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16-25 

16 

 

 

 

Article 

Advancing urban flood mitigation and climate 

resilience: a GIS-based hydrodynamic modeling 

approach using HEC-RAS and remote sensing data 
Navid Navidpour1, Mohammad Reza Yari2, Faraz Estelaji3*, Amir Maleki4, Bita Rouhi Asl5, 

Sahand Heidary6 

1Department of Computer Engineering, faculty of software engineering, Amirkabir University of Technology, Tehran, 

Iran 
2Department of Construction Engineering and Management, Faculty of Civil Engineering, Iran University of Science and 

Technology, Tehran, Iran 
3Department of Construction Engineering and Management, Faculty of Civil Engineering, Khajeh Nasir Toosi University, 

Tehran, Iran 
4Department of Civil, Water, and Environmental Engineering, Shahid Beheshti University, Tehran, Iran 
5Department of Environmental Management, Faculty of Marine Science and Technology, Azad University-North Tehran 

Branch, Tehran, Iran 
6Faculty of Computer Engineering, Khajeh Nasir University, Tehran, Iran 

               A R T I C L E   I N F O 
 

Article history: 
Received 10 January 2025  
Received in revised form 
15 February 2025 
Accepted 01 March 2025 
 
Keywords:  
Flood hazard evaluation, Hydrological modeling, 
HEC_RAS modeling, River flow management, 
Climate change 
 
*Corresponding author 
Email address: 
faraz.estelaji.1996@gmail.com 
 
 
 
DOI: 10.55670/fpll.fusus.3.2.3 
 

A B S T R A C T 
 

Over the past decades, significant adverse effects, including the resiliency of 

critical centers, have emerged. The negative impact has manifested in the 

vulnerability of critical urban centers during natural disasters and emergencies, 

leading to their inefficiency, heightened public dissatisfaction, and a breakdown 

in service delivery during crises. In order to enhance the resilience of key 

centers, it is essential to first identify and assess the vulnerability of these 

crucial urban hubs to various risks and threats. In this research, the 

classification was graded and assessed following the formulation of a 

questionnaire and the collection of results. Utilizing the arithmetic mean of 

sample opinions, the Analytic Hierarchy Process (AHP) was applied through the 

Expert Choice software to assign weights to these criteria and sub-criteria. 

Subsequently, the key urban centers were identified. The hydrology within the 

city and its surroundings, along with the modeling of rivers during various 

return periods, were studied using the HEC_RAS. The results were then 

integrated into GIS to delineate flood risk zones in the city of Hamedan. 

Following the input of the arithmetic mean of sample opinions into the Expert 

Choice, the value of each indicator was meticulously determined. This 

delineation indicates that the quantitative level indicator benefits from the 

maximum weight, while the economics of assets hold the least weight in the 

assessment. Ultimately, by aligning key urban centers with flood-prone zones 

in the GIS framework, vulnerable centers were enumerated. The method used 

in this study can be extended to all cities based on their river flow modeling and 

urban zoning. 

 

 

 

 

 

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Open Access Journal 

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May 2025| Volume 03 | Issue 02 | Pages 16-25 

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ISSN 2995-0473 

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N. Navidpour et al. /Future Sustainability                                                                                     May 2025| Volume 03 | Issue 02 | Pages 16-25 

17 

 

