Flood Risk Mapping Research Articles

Flood risk assessment: role of mitigation capacity in spatial flood risk mapping

Globally, the frequency, intensity, and damage rate of floods have increased extensively in the last few decades. The present study proposes a spatial-based multi-criteria approach integrating mitigation capacity to map the degree of flood risk. Flood risk is calculated considering 14 relevant factors under three risk components. Thematic layers were ranked through fuzzy membership functions, and risk components were prepared using fuzzy overlay operation. To make a comparison, first, we calculated risk with mitigation capacity and later without it. Results exhibit, without mitigation capacity around 67% of the study area is exposed to high flood risk, while after integrating mitigation capacity flood risks decreased to 62%. Moreover, validation evaluation suggests risk map integrating mitigation capacity is more accurate (82%) than the risk map without it (79%). Therefore, the developed integrated risk mapping approach is more efficient to assess flood risk, and can be implemented by policymakers in similar environments.

Flood Risk Mapping by Remote Sensing Data and Random Forest Technique

Detecting effective parameters in flood occurrence is one of the most important issues that has drawn more attention in recent years. Remote Sensing (RS) and Geographical Information System (GIS) are two efficient ways to spatially predict Flood Risk Mapping (FRM). In this study, a web-based platform called the Google Earth Engine (GEE) (Google Company, Mountain View, CA, USA) was used to obtain flood risk indices for the Galikesh River basin, Northern Iran. With the aid of Landsat 8 satellite imagery and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), 11 risk indices (Elevation (El), Slope (Sl), Slope Aspect (SA), Land Use (LU), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Topographic Wetness Index (TWI), River Distance (RD), Waterway and River Density (WRD), Soil Texture (ST]), and Maximum One-Day Precipitation (M1DP)) were provided. In the next step, all of these indices were imported into ArcMap 10.8 (Esri, West Redlands, CA, USA) software for index normalization and to better visualize the graphical output. Afterward, an intelligent learning machine (Random Forest (RF)), which is a robust data mining technique, was used to compute the importance degree of each index and to obtain the flood hazard map. According to the results, the indices of WRD, RD, M1DP, and El accounted for about 68.27 percent of the total flood risk. Among these indices, the WRD index containing about 23.8 percent of the total risk has the greatest impact on floods. According to FRM mapping, about 21 and 18 percent of the total areas stood at the higher and highest risk areas, respectively.

A Flood Damage and Shelter Need Assessment: A Case Study of Mueang Sing Buri, Thailand

Flooding is one of the main disasters in Thailand and Mueang Sing Buri is among those areas hit. Located on the Chao Phraya River Basin, in the central region of Thailand, the area receives a large amount of runoff during monsoon seasons which causes frequent flood disasters. The aims of this research are to create a flood hazard map and to estimate the number of people that may need shelter after the occurrence of a flood, and to evaluate whether the shelter capacity is adequate in Mueang Sing Buri. To explore the potential locations of emergency shelters, the relevant information related to flooding was initially recorded, such as building detail, flood depth, elevation map, and flood risk map. The available space of each building varies by the characteristics of building types. The calculation of shelter capacity thus depends on characteristics of the buildings, accessibility, and percent of vacant area. The emergency shelter assessment benefits many sectors in the design of preparation plans for hazard management.

Flood Risk Communication Using ArcGIS StoryMaps

In Canada, flooding is the most common and costly natural hazard. Flooding events significantly impact communities, damage infrastructures and threaten public security. Communication, as part of a flood risk management strategy, is an essential means of countering these threats. It is therefore important to develop new and innovative tools to communicate the flood risk with citizens. From this perspective, the use of story maps can be very effectively implemented for a broad audience, particularly to stakeholders. This paper details how an interactive web-based story map was set up to communicate current and future flood risks in the Petite-Nation River watershed, Quebec (Canada). This web technology application combines informative texts and interactive maps on current and future flood risks in the Petite-Nation River watershed. Flood risk and climate maps were generated using the GARI tool, implemented using a geographic information system (GIS) supported by ArcGIS Online (Esri). Three climate change scenarios developed by the Hydroclimatic Atlas of Southern Quebec were used to visualize potential future impacts. This study concluded that our story map is an efficient flood hazard communication tool. The assets of this interactive web mapping tool are numerous, namely user-friendly mapping, use and interaction, and customizable displays.

