Introduction
Floods are the most common type of natural disaster worldwide, causing significant environmental and socio-economic consequences (Heidari, 2014), increasingly affecting many people and their activities. In recent decades, the world has been affected by severe floods (Pinos & Quesada-Román, 2022). Some of the deadliest floods have occurred in China (1935, 1931, 1887), Guatemala (1949), Bangladesh (1974), Venezuela (1999), Iran (1954), India (2013), Japan (1953), and Peru (1941) (Milliman & Farnsworth, 2011). Flood caused by heavy rainfall is one of the most challenging types (Aslani & Mehdipour, 2015). These natural disasters can be properly monitored using modern technologies and information systems (Giordan et al.,2018), including geographic Information Systems (GIS), remote sensing, and multi-criteria decision analysis (MCDA) (Pahlavani et al., 2017). 
A flood is a sudden increase in water levels that exceeds the capacity of the river channel. This sudden increase causes the water to move out and flow into floodplains and surrounding lands, resulting in damage to residential areas and agricultural lands and endangering people’s health (Rezaei-Moghaddam et al., 2015). Flood risk zoning is an important process in water resource management and flood damage prevention. This process involves identifying flood-prone areas and assessing the associated risks so that appropriate planning can be made to reduce damage and increase community resilience. The Flood Hazard Index (FHI) is a practical and systematic parameter for assessing and managing flood risks. This index helps decision-makers to identify areas with high flood risk and design appropriate preventive and management measures.
Various studies have been conducted to identify flood-prone areas in Iran (Abedini & Fathi, 2015; Ghorbanzadeh et al., 2017; Azizi et al., 2021; Esfandiari Darabad et al., 2021; Mirzaei et al., 2025). Hatami Nejad et al. (2017) used the analytic hierarchy process (AHP) method and 11 indicators, including aquifers, climate, vegetation cover, elevation, soil, distance from the waterway network, geological formations, land use, evaporation, rainfall, and temperature, to zone flood risk in Izeh County, Iran, using a GIS. Their results showed that approximately 60% of the study area was at a very high or high risk of flooding. 
Najafi and Karimi Kordabadi (2019) assessed and zoned flood risk in district one of the Tehran metropolitan area using a hybrid AHP-FUZZY model with an emphasis on urban security. They identified very low, low, and medium risk zones in the north and center of the study area, as well as very high and high risk zones in the outlets and within urban areas. Hosseini (2021) used the AHP and flood-affecting indicators, including slope, curve number, distance from the river, watercourse erosion potential, elevation-slope-wetness index, slope direction, rainfall, and elevation, to zone flood risk in the Amughin watershed, Ardabil Province, Iran. Their results showed that 64% of the area was at high flood risk. In another study, Habibnejad Roshan et al. (2023) used the AHP model and flood-affecting indicators to identify areas with high flood risk in the Karun River watershed. Their results showed that about 10% of the watershed was located in the area with high and very high flood risk. 
Hosseini et al. (2023) evaluated flood risks in the Hamoon-Jazmurian watershed using a fuzzy model and AHP and 13 flood-affecting indices, including height, slope, slope direction, topographic curvature, drainage density, distance from the watercourse, geology, vegetation, land use, rainfall, topographic wetness index, steam power index, and soil type. Their results showed that the risky areas were located near the main watercourses. Daneshparvar et al. (2021) used the AHP and the analytic network process (ANP) to zone flood risks in the Sabalan Dam watershed in Ardabil Province. Their results showed that in both methods, the elevation and slope factors had the greatest impact, while the number of curves and distance to the river had the least impact. Hassanloo et al. (2019) also conducted flood risk zoning in Maneh and Samalgan counties in northern Khorasan province.
