Abstract

Flooding is one of the most common natural dangers occurring almost everywhere. Remote sensing and geographic information systems (GIS) are common and effective tools for hydrological analysis assessment and hazard management. Using GIS and remote sensing techniques, this study aimed to identify flood hazard maps in the Diyala governorate with a higher vulnerability to floods. Nine influencing parameters were collected, including elevation, slope, distance from the road, distance from the river, rainfall, drainage density, land use and land cover, normalized vegetation index, and topographic wetness index. The collected data were processed using GIS software and then relative weights were estimated using the analytical hierarchy process (AHP) approach to produce a flood map. According to the findings of this study, the largest zone, about 64% of the study area, faces moderate potential flood hazard, a very small area of less than 1% faces very high and very low potential flood dangers, and approximately 35% of the study area is subjected to high and low flood hazard.

1 Introduction

Flooding has the highest potential to cause natural disasters and widespread damage, with significant impacts on affected populations. Globally, evidence shows that increasing flood rates lead to greater economic losses and affect a growing number of people (Bolstad and Manson 2008). According to the literature, floods result in over 2,000 deaths and negatively impact more than 75 million people each year (Zou et al. 2013). Disasters can be classified as human-induced, artificial, or natural (Adjei-Darko 2017). Flood risk management faces serious challenges, particularly in developing regions like Asia and Africa, due to rapid urbanization. Increased urbanization results in more impervious surfaces, leading to higher runoff rates. Urban areas are particularly vulnerable to flood hazards because they concentrate people and infrastructure in regions already committed to other land uses, further increasing their susceptibility (Kovacs et al. 2017; Rincón et al. 2018).

There are many causes of floods: coastal storms, dams, heavy rain, storm surges, ice overflow, waterway overflow, wastewater systems, or snowmelt (Ani et al. 2020). Predictable magnitudes and local flood volumes are substantially affected and limited by topographic, climatologic, and geologic factors (O'Connor et al. 2002). Many studies indicate flood impacts from climate change in various parts of the world (Maghsood et al. 2019; Al-Anbari et al. 2019). Aside from these, the most common factors triggering flood occurrence include elevation, slope, curvature, rainfall, topographic wetness index (TWI), land use land cover, distance to rivers, and the Normalized Difference Vegetation Index (NDVI) (Ullah and Zhang 2020).

In recent years, many researchers have studied flood risk analysis and management, and assessments have been carried out in different regions and cities, such as mainland cities and coastal cities (Rincón et al. 2018; Hussein et al. 2023a, 2023b) and that improved when a combination of Geographic Information System tools, remote sensing, and multi-criteria decision analysis were employed (Msabi and Makonyo 2021; Kumar et al. 2023). Many significant applications use remote sensing, such as terrain properties mapping, and flood analysis (Granger et al. 1999). GIS is computer software capable of inputting, editing, managing, analyzing, and manipulating the various data sources for mapping, managing, and assessing potential flood risk zones. ArcGIS software offers many tools for determining and modeling flood-affected areas (Adjei-Darko 2017). ESRI developed ArcGIS hydrology tools, a common tool for simulating surface streams (Aziz et al. 2020). 

Several flood risk studies use artificial intelligence models with multi-criteria decision analysis (MCDA). The most successful and useful MCDA approach is the AHP method. To determine the flood factors' weights using the AHP approach, the pairwise comparison matrix (PCM) is used to find the determine how one factor outweighs another. The advantage of the AHP method to help decision-makers includes its effectiveness in factor selection and how it is easily implemented (Hagos et al. 2022).

To prevent and mitigate future flood situations, studying the analysis and mapping of flood hazards can help in detecting the weakest regions based on physical characteristics that affect the predilection for flooding. Flood hazard mapping is a critical module of flood-prone region planning and strategies for mitigation (Bhatt et al. 2014).

The main objective of this research is to find the spatial distribution of flood hazard maps in the Diyala governorate in Iraq (a developing country) by using hazard concepts within GIS, remote sensing, and an analytic hierarchy process (AHP) framework.