Evaluating urban flood risk using hybrid method of TOPSIS and machine learning

With the growth of cities, urban flooding has increasingly become an issue for regional and national governments. The destructive effects of floods are magnified in cities. Accurate models of urban flood susceptibility are required to mitigate this hazard mitigation and build resilience in cities. In this paper, we evaluate flood riskin Jiroft city, Iran, using a combination of machine learning and decision-making methods. Flood hazard maps were created using three state-of-the-art machine learning methods (support vector machine, random forest, and boosted regression tree). The metadata supporting our analysis comprises 218 flood inundation points and a variety of derived factors: slope aspect, elevation, slope angle, rainfall, distance to streets, distance to rivers, land use/land cover, distance to urban drainages, urban drainage density, and curve number. We then employed the TOPSIS decision-making tool for urban flood vulnerability analysis, which is based on socio-economic factors such as building density, population density, building history, and socio-economic conditions. Finally, we derived an urban flood risk map for Jiroft based on flood hazard and vulnerability maps. Of the three models tested, the random forest model yielded the most accurate map. The results indicate that urban drainage density and distance to urban drainages are the most important factors in urban flood hazard modeling. As might be expected, areas with a high or very high population density are most vulnerable to flooding. These results show that flood risk mapping provide insights for priority planning in flood risk management, especially in areas with limited hydrological data.

A Revised Approach to Flood Damage Estimation in Flood Risk Maps and Flood Risk Management Plans, Poland

This article describes the determination of asset values in residential and industrial areas in Poland that can be used in the preparation of flood hazard and risk maps (FRMs) and flood risk management plans (FRMPs). In the Floods Directive’s first cycle of implementation (2010–2015), German indicators of asset value were adapted to Polish conditions. This approach was used due to the lack of national statistics necessary to determine the value of property. In this study, the asset values in residential and industrial areas were prepared on the basis of the nationwide household wealth survey conducted in 2016 by the Narodowy Bank Polski (the central bank of Poland) and other data available from Statistics Poland. The proposed approach enables the determination of asset values based on real, validated data. The obtained indicators result in higher and more realistic values of the assets of households (buildings with contents) and the assets of companies (fixed assets and stocks) operating in both residential and industrial areas. The proposed approach, as an extension in relation to the first planning cycle, has been implemented in the second cycle of FRMP preparation in Poland.

Geomorphological Appraisal of the Madogo-Mororo Floods using Neuro-Fuzzy Inference and the Multinomial Logistic Regression Models

The Tana River County has had serious issues relating to management of its riparian corridors and gazetted catchments. Floods have been an annual episode in Madogo-Mororo areas, with disastrous consequences every time they occur. Lives of both humans and livestock have been lost in every such episode, alongside the destruction of infrastructures and damages to properties, valued in the range of millions of Dollars. The causes do not directly relate to the rainfall volume registered whenever it rains, but rather to myriads of anthropogenic, geomorphological and hydrological variables. A combined statistical - and Artificial Intelligence-aided mapping of the study area was deemed a necessity as a DESK-TOP planning tool for flood risk assessment in the Study Area, as well as for detailed flood-risk mapping. The objective of this study was to identify all these three components, which involved mapping the longitudes, latitudes, elevations, soil types prevalent, vegetation density, grainsize of rocks or soil particles deemed prevalent in the surficial geology, culvert types constructed in the areas visited, hosing types, nearness of settlement to rivers, erosional intensity status of an area, as the input variables, to predict the output variable, flooding probability of an area. Visits were made to the Madogo -Mororo areas and the coded weightings of the aforementioned variables assigned, as deemed appropriate by researcher. For instance, soil erosion intensity was coded as 1, 2, and 3, with the numbers respectively coding for low intensity, moderate intensity and high intensity. The R software modeling libraries were used in this exercise. The neuro-fuzzy algorithms were then used to develop prediction models for floods, based on these coded parameters to assess then accuracy levels of the algorithm test datasets were used, generating over 98.4 percent levels of accuracies. The logistic regression models were then used, for comparison, also recording accuracy levels of 98.4 percent. This affirmed the supremacy of fuzzy logic and logistic regression as useful assessment tools for floods probability mapping in the study area. A code value of 1, represented a high probability of an area being flooded (flood risk areas), and a code of 2 was used to represent high-probability of moderate risk to flooding, whereas the value of 3 signaled a risk-free high-probability in an area. These probability codes were then plotted in a GIS map to help display the geospatial models of flood risk in Madogo and Mororo areas. The study notes the role of human activities and geomorphic factors in exacerbating the propensity to flooding episodes in the study area. Summarily, this study validates the use of neuro-fuzzy inference and logistic regression as dependable mapping tools for flood episodes in Madogo Mororo areas, used alongside the GIS techniques.