There are also several related studies conducted in other countries. Nyarko (2000) integrated a hydrological model (modified rational model) in GIS software by the arithmetic overlay operation method to determine flood risk zones in Accra, Ghana, and its environs. Their results showed that the high flood risk zone covered 35.66% of the study area, while the low risk zone covered 26.85%. Samanta et al. (2016) used an MCDA to analyze flood risk in the Markham River, Morobe Province, Papua New Guinea. They suggested that the use of MCDA in GIS techniques is very useful for accurate and reliable flood risk analysis and mapping. Seejata et al. (2017) employed spatial analysis in a GIS environment, utilizing the AHP, to assess flood risk zones in Sukhothai Province, Thailand. The results showed that the Muang, Kongkrailat, Khirimat, and Sisamrong districts were flood-prone areas. 
Echogdali et al. (2018) used the watershed modeling system (WMS) and FHI models to map the floodplain of the El Maleh basin located in southeastern Morocco. The use of the WMS model allowed for the accurate mapping of the flood risk areas with precise flood heights at different levels. However, this method was only applicable for a small portion of the basin located downstream of the hydrological station. Swain et al. (2020) conducted a study using AHP, GIS, remote sensing, and Google Earth Engine software to investigate the flood-prone area of ​​Bihar, India. The results showed that the highest and lowest flood susceptibility indices were attributed to hydrological (0.497) and human (0.037) factors, respectively. Also, about 12% of the region had very low flood susceptibility, and about 40.36% had high to very high flood susceptibility.
Karymbalis et al. (2021) used GIS-based MCDA to zone flood risks in the Megalo Rhema river watershed in eastern Attica. Their results indicate that about 22.7% of the total catchment area belonged to the high-risk flood zone, and about 15% was at very high flood risk. Ikirri et al. (2022) performed flood risk zoning using the FHI method in a GIS environment based on MCDA and considering seven parameters in the Taguenit Wadi watershed, southern Morocco. The results showed that about 28.67% were located in high and very high risk areas, and about 40% in low and very low risk areas. The aforementioned studies confirm the use of the AHP, MCDA, and GIS methods in identifying flood-prone areas. These studies generally emphasize the importance of creating flood risk maps for managing and reducing flood damage.
The Seymareh river watershed, located in Ilam province, southwest of Iran, is vulnerable to heavy rainfall and is a flood-prone area. This watershed was flooded in 2015, 2016, 2018, and 2019, causing extensive damage to infrastructure and rural residential areas. As part of the Seymourh watershed, Lowmar has faced climate challenges including heavy rainfall and rising temperatures. These changes could have significant impacts on the lives of local residents, agriculture, and infrastructure. Therefore, planning to manage water resources and reduce the risks of flash floods in this region is essential. In this regard, this study aims to identify areas prone to floods in the Lowmar sub-watershed of Seymareh and present a comprehensive flood hazard map, using the FHI and MCDA methods. This is the first study that combines FHI with MCDA. This research, covers the specific needs of Lowmar sub-watershed and addresses practical applications and appropriate results in the field of flood risk management.

