistically combines the outputs of traditional physical-based models with historical data to train Long Short-Term Memory (LSTM) networks. Specifically, the NAM hydrological model and the HD hydraulic model are employed to simulate flood processes. Focusing on the Jinhua basin, a typical plains river area in China, this research evaluates the efficacy of LSTM models trained on measured, mixed, and simulated datasets. The LSTM architecture includes multiple layers, with optimized hyperparameters tailored for flood forecasting. Key performance indicators such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Peak-relative Error (PRE) are employed to assess the predictive accuracy of the models. The findings demonstrate that LSTM models trained on mixed datasets with a simulated-to-measured data ratio of less than 2:1 consistently achieve superior performance, exhibiting significantly lower RMSE and MAE values compared to models trained on mixed data with larger data ratios. This highlights the advantage of integrating measured and simulated data, leveraging the strengths of both data types to enhance model accuracy. Despite its advantages, the approach has limitations, including dependence on the quality of simulated data and potential computational complexity. However, the development of this hybrid model marks a significant advancement in flood forecasting, offering a promising solution to the challenges of computational efficiency and data scarcity. Potential applications of this approach include real-time flood prediction and risk management in other flood-prone regions, providing a robust framework for integrating diverse data sources to improve forecasting accuracy.

Community Participation in Reducing Flood Disaster Risk in Boyantongo Village, South Parigi Sub-district, Parigi Moutong District

Boyantongo Village is one of the villages in the South Parigi Sub-district, Parigi Moutong Regency, which has experienced flooding several times during the rainy season. Flash floods that hit Boyantongo Village occurred on August 25, 2012, causing 142 residents of Boyantongo Village to flee Flash floods in Boyantongo Village occurred again on July 11-13, 2020 flash floods occurred at 21.00 WITA causing 18 Boyantongo Village residents to flee. The purpose of this study was to determine the level of community participation in reducing the risk of flood disasters in Boyantongo Village. The study used a sample survey method, survey research is one research approach that is generally used for extensive data collection. At the same time, the data collection techniques used in this study used instruments in the form of questionnaires. The research was conducted by distributing questionnaires to 67 households. The results showed that the potential for flooding is in Hamlet I, Hamlet III, and Hamlet IV. Aspects of community understanding of basic disaster knowledge, from Hamlet I is categorized as quite good, Hamlet III is categorized as good and Hamlet is categorized as quite good. Community knowledge in disaster mitigation during floods is categorized as low. It shows that the community's knowledge of disasters is not good, as seen from the parameters of knowledge and attitudes towards disaster risk. This is because the community has not participated in socialization and training on community participation in reducing the risk of flooding in the village.

Rainfall classification and forecasting based on a novel voting adaptive dynamic optimization algorithm

Environmental issues of rainfall are basic in terms of understanding and management of ecosystems and natural resources. The rainfall patterns significantly affect soil moisture, vegetation growth and biodiversity in the ecosystems. In addition, proper classification of rainfall types helps in the evaluation of the risk of flood, drought, and other extreme weather events’ risk, which immensely affect the ecosystems and human societies. Rainfall classification can be improved by using machine learning and metaheuristic algorithms. In this work, an Adaptive Dynamic Puma Optimizer (AD-PO) algorithm combined with Guided Whale Optimization Algorithm (Guided WOA) introduces a potentially important improvement in rainfall classification approaches. These algorithms are to be combined to enable researchers to comprehend and classify rain events by their specific features, such as intensity, duration, and spatial distribution. A voting ensemble approach within the proposed (AD-PO-Guided WOA) algorithm increases its predictive performance because of the combination of predictions from several classifiers to localize the dominant rainfall class. The presented approach not only makes the classifying of rain faster and more accurate but also strengthens the robustness and trustworthiness of the classification in this regard. Comparison to other optimization algorithms validates the effectiveness of the AD-PO-Guided WOA algorithm in terms of performance metrics with an outstanding 95.99% accuracy. Furthermore, the second scenario is applied for forecasting based on the long short-term memory networks (LSTM) model optimized by the AD-PO-Guided WOA algorithm. The AD-PO-Guided WOA- LSTM algorithm produces rainfall prediction with an MSE of 0.005078. Wilcoxon rank test, descriptive statistics, and sensitivity analysis are applied to help evaluating and improving the quality and validity of the proposed algorithm. This intensive method facilitates rainfall classification and is a base for suggested measures that cut the hazards of extreme weather events on societies.

Designing Effective Low-Impact Developments for a Changing Climate: A HYDRUS-Based Vadose Zone Modeling Approach

Low-Impact Developments (LIDs), like green roofs and bioretention cells, are vital for managing stormwater and reducing pollution. Amidst climate change, assessing both current and future LID systems is crucial. This study utilizes variably saturated flow modeling with the HYDRUS software (version 4.17) to analyze ten locations in Ontario, Canada, focusing on Toronto. Historical and projected climate data are used in flow modeling to assess long-term impacts. Future predicted storms, representing extreme precipitation events, derived from a regional climate model, were also used in the flow modeling. This enabled a comprehensive evaluation of LID performance under an evolving climate. A robust methodology is developed to analyze LID designs, exploring parameters like water inflow volumes, peak intensity, time delays, runoff dynamics, and ponding patterns. The findings indicate potential declines in LID performance attributed to rising water volumes, resulting in notable changes in infiltration for green roofs (100%) and bioretention facilities (50%) compared to historical conditions. Future climate predicted storms indicate reduced peak reductions and shorter time delays for green roofs, posing risks of flooding and erosion. Anticipated extreme precipitation is projected to increase ponding depths in bioretention facilities, resulting in untreated stormwater overflow and prolonged ponding times exceeding baseline conditions by up to 13 h at numerous Ontario locations.