A review on flood risk assessment using multicriteria decision making technique

AbstractFlood disaster risk assessment is a crucial and very challenging task because of the uncertainty of flood events that are due to many climatic and physiographic factors of watersheds. The focus of this paper is to review flood risk assessment using multicriteria analysis (MCA) approach by considering risk as a function of flood hazard and vulnerability. The study analyzes the methods and reports of recent works in assessing flood risk based on MCA technique. The review reveals that the analytical hierarchical process (AHP) is the most widely used MCA technique due to its simplicity, less data requirement, and sound and reliable predictions of flood risk zones. The study also reveals that flood risk is a function and product of flood hazard and vulnerability. More so, high flood hazard does not guarantee high flood risk and vice versa. Furthermore, high flood vulnerability does not guarantee high flood risk and vice versa. In conclusion, more efficient state‐of‐art flood models that are based on hydrological processes should be explored for effective flood risk assessment and mapping. Further studies on flood risk should incorporate impact of climate change on flood risk indicators.

Vulnerability Assessment of Urban Floods in Lahore, Pakistan using GIS based integrated Analytical Hierarchy Approach

Urban flooding is getting attention due to its adverse impact on urban lives in mega cities of the developing world particularly Pakistan. This study aims at finding a suitable methodology for mapping urban flooded areas to estimate urban flooding vulnerability risks in the cities of developing countries particularly Lahore, Pakistan. To detect the urban flooded vulnerability and risk areas due to natural disaster, GIS-based integrated Analytical Hierarchy Process (AHP) is applied for the case of Lahore, which is the second most populous city and capital of the Punjab, Pakistan. For the present research, the flood risk mapping is prepared by considering these significant physical factors like elevation, slope, and distribution of rainfall, land use, density of the drainage network, and soil type. Results show that the land use factor is the most significant to detect vulnerable areas near roads and commercial areas. For instance, this method of detection is 88%, 80% and 70% accurate for roads, commercial and residential areas. The methodology implemented in the present research can provide a practical tool and techniques to relevant policy and decision-makers authorities to prioritize and actions to mitigate flood risk and vulnerabilities and identify certain vulnerable urban areas, while formulating a methodology for future urban flood risk and vulnerability mitigation through an objectively simple and organizationally secure approach.

Cartografía de inundaciones en la planificación territorial. Estudio comparado entre Argentina y España

Flood risk management has gained new prominence thanks to the incorporation of cartographic tools in spatial planning. Flood risk mapping has been incorporated in several countries as an important piece when it comes to planning new land uses. Thus, traditional flood risk reduction measures based on structural measures have been enriched with the incorporation of risk mapping as an argument for legal accreditation in land use planning. This paper analyzes the application of flood risk mapping in land use planning in Argentina and Spain. The analysis is focused on the use of cartographic documents to certify risk levels at a local scale. These are two countries that have opted for the elaboration of risk mapping in the processes of assigning land uses. Although the results of their implementation are different in relation to the existence of regulatory regulations for these processes and the different administrative scales with competences in spatial planning and urban planning. In both cases, it is verified that the flood map in urban processes is understood as a hazard map, without integrating, except for specific examples, vulnerability analysis in the territories under planning.

Mapping Flood Risk Uncertainty Zones in Support of Urban Resilience Planning

<p>River flooding and urbanization are processes of different character that take place worldwide. As the latter tends to make the consequences of the former worse, together with the uncertainties related to future climate change and flood-risk modeling, there is a need to both use existing tools and develop new ones that help the management and planning of urban environments. In this article a prototype tool, based on estimated maximum land cover roughness variation, the slope of the ground, and the quality of the used digital elevation models, and that can produce flood ‘uncertainty zones’ of varying width around modeled flood boundaries, is presented. The concept of uncertainty, which urban planners often fail to consider in the spatial planning process, changes from something very difficult into an advantage in this way. Not only may these uncertainties be easier to understand by the urban planners, but the uncertainties may also function as a communication tool between the planners and other stakeholders. Because flood risk is something that urban planners always need to consider, these uncertainty zones can function both as buffer areas against floods, and as blue-green designs of significant importance for a variety of ecosystem services. As the Earth is warming and the world is urbanizing at rates and scales unprecedented in history, we believe that new tools for urban resilience planning are not only urgently needed, but also will have a positive impact on urban planning.</p>