Materials and Methods

Study area
The Seymareh watershed is one of the headwaters of the Karkheh River, located in the southwest of Iran and in the Zagros Mountain Range. Among the important areas in this region are the towns of Valiasr, Lowmar, and Kolm. The study area, with an area of ​​1131 km2, is part of the Seymareh watershed, which starts from the confluence of the Seymareh and Cherdavel rivers in the north of Lowmar town in Ilam province and ends in the south at the Seymareh Dam (Figure 1). The long-term average annual rainfall and temperature at Lowmar station are 412.8 mm and 20.9 °C, respectively. 

Flood risk assessment 
The FHI was used to identify areas vulnerable to flood risk. It is based on the MCDA of the AHP which considers eight hydrogeomorphic-climatic parameters including elevation, rainfall, slope, drainage network density (DND), distance from river (DFR), normalized difference vegetation index (NDVI), land use, and lithology (Abedini & Fathi, 2015). The selection of these factors was theoretically based on their relationship to flood risk. Each of the eight factors was classified based on the degree of impact on flood risk. The final flood risk map is prepared based on a combination of climatic and physical factors and elements (Esfandiari Darabad et al., 2021). Figure 2 presents a diagram of the steps involved in flood risk assessment.

Distance from the river 
The areas that are close to rivers are more prone to flooding in both normal and flash floods in the river basin, because water flows from higher elevations and accumulates at lower elevations. The areas close to other terrestrial water bodies, such as ponds, dams, and lakes, are also likely to be flooded in the event of heavy rains (Reager, 2014). The classification of DFR was estimated and performed using the Euclidean distance tool in the ArcGIS 10.3 environment and using a 1:50,000 scale topographic map (Figure 3).

Elevation
Elevation plays an important role in the spread and depth of floods in the downstream basin (Stieglitz et al.,1997). The elevation map of the study area was estimated from a digital elevation model (DEM) with a resolution of 30×30 m in the ArcGIS environment. The map is plotted in Figure 3, which shows that the highest elevation was in the northwest of the region.

Slope
Slope is an important indicator of a surface area that is highly susceptible to flooding (Regmi et al., 2010). A steeper slope indicates the influence of topography on flooding (Cai et al., 2021). Water flows from higher elevations to lower areas, so slope affects the amount of surface runoff and infiltration (Rezaei Termehe et al., 2018). In this study, the slope map was prepared from the DEM. The map is shown in Figure 3, indicating that the highest slope was in the west of the area.

Land use
Land use directly or indirectly affects some hydrological factors such as runoff production, infiltration, and evapotranspiration (Rahmati et al., 2016). In other words, it can cause flooding in the basin by increasing the levels of barren land and similar uses, resulting in more water consumption and infiltration by increasing the levels of garden and similar uses, and plays an effective role in reducing discharge (Esfandiari Darabad et al., 2021; quoted by Rezaei Moghadam et al., 2014). In this study, land use maps were obtained from Landsat 8 satellite images (for 2021) using ArcGIS software (Figure 3) and supervised classification. For this study, land use types were divided into five different categories depending on the type of use.

Vegetation
Using Landsat 8 satellite images, a vegetation map was prepared in the ArcGIS environment (Figure 3), which was classified into areas with vegetation and without vegetation.

Rainfall
Rainfall is one of the most important factors affecting flood intensity (Adiat et al., 2012). Rainfall is the main source of surface runoff. The intensity and volume of channel discharge are largely controlled by rainfall (Hadian et al., 2022). In this study, the rainfall map was prepared by using the meteorological data for a period of 20 years (2002-2022) obtained from the Sarabeleh and Lowmar synoptic stations. The map is shown in Figure 3, indicating that the highest rainfall occurred in the western part of the region.

DND
Flood runoff varies directly with the square of the DND. In other words, the higher the DND, the greater the flood runoff (Carlston, 1963). DND is defined as the ratio of the length of the drainage network (in kilometers) to the catchment area (in square kilometers). In this study, the DND map was obtained from a 1:50,000 scale topographic map of the National Mapping Organization (Azadtalab et al., 2019). For this study, DND types were divided into five different classes that correspond to drainage networks and their adjacent areas.

Lithology
In this study, the lithology map was prepared from the 1:50,000 scale lithology map of the Geological Survey of Iran data. The geology of the region was classified into 10 classes according to the characteristics of the region. The score of each class was evaluated based on expert opinion. 