1. Introduction 

Major cities, being the most densely populated nuclei, 

accommodate the highest levels of human resources, 

investments, economic projects, and infrastructure [1]. Due to 

reasons such as excessive concentration, non-principled land 

use, disregard for accessibility standards (building and road 

compatibility), the presence of multiple bridges, neglect of 

river buffer zones, limited attention to climatic elements like 

prevailing winds, precipitation in constructions, and even the 

lack of balanced and standard access distances in the 

distribution of emergency and rescue centers such as 

hospitals and fire stations, these cities are exposed to 

numerous threats and substantial damages [2]. These 

challenges, especially during crises, can significantly impact 

the lives of citizens. Therefore, taking appropriate measures 

within the framework of urban management plans and 

adhering to the principles is imperative [3]. Given recent 

climatic changes and the damages caused by floods and 

droughts, examining this issue holds great importance [4]. As 

floods typically affect, areas adjacent to rivers and economic 

activities, as well as human settlements, are often 

concentrated near rivers today, studying these regions in 

terms of flood-prone parameters and preparing flood zoning 

maps is essential for urban planning [5]. Floods are defined 

when water overflows its natural bed, inundating low-lying 

areas and riverbank territories, causing financial and human 

casualties [6]. Identifying and grading assets based on their 

importance for the continuity of urban life and the urban 

system's resilience in the face of natural and human-made 

crises facilitate crisis management processes. This resilience 

enables the urban system to meet city needs during crisis 

conditions, thereby easing the management of crises resulting 

from both natural and human-made disasters. The phase of 

recognizing and prioritizing assets in the case of the city is one 

of the fundamental steps in this research.  

2. Literature review 

Considering the principled and scientific design of the 

surface water collection network is very important as one of 

the most important components of the social welfare system 

of citizens, and for this system to function optimally to solve 

the problem of flooding in one of the rainiest cities in the 

country, different parts of Rasht can be collected and directed 

to the inlet channel by constructing a structure in appropriate 

locations that meet the required standard size. The runoff that 

flows from impervious surfaces can be properly collected and 

directed to the outlet, thereby solving the problem of localized 

flooding in most places. Of course, this requires the proper 

design of the urban drainage network along with an 

examination of the river flood level at the discharge site [7]. 

In a study aimed at evaluating the HEC-RAS model in flood 

prediction of the Qorveh watershed in Kurdistan province, 

various effective parameters in the hydrological modeling of 

this basin were studied. Then, using the HECRas sub-model 

and the US Soil Conservation Service (SCS) and Schneider 

methods, the basin flood hydrograph was simulated and then 

calibrated and verified. It was determined that the SCS 

method was more consistent with observational data in 

simulating the peak discharge of the hydrograph [8]. In this 

paper, flood zoning was studied using a hydraulic model of 

river analysis in the Manshad watershed of Yazd province. In 

this regard, with the aim of integrating the HEC-RAS hydraulic 

model with ArcGIS software through the HEC-RAS extension, 

the flood extent was calculated for return periods of 2 to 200 

years in the riverside lands with the help of digitized 

topographic maps. Finally, the area of each of the land uses at 

risk of flooding with a return period of 200 years was 

determined, and the most at-risk land use was related to 

agricultural lands [9]. In the list of most natural hazards, 

floods are in the first place. Increased rainfall has made 

surface runoff disposal a critical problem in urban areas. 

Various hydraulic models have been developed to measure 

the amount of runoff in urban areas [10]. 

The critical crisis areas in the urban structure against 

floods and inundation were identified, and the damages 

caused by floods were presented in the form of a damage 

assessment map [11]. In another investigation, flood zoning 

was carried out using hydraulic modeling in the province of 

Yazd, specifically in the Manshad plain. HEC-RAS was 

employed in this study, revealing that the integration of 

geographic information systems with the HEC-RAS model 

facilitates calculations, reduces field operations, and is highly 

recommended for application in watersheds [12]. Ouma et al. 

[13] utilized building characteristics and their surrounding 

environment as fundamental information for assessing 

vulnerability to floods. They establish a direct relationship 

between the type of construction and building vulnerability. 

Bloemen et al. [14] emphasized the crucial role of planning, 

decision-making processes, and appropriate technical 

execution in flood risk management. However, the often-

overlooked technological aspects of building resilience to 

floods can significantly contribute to vulnerability reduction 

and flood risk management. 

Bennett and Blamey [15] introduced a novel method that 

not only studies vulnerability but also incorporates economic 

assessment and valuation for historical buildings. Their 

approach assesses vulnerability by examining the exposure of 

historical buildings to floods, considering factors such as 

architectural form, preservation, and archaeology. Albano et 

al. [16] highlighted the importance of vulnerability analysis 

for elements exposed to flood risks and the subsequent 

implementation of hydrological risk reduction strategies. 

Their study focuses on assessing economic and social 

damages through modeling, examining the intensity trend, 

the impact on exposed elements, and the physical response of 

buildings. 