2 Study area

The Diyala governorate is located between longitude (44º 22'and 45º 56) east "and latitude (33º 3'and 35 º 6') north, in the northeastern part of Iraq, as shown in Figure 1, and covers an area of 17,685 km². The area borders Iran, and shares internal boundaries from the east by the Iraqi-Iranian borders, to the west by Baghdad and Salah al-Din governorates, and from the south by Wasit governorate (Al-Hasani and Al-Mashhadani 2021). In the north, the Hamrin Mountain range crosses the governorate, giving way to desert plains in the south. On the Diyala River, the governorate constructed the Hamrin Lake dam, located about 50 km northeast of Baqubah, the capital of the Diyala governorate (Bhatt et al. 2014). The annual average precipitation and temperature are 18.39 mm and 21.51ºC, respectively. (Diyala, Iraq Climate Zone 2023).

Figure 1 Location of the study area.

3 Materials and methods

3.1 Data used

Books, journals, satellite data and other secondary sources were used in this study, as shown in Table 1 (Bolstad and Manson 2008). The elevation, slope, and drainage density maps were produced using the Digital Elevation Model (DEM). A LULC map was generated using Landsat 8 satellite imagery acquired for 2022. Precipitation data for two meteorological stations were obtained from the Iraqi Agrometeorological Network, and distance to river and distance to road maps were created using shapefiles.

Table 1 Data used in the search.

Data Type	Source	Acquisition Date	Relevance	Spatial resolution
Shuttle Radar Topographic Mission (SRTM)	EarthExplorer https://earthexplorer.usgs.gov/	-	Elevation, Slope, Topographic wetness index, drainage density	30 m
Landsat Imagery	EarthExplorer https://earthexplorer.usgs.gov/	2022	Land use land cover, NDVI	30 m
Precipitation	Iraqi Agrometeorological Network	2022	Sum Precipitation map	-
River shape file	Diyala Governorate	-	Distance to river	-
Road shape file	Diyala Governorate	-	Distance to road	-

3.2 Methodology

The integration of GIS and the Analytical Hierarchy Process (AHP) is needed to find and map potential flood hazard areas in the study area. The nine spatial data layer factors that affect the occurrence of floods used in this study include elevation (El), slope (Sl), topographic wetness index (TWI), drainage density (DD), Normalized Difference Vegetation Index (NDVI), land use land cover (LULC), distance to rivers (DRI), distance to the road (DRO), and precipitation (Rf) were created using GIS software and remote sensing techniques from different sources of collected data. Elevation, slope, drainage density, and topographic wetness index (TWI) maps were created from the Digital Elevation Model (DEM) with a resolution of 30 m using SRTM data. LULC and NDVI maps were generated using Landsat 8 satellite imagery acquired for 2022. A precipitation map was created using data collected from the Iraqi Agrometeorological Network data. Distance from the river and distance from road maps were produced using data collected from the Diyala governorate. All these layers were converted to integer raster maps with a spatial resolution of 30 m. Then, all raster factor maps were reclassified using the Reclassify tool of Spatial Analyst in ArcGIS 10.8 to a measurement scale of 1 (very low), 2 (low), 3 (moderate), 4 (high), and 5 (very high). After that, a relative weight of influence to each factor applying the analytical hierarchy process (AHP) was found using Excel. Finally, flood hazard mapping by overlaying the nine-factor flood maps using the weighted overlay method in the ArcGIS10.8 software was completed. The complete methodology is shown in (Figure 2).

Figure 2 Methodology of potential flood hazard mapping.

4 Preparation and reclassification of flood‑controlling factors

The elevation and slope maps were directly created from the DEM map of the study area using the slope in the Spatial Analyst Tools in ArcGIS10.8.

To derive a drainage density map using the hydrology tools in ArcGIS Spatial Analyst, a filled sink DEM of the study area was created to remove imperfections in the data. Then, the flow direction was calculated to determine the flow path of streams from the filled cells, followed by generating flow accumulation, which represented the accumulated weight of all cells. Using this data, stream order was determined by assigning numeric values to the cells based on their hierarchy. The stream-to-feature tool was then applied to convert the linear stream network from raster to vector format using the flow accumulation and stream order data. Finally, the line density tool was used to calculate the magnitude-per-unit area of the linear network.

A rainfall map was produced using data from two meteorological stations collected from the Iraqi Agrometeorological Network. The data was initially organized in Excel and then exported to ArcGIS. Using the Inverse Distance Weighted (IDW) method in the ArcGIS 10.8 environment, with a pixel size of 30 meters, a rainfall map for the study area was generated based on its shapefile.

