Flood Risk Assessment Model Research Articles

Morphometric-hydro Characterization of the Coastal Line between El-Qussier and Marsa-Alam, Egypt: Preliminary Flood Risk Signatures

Egypt is highly exposed to flash flood hazards, particularly in Sinai Peninsula and along the Red Sea coast, causing sudden and huge damages to constructions and huge losses in human lives during a very short time. This paper investigates the dominant characterization of morphometrical features and their relationships with the hydrological behaviors along an important strip of the western Red Sea coast. The study focuses on analyzing the October 2009 and 2019 storm events along the coastal area between EL-Qussier and Marsa Alam in order to intiate a preliminary flood risk assessment model. Morphometric features along the entire study zone provide a complete scenario of the nature of the catchments and sub-catchments development. Numerous morphometric indexes such as catchments geometry, areal indexes, linear indexes, and relief indexes were examined through processing different sets of data. Modern techniques such as remote sensing and geospatial analysis were applied to process different spatial and spectral data. The hydrological model (HEC-HMS) in the WMS software was run to delineate the catchments and sub-catchments and extract the peak flow hydrograph curves for the main catchments. The results of the water amounts and peak flow were calculated using the SCS unit hydrograph approach. The hydrological characteristics of the major catchments reveal conditions for moderate levels of flash flooding. The study ended with a number of recommendations that could minimize the negative effects of the flash flood hazards.

Assessment of long and short-term flood risk using the multi-criteria analysis model with the AHP-Entropy method in Poyang Lake basin

China suffers the most serious loss of life and property with the most floods in the world. In this study, a multi-criteria analysis model with the combined analytic hierarchy process and Entropy weight method (AHP-Entropy) was proposed to assess the long and short-term flood risk in Poyang Lake basin, and results were verified by several flood events that happened on July 2020. Considering multi-factors of flood risk, six flood hazard factors (namely, maximum three-day rainfall (RMAX3), annual average rainstorm frequency (RF), annual average rainstorm amount (ARA), drainage density (DD), slope, elevation (DEM)) and four flood vulnerability factors (namely, population density (PD), land use pattern (LUP), GDP, normalized difference vegetation index (NDVI)) were selected and weights of them were derived from the AHP-Entropy method. Results show that PD (0.168), RMAX3 (0.163), LUP (0.146), GDP (0.129), and RF (0.111) play a vital role in the results of flood risk assessment. Spatially, the long and short-term flood risk maps are shown to have similar characteristics with correlation coefficient of 0.9056. Areas with high risk and very high risk account for 19.6% of the total area in the long-term flood risk map and increased to 22.2% in the short-term flood risk map. Overall, the northeastern parts of the Poyang Lake basin are more prone to floods and the flood risk gradually decreases from the Poyang Lake towards the surrounding areas. Verification of the results with Sentinel-1 synthetic aperture radar data shows that the flood risk assessment model has an accuracy of more than 50% in very high risk zones for floods, and more than 90% for high and very high risk floods, which showed that the presented model is reliable in flood risk assessment.

A systematic review of agent-based model for flood risk management and assessment using the ODD protocol

Recently, applications of agent-based model (ABM) have been used to understand the interaction between social and hydrological systems. These systems are dynamic and co-evolving, which can be captured through different decision-making entities in an ABM simulation. Therefore, this review aims to better understand the use of ABM for flood risk management and assessment (FRMA). The review comprises a systematic selection of literature using the PRISMA method, which is then assessed using an adapted version of the overview, design, and detail (ODD) protocol to better understand the ABM model development process for FRMA. The review finds that the use of the ODD protocol was only seen in 25% of the studies. The studies which did not explicitly use the ODD had a comprehensive description of the models, albeit done in a non-standardised way. Modellers continue to face the dilemma between parsimony and the breadth of the model as identified from the design component of the ODD. The hydrological component is mainly captured in the sub-model process of the ODD, however, improvements in the definition of the sub-model component may warrant a more comprehensive description of the processes and facilitate comparison across studies. The applications of ABM have shown promise to understand long term flood risks, test the efficacy of policies and better understand the factors that affect warning response during the flood evacuation process. ODD adopted for this review may consequently allow for the adoption and more coherent use of the protocol to document models in FRMA.