Qiu et al. [17] employed a cellular-based metric system 

to evaluate urban resilience in Dalian City, China, against 

floods. They simulate floods using the CADDIES model and 

identify vulnerable areas within the city's 31 sub-watersheds. 

Their study emphasizes the effectiveness of the new metric in 

reflecting the system's performance changes, aiding the 

selection of tailored strategies for enhancing resilience. 

Karber [18] delved into urban design principles for flood 

resilience in the Mekong Delta, Vietnam. Instead of focusing 

on post-flood damage, they propose alternatives for proactive 

adaptation to flooding, drawing insights from local ecological 

knowledge in rural areas and emphasizing the use of local 

capacities for urban areas. 

Eakin et al. [19] explored the resilience of urban 

structures in Mexico City against climatic hazards, 

particularly floods. They underline the complexity and 



N. Navidpour et al. /Future Sustainability                                                                                     May 2025| Volume 03 | Issue 02 | Pages 16-25 

18 

 

sequenced nature of building resilience, influenced by social, 

economic, and institutional conditions. Harirchian et al. [20] 

suggested the use of an intelligent system for assessing and 

retrofitting buildings prone to flood damage. This system, 

comprising vulnerability assessment, retrofitting options, 

and fundamental damage assessment subsystems, aids in 

calculating the vulnerability of structures and recommending 

appropriate retrofitting strategies based on the degree of 

vulnerability. 

3. Methodology 

Hamedan Province spans approximately 19,546 square 

kilometers, situated in the western mountainous region of 

Iran [21]. Its geographical coordinates range from 2 degrees 

33 minutes to 2 degrees 38 minutes north latitude and 2 

degrees 45 minutes to 2 degrees east longitude. The most 

populous city within the province is Hamedan city, positioned 

at an elevation of 1,870 meters [22]. The precise geographical 

location of Hamedan city is illustrated in Figure 1. 

 

 
Figure 1. Geographical location for Hamedan city 

The current investigation falls within the realm of 

applied research, aiming to enhance the state of a 

phenomenon in Hamadan city. The research methodology is 

rooted in theoretical objectives, adopting a descriptive-

analytical approach. In the descriptive phase, library and 

documentary studies were employed to compile the 

necessary information and data [23]. The statistical 

population encompasses experts, activists, and professionals 

specializing in the city's natural risks. These individuals were 

selectively sampled using a purposive sampling method, 

chosen for their suitability in gathering specialized and 

precise data on the research subject. A questionnaire was 

employed with the goal of identifying specific criteria and 

sub-criteria directly influencing the assessment of 

endangered assets. Expert Choice software facilitated the 

prioritization of these criteria, sub-criteria, and strategies. 

The integration of these procedures, along with the 

simultaneous execution of quantitative and qualitative 

analyses, positions the current study within the realm of 

mixed research [24].  

The assessment of the weight of influential resilience 

indicators is conducted through a comprehensive five-stage 

process focusing on flood-vulnerable key centers. 

3.1 Determination and identification of assets 

Prioritization of key centers in the face of impending 

threats is accomplished based on criteria and indicators 

categorized into three levels: life, sensitivity, or importance 

[25]. Quantification of qualitative criteria and indicators for 

key centers is achieved. Table 1 shows criteria, sub-criteria, 

and indicators. 

Table 1. Scores of the main criteria of the grading matrix [26] 

Considerations grade The main criteria No 

1 sub-criterion and 5 
quantitative indicators 

10 Fundamental 
importance 

1 

3 sub-criteria and 10 
quantitative indicators 

20 Scope of influence 2 

1 sub-criteria and 7 
quantitative indicators 

20 Possibility of 
replacement 

3 

5 quantitative indicators 6 to be unique 4 

3 quantitative indicators 14 Role-Playing 5 

2 sub-criteria and 7 
quantitative indicators 

5 Capital value 6 

3 quantitative indicators 10 Consequences of 
injury 

7 

- 100 - Total 

 

 