Relative weight assessment
The weight of the factors applied for flood risk zoning in the Lowmar sub-watershed was determined using the AHP model. The AHP is one of the most comprehensive systems designed for decision-making with multiple criteria, because this technique allows the problem to be formulated hierarchically and considers various quantitative and qualitative criteria in the problem (Ghorbanzadeh et al., 2017). This technique analyzes and weights parameters and helps researchers make better decisions about flood management. This method is based on pairwise comparisons and allows for relative evaluation and prioritization of options (Kamaruzzaman et al., 2018; Chen, 2016). The steps of AHP include: problem and goal definition, hierarchy construction, pairwise comparisons, weight calculation, consistency analysis, and best decision selection (decisions with the highest weight). The AHP pairwise comparison method is often used to determine the weights of the main criteria and sub-criteria. This method helps all decision makers to weigh their opinions and rank complex criteria (Ikram et al., 2020).
The eight factors were hierarchically constructed in Table 1. The applied matrix was ​​8x8 and the diagonal elements were equal to 1. The normalized weights for flood risk factors are presented in Table 2. Rainfall, vegetation, slope, and land use were identified as the most relevant factors in the FHI.
There are two main AHP consistency tests: Consistency index (CI) and consistency ratio (CR). The CI is obtained as: (λmax-n)/(n-1); the closer the λmax (maximum value of the comparison matrix) is to n, the better the consistency and the smaller the CI value. The CR is obtained as CI/RI, where RI is the consistency index of a random matrix that randomly generates a cross-correlation matrix and is dependent on the order. When the order of n is larger, the value increases. When the CR value is ≤0.1, the consistency of the matrix is ​​acceptable (Chen, 2020). Table 3 provides the RI and n values used to calculate the CR. For eight factors, the λmax was ​​8.897 and RI was 1.41. The CR was obtained as 0.09, which is less than 0.1; therefore, the consistency of the matrix was acceptable. The FHI was then calculated as Equation 1 :
1. FHI=[0.172×(rainfall)]+[0.224×(slope)]+[0.142 ×(land use)]+[0.075×(Lithology)]+[0.092×(DND)]+ [0.101×(DFR)]+[0.171×(NDVI)]+[0.021×(elevation)]

Results
Figure 4 shows the flood risk map of the Lowmar sub-watershed. The map illustrates five distinct classes of FHI, ranging from very low to very high, based on the AHP model. Based on the results presented in Table 4, 15.53% of the study area was at very high risk, 21.15% at high risk, 25.48% at moderate risk, 22.30% at low risk, and 15.53% at very low risk. The names of the different areas within each flood risk zone, along with the proposed measures, are listed in Table 5. Most of the flood-prone areas were located in the western part of the sub-watershed. in the Seymareh watershed, 45 of 111 villages (40.5%) and 3 towns were located in areas at high and very high flood risk, and roads in the western part of the watershed were located in areas with high flood risk (Figure 5). The results of this study are consistent with the findings of Eguaroje et al. (2018), given that important factors (average slope, annual rainfall, vegetation, and land use) had the greatest impact on flood risk severity.