A Performance Comparison between GIS-based and Neuron Network Methods for Flood Susceptibility Assessment in Ayutthaya Province

Flooding has been a long withstanding issue in Thailand. Due to its geographical setup, mitigation and management of floods are challenging and hard to execute. One of the tools used in managing the events is “flood susceptibility mapping,” in which an incident probability as well as a rescue path is estimated and planned. To create one, the traditional GIS method called FRAM (flood risk assessment model), combined with AHP (analytical hierarchy process), is used and implemented on ArcGIS software. In this method, we first created a comparison table to compute weights for each of the selected factors. Then the computed weights were used in the FRAM model in ArcGIS to create a flood susceptibility map for each region. Each region was then classified as very high, high, medium, low, and very low risk. On the other hand, in computer science, machine learning and AI are prevalent and being adopted to various domains, promising the effectiveness of the method, potentially beat the forementioned traditional method. Therefore, ANN (artificial neural network) is adopted in this work to create the flood susceptibility map. The ANN technique is developed by using causal factors. The ANN classifies areas as either flood areas or flood-free areas. The 2 methods from different disciplines (GIS and Computer Science) are applied and described in this paper with the intention to prove whether the machine learning is really efficient and can outperform the traditional GIS approach. Data on Thailand’s Ayutthaya Province is used in this work as a case study - in order to assess flood prone areas and compared for performance evaluation. Both of which use the 6 selected factors according to the literature: (i) flow accumulation, (ii) elevation, (iii) land use, (iv) rainfall intensity, (v) slope and (vi) soil types. The results from the 2 methods were verified with historical flood data and compared. The results showed that ANN (obtained via sensitivity analysis) outperformed the FRAM with precision of 79.90 %, recall of 79.04 %, F1-score of 79.08 % and accuracy of 79.31 %. In addition, we found that (according to our ANN experiments) the main causal factors related to flood susceptibility map only included 3 factors: flow accumulation, elevation, and soil types. Therefore, the proposed methodology for assessment of flood susceptibility areas using these 3 factors could be considered sufficient and applied to other regions in related applications, when needed.

Flood risk assessment model combining hierarchy process and variable fuzzy set theory: a case study in Zhejiang province, China

Flood risk assessment model combining hierarchy process and variable fuzzy set theory: a case study in Zhejiang province, China

Coupling a land surface model with a hydrodynamic model for regional flood risk assessment due to climate change: Application to the Susquehanna River near Harrisburg, Pennsylvania

AbstractAn increase in heavy precipitation associated with climate change has exacerbated flooding in the Eastern U.S. To assess regional flood risk with changing climatic conditions, we demonstrate the application of a novel hydrologic modeling framework that integrates climate projections with a coupled Noah‐MP land surface model and a two‐dimensional HEC‐RAS hydrodynamic model. We employ this framework along a 41 km reach of the Susquehanna River near Harrisburg, Pennsylvania, where recent flood damages exceeded $2 billion (2011 Irene and Lee floods). Historical and future 30‐year and 100‐year peak‐discharge estimates were compared to assess how flood risk might be altered due to climate change. Results indicate that precipitation increases from climate change do not always lead to increases in flood risk, because interplay of hydrological components in the watershed, which are considered by Noah‐MP, largely controls flooding severity. However, climate change is expected to increase the severity of extreme events; if a 50‐year flood (the recurrence interval of Tropical Storm Lee) occurred toward the end of the 21st century in the worst‐case emission scenario, then flood volume would increase by 40% and flood extent by 15%, due to an increase in soil moisture from a wetter overall climate.

Dynamic Assessment of the Flood Risk at Basin Scale under Simulation of Land-Use Scenarios and Spatialization Technology of Factor