3.2 Weighting of the main criteria of the Ministry for 

Asset Leveling and Evaluation 

After selecting the effective criteria in zoning in order to 

combine them together as information layers, the weight of 

each criterion must be determined in proportion to their 

importance according to one of the weighting methods. Given 

that among the selected criteria, some are quantitative, and 

some are qualitative, we must use a method that allows us to 

compare and weigh quantitative criteria with qualitative 

ones, which is one of the problems of weighting in multi-

criteria decision-making problems. The weight given is given 

as a number in the evaluation, which indicates the relative 

importance of that criterion compared to other criteria. This 

method has found many applications in social and economic 

issues and has also been used in urban management in recent 

years. In this research, the Analytic Hierarchy Process (AHP) 

method was used to weight the criteria. The Analytic 

Hierarchy Process (AHP) method was founded by Saati in 

1977. The method is based on performing pairwise 

comparisons and determining the degree of preference of 

elements over each other with respect to the desired criteria 

and is used to solve multi-criteria evaluation problems and 

determine the priority of multiple options with respect to the 

desired criteria [27]. Utilizing the indicators outlined in Table 

2. 



N. Navidpour et al. /Future Sustainability                                                                                     May 2025| Volume 03 | Issue 02 | Pages 16-25 

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The results are classified into one of three categories: critical, 

sensitive, or important. This classification scheme, proposed 

by Troy et al. [28], provides a comprehensive overview of the 

criteria used to determine the critical, sensitive, and 

important classifications for the assessed assets.  

Table 2. Classify different levels based on the final score 

 

3.3 Using the HEC_RAS model 

This model is widely used to model urban drainage 

channels [29]. GIS and HEC-RAS software have recently been 

used to display flooded areas [30]. HEC-RAS software with the 

HEC-GEORAS tool can model and display flooded areas with 

different return periods [31]. 

4. Results and discussion 

The selected assets were those whose removal would 

impact the provision of services, disrupting the primary 

functions of the city. To validate the results obtained from the 

evaluation of key assets in Hamadan city, the weighting of 

asset evaluation indicators becomes crucial. This is because 

the influence of each index is determined by its weight in 

defining the significance of an asset. Since the indicators listed 

in Table 3 do not carry equal weight, a questionnaire was 

designed and distributed, and its results were collected. The 

AHP technique in the Expert Choice software was then 

employed to calculate the weights of these indicators. 

Subsequently, the software determined the weight of each 

index separately based on the arithmetic average of the 

sample comments. The resulting weights are presented in 

Table 3, providing a comprehensive understanding of the 

relative importance of each indicator in the asset evaluation 

process. 

Table 3. Weight of asset valuation indicators 

 

 

4.1 Prioritizing important assets 

Adhering to the priority law (80/20), which posits that 

prioritizing activities based on their importance leads to 80% 

success with 20% effort, time, and resources, it is crucial to 

underscore the significance of proper prioritization. 

Neglecting key priorities can result in 80% effort, time, and 

resources yielding only 20% success in achieving goals.  

Key urban centers should be leveled based on their 

importance. The implementation of crisis management 

measures should be prioritized across three grades: 1st 

grade, 2nd grade, and 3rd grade centers. The quantification of 

qualitative criteria and indicators is essential for this leveling 

process. A table designed by the crisis management 

organization serves as the basis for prioritizing urban 

infrastructure centers in different areas, categorizing them 

into three levels: "grade 1, grade 2, and grade 3" (Table 3). 

In this research, after evaluating and assigning scores to each 

indicator, the total score is recorded in the "Score Total" 

column. Based on these scores, the level of the relevant key 

center is determined, categorizing it into one of the three 

levels: grade 1, grade 2, or grade 3. Indicators of effectiveness 

and performance level, recovery ability (reversibility time - 

recovery level), and 12 consequential effects (losses [human], 

damages [physical-real], injuries [psychological]. The 

simulation of river flow and city zoning against floods using 

the HEC_RAS model is a critical step in enhancing resilience. 

Identifying threats in a timely manner is essential for 

implementing intelligent countermeasures, thereby 

mitigating weaknesses and reducing vulnerability. By 

developing a rainfall-runoff model, design flood values are 

determined for different return periods, as shown in Figure 2. 