To create the Topographic Wetness Index (TWI) map, the flow accumulation was calculated using a raster calculator and Equation 1, similar to the process used for deriving drainage density. Next, the slope in radians was estimated from the slope in degrees using the appropriate conversion formula. Finally, the TWI was computed using Equation 2.

(1)

(2)

To generate the TWI map, the Raster Calculator from the ArcGIS environment map’s Spatial Analyst Tools were used for Equation 3.

(3)

Where:

α	=	the upslope area per unit contour length, and
β	=	the local slope.

The NDVI map was derived from a Landsat 8 satellite image downloaded from the USGS website. NDVI was estimated by applying Equation 4 in the ArcGIS10.8 environment, which depended on bands four and 5 (Gangashe 2020).

(4)

Where:

ρ NIR	=	near infrared spectral band, and
ρ Red	=	red spectral band.

An LULC map was created using supervised classification, and for the maximum likelihood method for combined bands (3, 4, and 5), a Landsat 8 image was explored using an ArcGIS map.

Distance to the river and distance to road maps were produced using the endurance distance tool in the spatial analyses’ toolbox in ArcGIS 10.8 software for river and road shapefiles collected from the Diyala Governorate.

This paper reclassified all factors into five classifications using the reclassify tool in ArcGIS (very low, low, moderate, high, and very high).

5 Analytical Hierarchy Process (AHP)

The most effective and common approach used in the multi-criteria decision-making (MCDM) process is the analytical hierarchy process (AHP) that is suggested by Saaty (1987). The AHP is an approach that can solve a combination of composite quantitative, qualitative, and sometimes competing decision problems (Adjei-Darko 2017).

The Analytical Hierarchy Process (AHP) approach was used to rank the weights assigned to each criterion. This tool was employed to compute weights for the different indicators considered.

Equation 5 is used to create the binary comparison matrix and determines the weights, and the criteria are compared to the relative importance of determining the comparison matrix. This study ranges from 1 to 9, where 1 is the equal significance, and nine is the highest.

(5)

Where:

n

=

the parameter number.

To calculate the normalized pairwise comparison matrix (NPCM), Equation 6 is used by dividing the number of each column cell by the sum number of columns.

(6)

To calculate the Priority vector, Equation 7 is used.

(7)

The consistency ratio (CR) is used to assess the coherence of judgments and is determined by calculating the Random Index (RI) and the Consistency Index (CI) using Equation 8. The calculation depends on the number of factors compared in the matrix (n) and the highest eigenvalue of the pairwise comparison matrix (λmax). the (λ) is determined by multiplying the normalized pairwise comparison matrix with the priority vector (w). The CI is calculated using Equation 9, which is based on the eigenvalue of the judgment matrix (λmax), itself influenced by the weight vector (W) and the judgment matrices (P) for the factors. The value of λmax can be estimated using Equation 10, derived from the binary comparison matrix and the normalized pairwise comparison matrix.

The random index (RI) is dependent on the number of factors used in the pairwise comparison matrix. The comparison matrix was deemed acceptable if the CR was less than 0.10 Otherwise, if the CR is greater than or equal to 0.10, it is unacceptable, and the comparison process must be repeated until the CR value is less than 0.10 (Saaty 1987).

(8)

(9)

(10)

6 Flood preparation using a flood hazard map

After reclassifying flood factors into five zones from 1 (very low hazard) to 5 (very high hazard) using ArcGIS and estimating weights using the AHP approach of the nine flood-controlling factors, the layers were integrated using the weighted overlay tool in the Spatial Analyst Toolbox in ArcGIS, which uses Equation 11, to estimate the flood dangerous map of the study area.

(11)

Where:

y	=	flood susceptibility,
n	=	number of decision criteria,
xi	=	normalized criterion, and
wi	=	respective weight of the criterion.

7 Results

7.1 Factors contributing to flooding

Elevation (EL)

Elevation plays a major influence in controlling flood occurrence. Flood disasters often happen in regions with low topographic elevations or downstream areas (Jati and Santoso 2019). The elevation map was prepared using ASTER DEM with a spatial resolution of 30 m (Li et al. 2017). The elevation values in the study area have a mean elevation of 49 m above sea level and range between a minimum of 17 m and a maximum of 1826 m above sea level, as shown in Figure 3 and Table 2.