Climate change, population increase, and urban expansion have increased the risk of flooding. Therefore, accurately identifying future changing patterns in the flood risk is essential. For this purpose, this study elaborated a new framework for a basin scale that employs a future land-use simulation model, a factor spatialization technique, and a novel hybrid model for scenario-based flood risk assessment in 2030 and 2050. Three land-use scenarios (i.e., natural growth scenario, cropland protection scenario, and ecological protection scenario) were set and applied in Jinjiang Basin to explore the changes in future flood risk under these scenarios. The results indicate the different degrees of increase in flood risk that will occur in the three scenarios. Under the natural growth (NG) scenario, the city will expand rapidly with the growth of population and economy, and the total area with high and very high flood risk will increase by 371.30 km2 by 2050, as compared to 2020. However, under the ecological protection (EP) scenario, woodlands will be protected, and the growth in population, economy, and built-up lands will slow down with slightly increased risk of flooding. In this scenario, the total area with high and very high flood risk will increase by 113.75 km2 by 2050. Under the cropland protection (CP) scenario, the loss of croplands will have been effectively stopped, and the flood risk will not show a significant increase under this scenario, with an increase by only 90.96 km2 by 2050, similar to the EP scenario. Spatially, these increased flood risks mainly locate at the periphery of existing built-up lands, and the high-flood-risk zones are mainly distributed in the southeast of the Jinjiang Basin. The information about increasing flood risk determined by the framework provides insight into the spatio-temporal characteristics of future flood-prone areas, which facilitates reasonable flood mitigation measures to be developed at the most critical locations in the region.

A distributed hydrodynamic model for urban storm flood risk assessment

Hydrodynamic models have been increasingly applied to urban flood simulations, but still encounter challenges because of complicated urban landscapes, buildings and structures, as well as insufficient sewer data. In order to improve the applicability in real-life urban environments, a distributed hydrodynamic model for urban storm flood risk assessment (DHMUrban) is developed in this study. It is based on the two-dimensional shallow water flow equations simplified by neglecting the convection terms in the momentum equations. The sharp variation of ground surface often happening in urban areas is specially treated with the broad-crested weir flow equation. The rainfall, infiltration and sewer drainage are considered at each overland cell. Two connection approaches between the overland model and the sewer model are proposed depending on the availability of sewer data. The overland cells and the sewer nodes are connected physically in areas with complete sewer data, and connected conceptually through a drainage relationship in areas where sewer data are insufficient or unavailable. The present model has been evaluated in five cases, showing comparable reliability and stability. It has been applied to a real-life urban catchment in Shenzhen City, and demonstrated to be capable of presenting well the ground surface flooding, adapting adequately to the real-life urban areas with different data availability and providing practical and reliable solutions for urban storm flood risk assessments.

Flood Risk Assessment in Small Watershed Based on Catastrophe Theory

Flood system is a complex system affected by many control factors and has typical system characteristics, such as irreversibility, fuzzy characteristics, gray characteristics, etc. Catastrophe theory is a well developed singularity theory, which has been initially applied in some leaping disaster systems. However, in the field of flood risk management, the application of catastrophe theory is relatively few. In this paper, a small watershed in the mountainous area of southwest China is taken for an example. The catastrophe assessment theory is adopted to conduct flood risk assessment. By analyzing the characteristics of the flood system and selecting flood risk factors, the flood risk assessment index system is established and the catastrophe theory flood risk assessment model is conducted, thus conducting flood risk zoning and management according to the assessment result, which provides a new reliable method for flood risk assessment and offers foundation for disaster reduction department.

A Case Study of Flood Risk Evaluation Based on Emergy Theory and Cloud Model in Anyang Region, China

With the progression of climate change, the intensity and frequency of extreme rainfall have increased in many parts of the world, while the continuous acceleration of urbanization has made cities more vulnerable to floods. In order to effectively estimate and assess the risks brought by flood disasters, this paper proposes a regional flood disaster risk assessment model combining emergy theory and the cloud model. The emergy theory can measure many kinds of hazardous factor and convert them into unified solar emergy (sej) for quantification. The cloud model can transform the uncertainty in flood risk assessment into certainty in an appropriate way, making the urban flood risk assessment more accurate and effective. In this study, the flood risk assessment model combines the advantages of the two research methods to establish a natural and social dual flood risk assessment system. Based on this, the risk assessment system of the flood hazard cloud model is established. This model was used in a flood disaster risk assessment, and the risk level was divided into five levels: very low risk, low risk, medium risk, high risk, and very high risk. Flood hazard risk results were obtained by using the entropy weight method and fuzzy transformation method. As an example for the application of this model, this paper focuses on the Anyang region which has a typical continental monsoon climate. The results show that the Anyang region has a serious flood disaster threat. Within this region, Linzhou County and Anyang County have very high levels of risk for flood disaster, while Hua County, Neihuang County, Wenfeng District and Beiguan District have high levels of risk for flood disaster. These areas are the core urban areas and the economic center of local administrative regions, with 70% of the industrial clusters being situated in these regions. Only with the coordinated development of regional flood control planning, economy, and population, and reductions in the uncertainty of existing flood control and drainage facilities can the sustainable, healthy and stable development of the region be maintained.