This comprehensive approach provides a thorough 

understanding of possible flood scenarios and helps to 

develop effective preventive measures and urban zoning 

strategies. 

Leveraging road surface numbers, a digital elevation 

map of Hamedan city was created, integrating it with the 

elevation digital map of suburban lands to obtain a 

comprehensive digital elevation map of Hamedan city and its 

surrounding suburban basins. Two key considerations are 

taken into account when determining the watershed 

boundaries: the first pertains to the area, and the second is 

related to land use within each sub-basin. Design 

precipitation, a crucial parameter in rainfall-runoff modeling, 

is tailored to the needs of the selected precipitation-runoff 

model. Four characteristics are established for design 

precipitation, including the continuity of total rainfall, depth 

of total rainfall, temporal distribution, spatial distribution of 

rainfall, and the structure of the rainfall-runoff model. This 

meticulous approach ensures the accuracy and relevance of 

the hydraulic modeling process, contributing to a 

comprehensive understanding of the hydraulic adequacy of 

the network. 

To model the basin for calculating design floods at the 

project site, a rainfall-runoff model was constructed using the 

HEC_RAS software. The model incorporates the extra-urban 

basins of Hamedan city with six sources, 24 sub-basins for 

Hamedan city, and 23 waterways representing the city's 

rivers. The flooding of suburban waterways in the model is 

considered based on the outcomes of hydrological studies. 

Precipitation loss determination and the conversion of 

precipitation to runoff are carried out using methods 

provided by the SCS soil protection organization, and river 

flood routing is conducted using the Muskingum Cunge 

method. 

 

Range of grade Rating level 

93-100 Special 1 

83-92 Vital 2 

71-82 Sensitive 3 

56-70 Important 4 

36-55 Protective 5 

Weight Asset valuation indicators Priority 

0.117 Functional value 1 

0.133 Quality level of operation 2 

0.416 Quantitative level of operation 3 

0.188 Environmental value 4 

0.073 Possibility of replacement and repair 5 

0.045 Dependence on the outside 6 

0.022 Economic value 7 



N. Navidpour et al. /Future Sustainability                                                                                     May 2025| Volume 03 | Issue 02 | Pages 16-25 

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Figure 2. Structure of rainfall-stream 

 

 

Given that water infiltration in the soil depends on factors 

such as soil type and texture, land use, and vegetation type 

and density, the amount of rainfall losses can be determined 

using these data and common methods. For the calculation of 

design floods, the 6-hour rainfall from Hamedan airport 

station was considered as the representative rainfall. This 

rainfall was adjusted according to the level of each sub-basin 

using point rainfall reduction coefficients, aligning with the 

time distribution pattern of 6-hour rainfall. In this modeling 

approach, out-of-town areas are treated as a source, and the 

design rainfall is applied to the urban lands. Table 4 provides 

an overview of the design floods entering the rivers of 

Hamedan City, reflecting the outcomes of this comprehensive 

rainfall-runoff modeling effort. 

4.2 Analysis for river modeling 

Some specifications of the river modeling are as follows: 

A) Determination of transverse sections and specifications of 

existing bridges and culverts along the flow path. 

B) Manning coefficients. 

C) Drop coefficients resulting from the expansion and 

contraction of the flow. 

D) Flow rate. 

E) Boundary conditions. 

The analysis of river flow at various times was conducted 

using HEC_RAS software. Figure 3 illustrates an example of 

the flow in Morad Beig and Divin River as an outcome of this 

modeling effort. 

 

 

 

 

Analysis of the water carrying capacity in different segments 

of Hamedan City Rivers was conducted based on the current 

conditions. The methodology involved considering the 

downstream section of each river as the zero-kilometer 

reference point, and distances to other sections were 

measured accordingly. Additionally, the general slope of the 

ground at each section location was determined using the 

city's topographical map. The capacity of each section was 

then calculated using Manning's formula. 