Mapping the spatial and temporal variability of flood hazard affected by climate and land-use changes in the future

The predicts current and future flood risk in the Kalvan watershed of northwestern Markazi Province, Iran. To do this, 512 flood and non-flood locations were identified and mapped. Twenty flood-risk factors were selected to model flood risk using several machine learning techniques: conditional inference random forest (CIRF), the gradient boosting model (GBM), extreme gradient boosting (XGB) and their ensembles. To investigate the future (year 2050) effects of changing climates and changing land use on future flood risk, a general circulation model (GCM) with representative concentration pathways (RCPs) of the 2.6 and 8.5 scenarios by 2050 was tested for impacts on 8 precipitation variables. In addition, future land uses in 2050 was prepared using a CA-Markov model. The performances of the flood risk models were validated with Receiver Operating Characteristic-Area Under Curve (ROC-AUC) and other statistical analyses. The AUC value of the ROC curve indicates that the ensemble model had the highest predictive power (AUC = 0.83) and was followed by GBM (AUC = 0.80), XGB (AUC = 0.79), and CIRF (AUC = 0.78). The results of climate and land use changes on future flood-prone areas showed that the areas classified as having moderate to very high flood risk will increase by 2050. Due to the changes occurring with land uses and in climates, the area classified as moderate to very high risk increased in the predictions from all four models. The areal proportion classes of the risk zones in 2050 under the RCP 2.6 scenario using the ensemble model have changed of the following proportions from the current distribution Very Low = −12.04 %, Low = −8.56 %, Moderate = +1.56 %, High = +11.55 %, and Very High = +7.49 %. The RCP 8.5 scenario has caused the following changes from the present percentages: Very Low = −14.48 %, Low = −6.35 %, Moderate = +4.54 %, High = +10.61 %, and Very High = +5.67 %. The results of current and future flood risk mapping can aid planners and flood hazard managers in their efforts to mitigate impacts.

Climate Justice Planning in Global South: Applying a Coupled Nature–Human Flood Risk Assessment Framework in a Case for Ho Chi Minh City, Vietnam

Developing countries in the global south that contribute less to climate change have suffered greater from its impacts, such as extreme climatic events and disasters compared to developed countries, causing climate justice concerns globally. Ho Chi Minh City has experienced increased intensity and frequency of climate change-induced urban floods, causing socio-economic damage that disturbs their livelihoods while urban populations continue to grow. This study aims to establish a citywide flood risk map to inform risk management in the city and address climate justice locally. This study applied a flood risk assessment framework integrating a coupled nature–human approach and examined the spatial distribution of urban flood hazard and urban flood vulnerability. A flood hazard map was generated using selected morphological and hydro-meteorological indicators. A flood vulnerability map was generated based on a literature review and a social survey weighed by experts’ priorities using the Fuzzy Delphi Method and Analytic Network Process. Vulnerability indicators including demographic characteristics, infrastructure, and land use patterns were used to generate a flood vulnerability map. The results illustrate that almost the entire central and northeastern parts of the city are at high flood risk, whereas the western part is at low flood risk. The findings have implications in urban planning via identifying risk hot spots in order to prioritize resources for mitigating hazards and enhancing community resilience to urban floods.

A dynamic, convenient and accurate method for assessing the flood risk of people and vehicle

With the increase of extreme rainstorm caused by climate change, and the development of urbanization and the improvement of people's living standard, there is an urgent need to draw a dynamic, convenient and accurate flood risk map for different disaster bearing bodies, so as to protect people's lives and properties, as well as improve people's risk awareness and facilitate people's lives. This study mainly researched a method of drawing a dynamic, convenient and accurate flood risk map for people and vehicle. In this study, the surface runoff is simulated by GPU (Graphics Processing Unit) Accelerated Surface Water Flow and Transport model (GAST model), meanwhile, the flood risk of people and vehicle is graded base on the incipient velocity formulas, the most unfavorable principle and grading method, finally, the method is applied in two application cases. The following results are obtained: (1) this method could assess the flood risk of people and vehicle dynamically, conveniently and accurately; (2) the flood risk of people is less than that of vehicle, for the same flood hazard, time and place; (3) the adverse effect of water depth on flood risk for vehicle is greater than that for people. This method of assessing the flood risk map of people and vehicle is of great significance, for flood risk management, land use plan and emergency management department to reduce flood disaster risk.