Hurricane Freshwater Flood Risk Assessment Model for Residential Buildings in Southeast US Coastal States Considering Climate Change

AbstractA significant amount of hurricane losses are caused by rainfall-induced freshwater flooding, and hurricane precipitation is expected to increase in the warming climate. However, the climate...

Assessing Property Level Economic Impacts of Climate in the US, New Insights and Evidence from a Comprehensive Flood Risk Assessment Tool

Hurricanes and flood-related events cause more direct economic damage than any other type of natural disaster. In the United States, that damage totals more than USD 1 trillion in damages since 1980. On average, direct flood losses have risen from USD 4 billion annually in the 1980s to roughly USD 17 billion annually from 2010 to 2018. Despite flooding’s tremendous economic impact on US properties and communities, current estimates of expected damages are lacking due to the fact that flood risk in many parts of the US is unidentified, underestimated, or available models associated with high quality assessment tools are proprietary. This study introduces an economic-focused Environmental Impact Assessment (EIA) approach that builds upon an our existing understanding of prior assessment methods by taking advantage of a newly available, climate adjusted, parcel-level flood risk assessment model (First Street Foundation, 2020a and 2020b) in order to quantify property level economic impacts today, and into the climate adjusted future, using the Intergovernmental Panel on Climate Change’s (IPCC) Representative Concentration Pathways (RCPs) and NASA’s Global Climate Model ensemble (CMIP5). This approach represents a first of its kind—a publicly available high precision flood risk assessment tool at the property level developed completely with open data sources and open methods. The economic impact assessment presented here has been carried out using residential buildings in New Jersey as a testbed; however, the environmental assessment tool on which it is based is a national scale property level flood assessment model at a 3 m resolution. As evidence of the reliability of the EIA tool, the 2020 estimated economic impact (USD 5481 annual expectation) was compared to actual average per claim-year NFIP payouts from flooding and found an average of USD 5540 over the life of the program (difference of less than USD 100). Additionally, the tool finds a 41.4% increase in average economic flood damage through the year 2050 when environmental change is included in the model.

Scenario-based flood risk assessment for urbanizing deltas using future land-use simulation (FLUS): Guangzhou Metropolitan Area as a case study

Preparing cities for sea-level rise is one of the critical challenges of the twenty-first century. Extreme weather events, natural hazards, and the failure of climate mitigation and adaptation are substantial risks. These risks are especially significant in fast-urbanizing deltas, such as the Pearl River Delta in China, because the conflict between urbanization and flooding caused by climate change will be more significant in the future. This paper elaborates on an approach that employs a future land-use simulation (FLUS) model for scenario-based 100-year coastal flood risk assessment. Storylines of future scenarios from the Intergovernmental Panel on Climate Change (IPCC), called the representative concentration pathways (RCPs) 2.6 and 8.5, are utilized in the present study. The Guangzhou Metropolitan Area (GMA) is used as a case study to explore the probable implications of future land-use changes due to the ongoing urbanization process in the region in relation to projected environmental changes (sea-level rise, storm surge, and land subsidence). The results indicate that there will be a significant increase in flooded urban areas in the future. The simulations show that, as compared to 2015, the built-up area in the GMA will increase by 246.57 km2 in 2030 and 513.03 km2 in 2050. As compared to 2015, the flooding of built-up areas in 2030 and 2050 will respectively increase by about 31.32 km2 and 48.49 km2 under the RCP 8.5 scenario. It is also found that, as the main driving factor, urbanization will increase the flooding of built-up areas in Guangzhou in 2030 and 2050 by about 1.9 km2 and 5.9 km2, respectively, under the RCP 2.6 scenario as compared to 2015. Additionally, due to environmental changes, the flooding of built-up areas in Guangzhou will increase by about 24.2 km2 and 26.8 km2, respectively, under the RCP 8.5 scenario by 2030 and 2050 as compared to 2015. This increasing flood risk information determined by the simulation provides insight into the spatial distribution of future flood-prone urban areas to facilitate the development and prioritization of flood mitigation measures at the most critical locations in the region.