Figure 3 illustrates the kilometer plan of the Divin River. The 

horizontal axis represents the distance from the zero section 

(chainage), while the vertical axis depicts the water-passing 

capacity of the sections (bankful discharge). Notably, the 

graph reveals that the capacity of river sections does not 

consistently increase downstream, highlighting the 

suboptimal water-passing capacity of these channels. It's 

essential to note that design floods, including a 50-year flood, 

have also been incorporated into the figure, providing a 

comprehensive overview of the water-carrying capacity and 

potential flood scenarios in Divin River (Figure 4). 

 

 

 

 

 

 

 



N. Navidpour et al. /Future Sustainability                                                                                     May 2025| Volume 03 | Issue 02 | Pages 16-25 

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Table 4. The modeled floods going to Hamedan’s Rivers 

/sec)3Modelled flood (m Place of entering 
modelled flood 

  Node / 
Sub basin 

2-yr 5-yr 10-yr 20-yr 50-yr 100-yr Range Node 

2.20 8.90 14.60 20.30 28.20 34.20 Heidare 0.0 A04 

9.10 13.20 16.00 18.50 21.90 24.50 Abbasabad-01 11240.0 S1 

9.50 15.10 19.20 23.20 28.60 32.80 Abbasabad-01 2126.7 9C 

11.80 24.30 34.30 44.20 57.70 68.00 Abbasabad-02 2447.0 17C 

11.80 24.60 34.70 44.80 58.60 69.00 Abbasabad-02 1210.0 3C 

2.80 4.00 4.90 5.60 6.70 19.90 Park mardom 1760.9 S5 

3.80 5.50 6.60 7.70 9.10 10.20 Divin-01 6032.6 S2 

3.90 6.40 8.10 9.90 12.30 14.40 Divin-01 3424.1 20C 

0.30 1.20 1.90 2.60 3.50 4.20 Park mardom 868.8 52C 

7.70 14.20 19.70 25.20 32.50 38.20 Divin-02 3650.7 16C 

7.80 15.30 21.10 27.70 36.50 43.40 Divin-02 2280.0 21C 

8.20 12.00 14.60 16.90 20.00 22.30 Moradbeig-01 10937.9 S3 

8.50 13.70 17.50 21.10 26.20 30.10 Moradbeig-01 6100.0 22C 

8.90 15.50 20.30 25.30 32.10 37.30 Moradbeig-01 4386.2 25C 

9.30 17.20 23.10 29.50 37.90 44.50 Moradbeig-01 3286.0 24C 

9.40 17.60 23.80 30.10 39.00 45.90 Moradbeig-01 1106.0 23C 

17.30 33.20 45.50 58.60 76.70 91.00 Moradbeig-01 700.1 15C 

2.10 3.00 3.70 4.30 5.10 5.60 Faqire-01 3664.1 S6 

2.30 4.20 5.70 7.20 9.40 11.10 Faqire-01 2039.3 50C 

0.90 3.50 6.10 8.80 12.30 15.10 Etehad 1799.7 K08 

3.10 7.70 11.50 15.90 21.60 26.10 Faqire-01 2834.4 31C 

7.30 10.70 13.00 15.00 17.80 19.90 Khezr-01 6848.0 S4 

7.50 12.00 15.30 18.30 22.70 26.10 Khezr-01 3058.7 26C 

10.70 22.80 31.90 40.70 52.90 62.00 Khezr-01 1843.3 30C 

14.20 31.70 45.20 58.00 76.30 89.80 Khezr-02 2234.3 32C 

15.30 36.10 52.40 68.10 90.40 106.90 Khezr-02 1532.8 33C 

15.80 37.80 55.20 71.90 95.50 113.10 Khezr-02 0.0 11C 

 



N. Navidpour et al. /Future Sustainability                                                                                     May 2025| Volume 03 | Issue 02 | Pages 16-25 

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Figure 4. Divin River water intake capacity 

 

 

4.3 Results of river hydraulic calculations 

Examination of the longitudinal profiles reveals that in 

certain segments of Hamedan City Rivers, the current 

conditions are inadequate. During designed flood events, 

water exceeds the channel capacity, leading to road flooding. 

The insufficient capacity of sections and the inappropriate 

dimensions of bridges and culverts contribute to the inability 

of Hamedan City Rivers to safely convey floodwaters out of 

the city. 