Discussion
This study examined flood risk zoning in the Lowmar sub-watershed of Seymareh river by a combination of FHI and AHP methods in the GIS environment. More than 60% of the sub-watershed area had moderate to very high susceptibility to flooding, which is consistent with the results of other studies. Hatami Nejad et al. (2017) also reported that about 60% of the study area in Izeh County was at very high and high flood risk. This consistency indicates a common pattern of vulnerability in the Zagros mountainous regions, which are due to similar topographic, climatic, and hydrological characteristics. Rainfall, slope, lack of vegetation, and land use were the most important factors affecting flood risk in the region. These results are consistent with the findings of Eguaroje et al. (2015) who confirmed the key role of slope, rainfall, and vegetation in increasing flood risk. Hosseini (2021) also reached similar results in the Amughin watershed. The high weight of the slope parameter in our study is considerable because steep slopes in the western and central regions of the sub-watershed increase runoff velocity and reduce concentration time. Cai et al. (2021) also emphasized that elevation and steep slope indicate a strong influence of topography on flood occurrence. The concentration of high-risk areas in the western and central parts of the sub-watershed is consistent with the rainfall pattern, topography, and DND. Consistent with our results, the study by Karymbalis et al. (2021) and Hosseini et al. (2023)’s study in the Hamoon-Jazmurian watershed, showed that high-risk areas are located near the main waterways. The location of 40.5% of villages and three towns (Lowmar, Sarab-Kalan, and Siah-Siah) in high and very high risk areas indicates serious vulnerability of human settlements. This result is consistent with the findings of Swain et al. (2020) in Bihar, India, who reported 40.36% of the area was at high to very high flood risk.
The high weight of vegetation and land use in our study demonstrates the importance of land management in reducing flood risk. Rezaei-Moghaddam et al. (2015) also showed that changes in land use and land cover have a direct impact on the hydrological regime of the basin. Reduction in vegetation cover and increase in barren areas cause increased surface runoff and flooding. Rahmati et al. (2016) emphasized that land use directly or indirectly affects hydrological factors such as runoff production, infiltration, and evapotranspiration. Therefore, proper land use planning and increasing vegetation cover can play an important role in reducing flood risk. The increase in the frequency and severity of floods in recent years (2015, 2016, 2017, and 2018) in the Lowmar region is consistent with the global trend of climate change. Pinos and Quesada-Román (2022) reported that climate change has increased the occurrence of extreme rainfall events worldwide. The average annual rainfall of 412.8 mm at Lowmar station, along with the steep topography, provides favorable conditions for flash floods.
Ikirri et al. (2022) also used the FHI method for flood risk zoning in the Taguenit Wadi watershed, southern Morocco, and suggested that this method is an efficient tool for flood risk zoning in case of lack of hydrometric data. however, it requires datasets with high resolution and accuracy, which may not be readily available. Additionally, this model does not consider social, economic, and cultural factors. However, information obtained from this model can help increase public awareness of flood risks and how to manage them. Therefore, it is advisable to utilize alternative models for flood risk zoning in watersheds. Najafi and Karimi Kordabadi (2020) used a hybrid AHP-FUZZY model that allows for consideration of uncertainty. Habibnejad Roshan et al. (2023) also used the AHP model, which better models the interrelationships between criteria. However, the simplicity and applicability of the FHI method in data-poor conditions make it a suitable tool for initial evaluation. Combining the FHI method with AHP in a GIS environment, as used in our study, is also an efficient method for flood risk zoning in data-poor conditions. However, to improve the accuracy and comprehensiveness of the assessment, future studies are recommended to use combined methods, hydraulic models, and hydrometric data, and that consider socio-economic factors.

Conclusion
In this study, flood risk zoning in the Lowmar sub-watershed within the Seymareh watershed in Illam Province, Iran, was performed using the FHI-MCDA method to determine the extent of flood-prone areas that should be considered in future land use plans. More than 60% of the sub-watershed area had moderate to very high susceptibility to flooding, especially in the central and western parts. Additionally, 45(40.5%) villages and three towns in the Seymareh watershed were found to be located in areas with high or very high flood risk. The roads in the western part of the Seymareh watershed were located in an area with high flood risk. Approximately 37% of the watershed area was classified as having a very low to low flood risk, primarily located in the southeast and north. The factors that most significantly affected flood risk severity were rainfall, slope, lack of vegetation, and land use. Topography could also be effective, considering the condition of the valleys and their effect on the spread of floods in the watershed. 
The following recommendations are provided for reducing potential flood risks in the study area:
- Hydrometric data collection: Establishing hydrometric stations to collect accurate and up-to-date data that helps improve the accuracy of the FHI model
- Strengthening land use planning: Using the results of this study in land use design and planning, especially in areas at risk, to reduce damage
- Increasing vegetation cover: Implementing programs to increase vegetation cover can help reduce the negative effects of rainfall and floods
- Education: Conducting educational courses for local communities about flood risks and preventive measures to increase their awareness
- Research and development: Invest in further research to improve flood prediction models and better understand the factors affecting their occurrence
Economic loss estimation: Accurate assessment of economic losses caused by floods for better planning and damage reduction.

Ethical Considerations

Compliance with ethical guidelines
All ethical principles were considered in this study.

Funding
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Authors' contributions
All authors contributed equally to the conception and design of the study, data collection and analysis, interpretation of the results, and drafting of the manuscript. Each author approved the final version of the manuscript for submission.

Conflicts of interest
The authors declared no conflict of interest.




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