Methodology for future flood assessment in terms of economic damage: Development and application for a case study in Nepal

AbstractTo address the lack of adequate measures for flood risk reduction in Nepal, where recurrent flood‐related hazards have had grave consequences for many people over the past decades, we develop a flood risk assessment model for a study area in the Sindhupalchok District. The model considers direct and indirect damages that are assigned to four asset categories (hydropower plants, roads, houses, and farmlands) and two scenarios (low‐exposure‐low‐flood and high‐exposure‐high‐flood scenarios). Model results indicate the following: (a) the planned expansion of hydropower plants reflected in the high exposure scenario is responsible for a substantial increase of economic damage compared with the low scenario and (b) for both scenarios, flood damage is largely related to road closures, which result in the loss of income for villagers and the loss of customs revenues. As this study aims both to provide methods for the assessment of flood risk and to demonstrate them in a case study, we discuss future work related to model‐based flood risk assessments that are needed towards flood risk reduction in Nepal.

A GIS-based spatial multi-index model for flood risk assessment in the Yangtze River Basin, China

This paper developed a GIS-based spatial multi-index model for large basin-scale flood risk assessment. In terms of the risk definition proposed by the IPCC, the flood risk in the Yangtze River Basin (YRB) was classified into indexes of hazard, vulnerability, and exposure. The model systematically accounts for various flood risk indicators related to the economic, social and ecological environment of the YRB. Using the robust data space analysis and processing capabilities of ArcGIS, these flood risk indicators were superimposed and analyzed to generate an integrated flood risk spatial distribution map for the YRB. The modeling results were verified reasonably well using observation data from the YRB floods in 1998, 2008, and 2016. We found that 24.90% of the study area was at very high and high risk in 1998, and the risk in these areas decreased to 15.95% and 17.61% in 2008 and 2016, respectively. We believe that the GIS-based spatial multi-index model can be applied to other areas where basin-scale flood risk assessment is desired and contribute to further scientific research on flood forecasting and mitigation.

Flood Risk Assessment Based on Fuzzy Synthetic Evaluation Method in the Beijing-Tianjin-Hebei Metropolitan Area, China

Flooding is one of the most devastating natural events and leads to enormous and recurring loss of life, properties, and resources around the globe. With climate change and accelerating urbanization, flood disasters in China have increasingly affected the sustainable development of metropolitan areas. Risk assessment is an essential step in flood management and disaster mitigation, which provide a quantitative measure of flood risk. However, the difficulty of flood risk zoning is dealing with the uncertainty of the evaluation process and the complicated non-linear relationship between indicators and risk levels. To address this issue, a fuzzy synthetic evaluation (FSE) method based on combined weight (CW) was utilized in this paper to generate flood risk maps at a grid-scale (1 × 1 km). For the case study in the Beijing-Tianjin-Hebei metropolitan area (BTH) in China, fourteen indicators were selected to construct the flood risk assessment model based on the FSE approach integrated with ArcGIS. The research demonstrates that moderate, high, and very high risk zones are distributed in the southeast fluvial plain of the BTH area, accounting for 31.36% of the total land area. Meanwhile, low and very-low risk zones occupy 68.64% of the total land area, and are primarily located in the high plateau and mountain regions in the northwest. We analyzed the risk level of each county and proposed risk mitigation measures based on field investigations. The verified risk assessment results were spatially consistent with the historical flood disaster records and loss positions, indicating the accuracy and reliability of the risk assessment map using the FSE approach. Compared with the IPCC (Intergovernmental Panel on Climate Change) TAR (Third Assessment Report) and AR5 (Fifth Assessment Report) methods, FSE has significant advantages in handling uncertainty, complexity, and the non-linear relationship between indices and risk grades. This study provides a novel quantitative method for flood risk assessment in metropolitan areas and practical implications for urban flood management.

A GIS analysis approach for flood vulnerability and risk assessment index models at sub-district scale

This paper presents flood vulnerability and risk assessment index models, with the aim to establish a spatial risk index at sub-district scales for urban flood scenarios in the Makassar region. Firstly, the overall vulnerability assessment to floods based on the local framework analysis of the BNPB (The Indonesian National Board for Disaster Management) has been developed using Geographical Information System (GIS) analysis. These indicators were composed of various social, physical, economic, and environmental factors. Second, GIS analysis conducts grid index modeling of flood hazard model by incorporating the measurement of floods in 2013 as a flood hazard scenario. Finally, by combining the spatial factors of flood hazard and flood vulnerabilities, a flood risk assessment model has been simulated at sub-district scales to evaluate the potential impacts on social, physical, economic, and environmental aspects.