Figure 3  Study area: Elevation map.

Slope (SL)

Slope is a major factor in flood-prone surface zones as it affects water flow. Slope, also called gradient, is the elevation rate change at a surface point (Weih and Mattson 2004). More exposure to flooding happens in lower-slope areas (Ullah and Zhang 2020). The common procedure to estimate slope is from a regularly gridded DEM (Tang and Pilesjö 2011). ArcGIS 10.8 was used in this study to create a slope map from ASTER DEM with a resolution of 30, as shown in Figure 4 and Table 2.

Figure 4  Study area: Slope map.

Table 2 Flood factors, average weights, classes, rating values, and percentage area.

Factor	Average weights	Class	Flood susceptibility	Rating	Area %
Elevation (m)	0.13	-3-97.416	very high hazard	5	61%
		97.417-240.87	high hazard	4	26%
		240.88-434.53	moderate hazard	3	10%
		434.54-922.26	low hazard	2	3%
		922.27-1826	very low hazard	1	1%
Slope (°)	0.11	0-2.09	very high hazard	5	57%
		2.1-5.39	high hazard	4	36%
		5.4-12.3	moderate hazard	3	6%
		12.4-26.3	low hazard	2	1%
		26.4-76.3	very low hazard	1	0%
Rainfall (Rf) (mm)	0.15	31.45-31.73	very low hazard	1	17%
		31.74-32	low hazard	2	23%
		32.01-32.25	moderate hazard	3	25%
		32.26-32.54	high hazard	4	17%
		32.55-32.8	very high hazard	5	17%
Distance to the rivers (DR) (km)	0.16	0-0.07926	very high hazard	5	12%
		0.07927-0.1628	high hazard	4	18%
		0.1629-0.2506	moderate hazard	3	21%
		0.2507-0.3428	low hazard	2	24%
		0.3429-0.5463	very low hazard	1	26%
Distance to the road (Dro) (km)	0.06	0-0.09499	very low hazard	1	36%
		0.095-0.2114	low hazard	2	27%
		0.2115-0.3462	moderate hazard	3	18%
		0.3463-0.5056	high hazard	4	13%
		0.5057-0.7813	very high hazard	5	5%
Drainage density (DD) (km/km2)	0.11	0-0.16	very low hazard	1	14%
		0.17-0.28	low hazard	2	25%
		0.29-0.39	moderate hazard	3	29%
		0.4-0.51	high hazard	4	23%
		0.52-0.89	very high hazard	5	9%
Topographic Wetness Index (TWI)	0.15	5.0399-9.388	very low hazard	1	35%
		9.389-11.05	low hazard	2	36%
		11.06-13.29	moderate hazard	3	20%
		13.3-16.53	high hazard	4	8%
		16.54-13.29	very high hazard	5	2%
Land use/Land cover (LULC)	0.07	built-up area	very low hazard	1	26%
		bare land area	low hazard	2	69%
		low vegetation density	moderate hazard	3	3%
		high vegetation density	high hazard	4	0.4%
		water area	very high hazard	5	2%
NDVI	0.06	-0.3-0.013	very high hazard	5	1%
		-0.012-0.077	high hazard	4	59%
		0.078-0.13	moderate hazard	3	31%
		0.14-0.22	low hazard	2	8%
		0.23-0.52	very low hazard	1	2%

Topographic Wetness Index (TWI)

TWI is a physical index that assesses the effect of topography on the resulting flood (Pourali et al. 2016). There is a positive correlation between TWI and flooding vulnerability (Khosravi et al. 2019). The Topographic Wetness Index (TWI) is derived from DEM data using slope and flow accumulation functions (Riadi et al. 2018). ASTER imagery was used to estimate TWI using the model builder in ArcGIS10.8 (Figure 5) and using the equation by Blumenthal et al. (2018). The results of the run model builder are shown in Figure 7 and Table 2.

Figure 5 Model builder estimates Topographic Wetness Index (TWI).