Flood Hazard Mapping and Assessment of Precipitation Monitoring System Using GIS-Based Morphometric Analysis and TRMM Data: A Case Study of the Wadi Qena Watershed, Egypt

Wadi Qena is one of the Nile Valley areas particularly at risk of severe flash flooding, located in Egypt. The study aims to verify TRMM rainfall data (TRMM 3B42), using eight stations across Egypt as well as relies on morphometric analysis to generate a flood risk map based on the ranking method. Three process could be recognized through the study, calibration, correction and verification processes. The results discuss the match daily rainfall trends of TRMM and observed data, producing a correction equation for TRMM data with root mean square error (RMSE) value of 0.837 mm d-1 and R2= 0.238 (calibration process). On the other hand, a verification process, using the developed correction equation, obtain RMSE value of 1.701 mm d-1 and R2= 0.601. The morphometric analysis shows 32 sub-basins with a hazard degree from moderate to high, amounting to 50.3% of the watershed area. Conclusively, this study confirms that the current monitoring system is not enough to cover the whole area, especially the high-risk sub-basins, and TRMM data could provide key information for water-related applications in Egypt.

AN EFFICIENT ND-POINT DATA STRUCTURE FOR QUERYING FLOOD RISK

Abstract. Governments use flood maps for city planning and disaster management to protect people and assets. Flood risk mapping projects carried out for these purposes generate a huge amount of modelling results. Previously, data submitted are highly condensed products such as typical flood inundation maps and tables for loss analysis. Original modelling results recording critical flood evolution processes are overlooked due to cumbersome management and analysis. This certainly has drawbacks: the ‘static’ maps impart few details about the flood; also, the data fails to address new requirements. This significantly confines the use of flood maps. Recent development of point cloud databases provides an opportunity to manage the whole set of modelling results. The databases can efficiently support all kinds of flood risk queries at finer scales. Using a case study from China, this paper demonstrates how a novel nD-PointCloud structure, HistSFC, improves flood risk querying. The result indicates that compared with conventional database solutions, HistSFC holds superior performance and better scalability. Besides, the specific optimizations made on HistSFC can facilitate the process further. All these indicate a promising solution for the next generation of flood maps.

SUPERVISED CLASSIFICATION METHODS FOR AUTOMATIC DAMAGE DETECTION CAUSED BY HEAVY RAINFALL USING MULTITEMPORAL HIGH RESOLUTION OPTICAL IMAGERY AND AUXILIARY DATA

Abstract. In the context of climate change and rising frequency of extreme hydro-meteorological events around the world, flood risk management and mapping of heavy rainfall-related damages represent an ongoing critical challenge. For decades now, remote sensing has been largely used to investigate spatial and temporal changes in land use and water resources. Today, different satellite products provide fast and crucial knowledge for the study of hydrological disasters over large areas, possibly in remote regions, with high spatial resolution and high revisit frequency. Yet, until now, few works have sought to detect the full range of extreme rainfall-related damages with optical imagery, especially those caused by intense rainwater runoff beyond the direct vicinity of major waterways. The work presented in this paper focuses on the Aude severe weather event of October 15th, 2018, in the South of France, for which more than a thousand claims for agricultural disaster were registered, both related to river overflowing and rainwater runoff.The full resources of ground truths, contextual information, land use as well as digital elevation model (DEM) combined to high resolution and high frequency optical imagery (Sentinel-2, Pléiades) are used to develop an automatic damage detection method based on supervised classification algorithms. Through the combination of several indicators characterizing heterogeneous spectral variations among agricultural plots following the event, a Gaussian process classifier achieved various classification accuracies up to 90% on a large comparable and independent photo-interpreted validation sample. This work builds great expectations for applications in other areas with contrasted climate, topography and land cover.

Designation of Flood Risk Zones Using the Geographic Information System Technique and Remote Sensing Data in Wasit, Iraq

Floods are a great concern for people and infrastructure, and this is an issue which has increased in several regions around the globe in recent years. This study aims to evaluate flood risk areas and create a flood risk map using integrated remote sensing data and a geographic information system (GIS) in the Wasit governorate – eastern Iraq. Specifically, GIS‑based multi‑criteria analysis (MCA) was used to map flood hazard areas using a four‑criteria layer which is as follows: flow accumulation, slope, rainfall, and elevation. These four layers are standardized and combined using the overlay approach in ArcGIS software and a final map was produced. The study area was divided into five zones based on the results map, namely: very low, low, medium, high, and very high, according to the flood risk area. The resulting map indicates that over 60% of the study area is likely to experience a high and very high level of propensity of flooding. This study could be useful for government planners and decision‑makers to predict potential flooding areas and enhance flood management plans.