Probabilistic Modeling of Flood Hazard and its Risk Assessment for Eastern Region of India.

This article models flood occurrence probabilistically and its risk assessment. It incorporates atmospheric parameters to forecast rainfall in an area. This measure of precipitation, together with river and ground parameters, serve as parameters in the model to predict runoff and subsequently inundation depth of an area. The inundation depth acts as a guide for predicting flood proneness and associated hazard. The vulnerability owing to flood has been analyzed as social vulnerability , vulnerability to property , and vulnerability to the location in terms of awareness . The associated risk has been estimated for each area. The distribution of risk values can be used to classify every area into one of the six risk zones-namely, very low risk, low risk, moderately low risk, medium risk, high risk, and very high risk. The prioritization regarding preparedness, evacuation planning, or distribution of relief items should be guided by the range on the risk scale within which the area under study falls. The flood risk assessment model framework has been tested on a real-life case study. The flood risk indices for each of the municipalities in the area under study have been calculated. The risk indices and hence the flood risk zone under which a municipality is expected to lie would alter every day. The appropriate authorities can then plan ahead in terms of preparedness to combat the impending flood situation in the most critical and vulnerable areas.

The Development and Application of the Urban Flood Risk Assessment Model for Reflecting upon Urban Planning Elements

As a city develops and expands, it is likely confronted with a variety of environmental problems. Although the impact of climate change on people has continuously increased in the past, great numbers of natural disasters in urban areas have become varied in terms of form. Among these urban disasters, urban flooding is the most frequent type, and this study focuses on urban flooding. In cities, the population and major facilities are concentrated, and to examine flooding issues in these urban areas, different levels of flooding risk are classified on 100 m × 100 m geographic grids to maximize the spatial efficiency during the flooding events and to minimize the following flooding damage. In this analysis, vulnerability and exposure tests are adopted to analyze urban flooding risks. The first method is based on land-use planning, and the building-to-land ratio. Using fuzzy approaches, the tests focus on risks. However, the latter method using the HEC-Ras model examines factors such as topology and precipitation volume. By mapping the classification of land-use and flooding, the risk of urban flooding is evaluated by grade-scales: green, yellow, orange, and red zones. There are two key findings and theoretical contributions of this study. First, the areas with a high flood risk are mainly restricted to central commercial areas where the main urban functions are concentrated. Additionally, the development density and urbanization are relatively high in these areas, in addition to the old center of urban areas. In the case of Changwon City, Euichang-gu and Seongsan-gu have increased the flood risk because of the high property value of commercial areas and high building density in these regions. Thus, land-use planning of these districts should be designed to reflect upon the different levels of flood risks, in addition to the preparation of anti-disaster facilities to mitigate flood damages in high flood risk areas. Urban flood risk analysis for individual land use districts would facilitate urban planners and managers to prioritize the areas with a high flood risk and to prepare responding preventive measures for more efficient flood management.

Residential Flood Loss Assessment and Risk Mapping from High-Resolution Simulation

Since the patterns of residential buildings in the urban area are small-sized and dispersed, this study proposes a high-resolution flood loss and risk assessment model to analyze the direct loss and risk impacts caused by floods. The flood inundation simulation with a fine digital elevation model (DEM) provides detailed estimations of flood-inundated areas and their corresponding inundation depths during the 2016 Typhoon Megi and 2017 Typhoon Haitang. The flood loss assessment identifies the impacts of both events on residential areas. The depth-damage table from surveys in the impacted area was applied. Results indicated that the flood simulation with the depth-damage table is an effective way to assess the direct loss of a flood disaster. The study also showed the effects of spatial resolution on the residential loss. The results indicated that the low-resolution model easily caused the estimated error of loss in dispersed residential areas when compared with the high-resolution model. The analytic hierarchy process (AHP), as a multi-criteria decision-making method, was used to identify the weight factor for each vulnerability factor. The flood-vulnerable area was mapped using natural and social vulnerability factors, such as high-resolution DEM, distance to river, distance to fire station, and population density. Eventually, the flood risk map was derived from the vulnerability and flood hazard maps to present the risk level of the flood disaster in the residential areas.