 

 

 

 

 

 

Figure 5 depicts areas with flood potential based on the 

conducted modeling. Specific sections along each river exhibit 

inadequate cross-section capacity to accommodate the design 

flood. Notably, significant portions of the Divin and Khizr 

rivers are incapable of handling a 50-year flood. However, 

some segments within these rivers pose even more critical 

conditions than others. Addressing these challenges is crucial 

to enhancing the overall flood resilience of Hamedan city. 

 

Figure 3.  Analysis of River Crossings at Morad Beig and Divin Using HEC-RAS Software 



N. Navidpour et al. /Future Sustainability                                                                                     May 2025| Volume 03 | Issue 02 | Pages 16-25 

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4.4 Integration of assets layer and river modeling output 

in GIS platform 

The layers containing key assets of Hamedan City and the 

river conditions have been harmonized within the GIS 

platform, and a comprehensive output map has been 

generated, as illustrated in Figure 6. This map encapsulates 

the calculated results of the adaptation process, offering a 

visual representation of the interplay between critical city 

assets and the state of its rivers. 

 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 5. Flood-prone areas with no capacity for water infiltration 

were identified through hydraulic modelling 

 

5. Conclusions 

The evaluation and mitigation of urban flood 
vulnerability stand as imperative prerequisites in urban flood 
management. The vulnerability of key structures during 
natural disasters can lead to their inefficiency, exacerbating 
public dissatisfaction and hindering emergency response 
services. Floods, alongside earthquakes and droughts, hold 
the highest ranks concerning both human and financial losses 
among recognized natural disasters. Urban floods have 
intensified due to climate change, urbanization growth, and 
constraints in urban drainage infrastructure. Over the past 
decade, these floods have left significant adverse effects. 
Hence, this study focuses on urban and suburban hydrology, 
hydraulic network analysis, and modeling of the rivers in 
Hamedan city across various return periods. Utilizing the 
HEC-RAS software and integrating the results into GIS, the 
flood hazard zone of Hamedan city was delineated. Key 
vulnerable centers to floods were identified, emphasizing the 
necessity for officials to adopt and implement strategies and 
resilience models to mitigate the cascading impacts of these 
vulnerabilities. Essentially, this research serves as an 
introduction to proposing resilient solutions for urban 
structures. In the contemporary era, the convergence of 
urban life complexities on different fronts, ranging from 
natural hazards and technological crises to social and security 
crises, has diminished urban resilience. The primary focus of 
this research is to elucidate and propose resilient strategies 
for vital and sensitive urban structures, particularly during 
natural threats, especially floods. To achieve this, high-
sensitivity key centers were first identified, and their 
vulnerability to flood-related risks and threats was examined. 
Criteria and sub-criteria for asset grading and evaluation 
were established, and, after questionnaire adjustment, results 
were collected, weighted using the Analytic Hierarchy 
Process (AHP) technique in the Expert Choice software. 
Subsequently, key urban centers were determined. The 
hydrology of both urban and suburban areas, hydraulic 
network analysis, and river modeling in Hamedan City across 
various return periods were studied using the HEC-RAS 
software.  

 
Figure 6.  Modification of the asset layer in Hamedan city and the flood permeability of various sections of its rivers 



N. Navidpour et al. /Future Sustainability                                                                                     May 2025| Volume 03 | Issue 02 | Pages 16-25 

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The results were then transferred to GIS for flood hazard 
zoning in Hamedan city. Finally, by aligning key centers and 
flood-prone zones within GIS, vulnerable centers were 
identified, and the results were presented. The results 
indicate that the green-colored zones, representing key 
centers and assets, intersect with the red lines, indicating 
inadequate river passage, signifying a critical issue. In this 
region, key structures and essential infrastructure are not 
safeguarded against potential future flood hazards. 

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically with regard to authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The authors adhere to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
The manuscript contains all the data. However, more data will 

be available upon request from the authors. 

Conflict of interest 

The authors declare no potential conflict of interest. 

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[1]     Kennedy, L., Robbins, G., Scott, D., Sutherland, C., 

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