Determination of the Flood Risk Maps: Case Study-Kirazlı Creek (Trabzon-Vakfıkebir)

Bu çalışma kapsamında, proje alanı olarak seçilen Trabzon İli, Vakfıkebir İlçesi Kirazlı deresi için Hydrologic Engineering Center-River Analysis System (HEC-RAS) yazılımı ile kurulmuş 1 boyutlu ve 2 boyutlu hidrodinamik modellerle, Q500 debisi kullanılarak, elde edilmiş taşkın tehlike ve derinlik haritaları doğrultusunda en riskli durumu belirleyebilmek için taşkına maruz kalan alanların yer aldığı binalarda nüfus analizi yapılmıştır. Çalışma alanının Doğu Karadeniz Bölgesinde seçilme nedeni, hem meteorolojik olarak fazla yağış alması hem de topoğrafik açıdan taşkın riski taşımasıdır. Daha önce hazırlanan taşkın tehlike ve derinlik haritaları doğrultusunda; taşkından etkilenen insan sayısı, sosyo-ekonomik aktiviteler ve olası ekonomik zararların tahmin edilmesi amaçlanarak taşkından etkilenebilecek yapıların risk durumunu belirlenmeye çalışılmıştır. Taşkın risk durumu belirlenirken Geographic Information System (Arc-GIS) programı ile taşkın derinlik haritasında yer alan konutlar, fabrika, sera alanlarının, konumları ve o bölgede yaşayan nüfus sayısının belirlenmesinin ardından yapıların cinsine göre ekonomik zarar hesaplanmıştır. Yapıların toplam ekonomik zarar değerinin yapıların kendi alanlarına bölünmesiyle taşkın birim zararı hesaplanarak, hesaplanan bu değerlerin birim metrekareye düşen maksimum ve minimum zarar değerleri 0 ile 1 arasında normalize edilmiştir. Nüfus risk durumu içinde yine aynı metot kullanılarak, normalize edilmiş birim taşkın zararı ile nüfus toplanarak taşkın risk durumu belirlenmiştir. Taşkın risk yüzdelerinin belirlendiği bu çalışmada, taşkından etkilenen yapıların durumuna göre de kabul yöntemiyle yapılan standart sapma analizi sonucu tehlike risk dereceleri belirlenmiştir. Bu kapsamda yapılan çalışma sonucu, taşkın zararlarının etkilerini en aza indirmek maksadıyla derenin belirli kısımlarda gerekli taşkın kontrol yapıları ile mevcut sanat yapılarının yeniden yapılması veya iyileştirilmesi ve derenin düzenli olarak yılda bir defa yatak temizliğinin yapılmasının faydalı olacağı düşünülmektedir.

Stakeholder perceptions in flood risk assessment: A hybrid fuzzy AHP-TOPSIS approach for Istanbul, Turkey

As an initial exploration, preliminary studies which are conducted for the identification of key points to be addressed in flood risk management (FRM) processes are crucial to mitigate potential impacts of floods. Generating the flood risk maps with the participation of diverse stakeholders at each level of administration is essential to develop effective FRM strategies. Hence, the objectives of this study are twofold: i) to produce district-based vulnerability, hazard, and flood risk maps for Istanbul with a hybrid fuzzy AHP-TOPSIS model, ii) to generate these maps by considering the perceptions of different stakeholders separately, which is an initial attempt in the literature, ensuring the comparative analysis of stakeholder perceptions in FRM. Local and metropolitan municipalities, disaster management and coordination centres, water and sewerage administrations, and universities were considered as the leading stakeholders since they are chiefly responsible decision-making bodies in FRM practices in Istanbul. Pearson's correlation coefficient and Spearman's rank correlation coefficient tests were used to obtain a more accurate understanding of the agreement levels between stakeholders. The results revealed the need for the involvement of various stakeholders to generate flood risk maps since significantly different perspectives were observed; and the need for changing the generated flood risk maps. The findings of this study are critical because generated maps show distinct differences according to the mentality of the organizations and experts, which inevitably change the most flood-prone areas and possible mitigation investments.