Drainage density (DD)

Drainage density is an important factor affecting flooding. Drainage density is a ratio of the sum lengths per unit area. It is generally expressed in units of km/km² (Dingman 1978). A higher drainage density leads to a higher likelihood of flood hazard (Ogden et al. 2011). The drainage density was calculated using a model builder (Figure 6) from ASTER DEM by applying the line density tool in spatial analysis in ArcGIS10.8, as shown in Figure 8 and Table 2.

Figure 6 Model builder used to estimate drainage density (DD).

Figure 7  Study area: Topographic Wetness Index.

Figure 8  Study area: Drainage density.

Normalized Difference Vegetation Index (NDVI)

The Normalized Difference Vegetation Index (NDVI) indicates the greenness of vegetation and its decrease due to flooding in a basin. The decreased vegetation area leads to flood susceptibility (Rahman et al. 2021). NDVI is calculated from the red visible and the near-infrared bands of the electromagnetic spectrum (Jasim et al. 2020; Shrestha et al. 2013). The NDVI ranges from -1 to +1 and can be calculated from Landsat 8 imagery using an equation by Pourali et al. (2016). Researchers used a raster calculator tool in ArcGIS software to calculate NDVI, as shown in Figure 9 and Table 2.

Figure 9  Study area: Normalized difference vegetation index map.

Land Use Land Cover (LULC)

The LULC distribution area has an important role in floodwater movement, accelerating or delaying the flow of water. LULC area changes affect the transformation of precipitation into hazardous floods (Roy et al. 2022). The main concern with flood frequency is the change in LULC area due to the increasing need for terrestrial for agricultural areas and other land uses (Sugianto et al. 2022).

The LULC map was created from Landsat 8 imagery from 2023. A supervised classification technique with a maximum likelihood method was applied to the Landsat imagery using ArcGIS 10.8. The LULC map was divided into five classes: water area, high-density vegetation area, low-density vegetation area, built-up area, and bare land area, as shown in Figure 10 and Table 2.

Figure 10  Study area: Land Use Land Cover map.

Distance from river (DRI)

There is a higher probability of hazardous flooding in the regions near the river rather than in faraway regions. The surplus river water initially reaches the riverbanks and covers a low-elevation area (Negese et al. 2022). Increasing distance from the river leads to higher elevation and slope (Zzaman et al. 2021). This study estimated the distance from the river using the Euclidean distance tool, as shown in Figure 11 and Table 2.

Figure 11  Study area: Distance from river. 

Distance to roads (DRO)

Impermeable surfaces (roadways, pavement, and parking spaces) increase the rainfall-runoff process (Zhu et al. 2019). Flood probability increases with increased distance from the road because roads slow down the infiltration water process (Addis 2023; Osman and Das 2023). This study estimated the distance from the road using the Euclidean distance tool, as shown in (Figure 12) and Table 2.

Figure 12  Study area: Distance to roads.

Rainfall (precipitation) (Pr)

Precipitation greatly impacts flooding, which happens when river channels and sewer systems cannot convey excess water. It is the most crucial factor due to flood inundation and the massive volume of runoff to streams due to excessive precipitation (Negese et al. 2022).

A rainfall map was created from the annual rainfall point data for the study area using Inverse Distance Weighting (IDW) Interpolation, as shown in Figure 13 and Table 2.

Figure 13 Study area: Rainfall map.

7.2 Flood hazard mapping of the study area

In this study, first the nine flood factors were resampled with a pixel size equal to 30 m, then were reclassified into five classes using the reclassify tool in ArcGIS 10.8, as shown in Figure 14. Next, rating each factor's classes depended on its effect in flood hazard as shown in Table 2. Second, the AHP method assigns weights to the nine flood factors based on their relative importance and influencing capacity on the flooding via approaching experts and reviewing published literature, as shown in Table 3. Third, the normalization of the pairwise comparison was completed, as shown in Table 4, and then a check of the consistency index was performed, which found it to be equal to (CI = 0.04) that was calculated from (CR=0.059) and (λ max = 8.63). Finally, the weight for each food-controlling factor was found. The results show the weight for each food factor, topographic wetness index (15%), elevation (13%), slope (11%), precipitation (15%), land use land cover (7%), normalized vegetation deferential index (6%), distance from river (16%), distance from road (6%), and drainage density (11%). Finally, flood hazard mapping was done by using the weighted overlay toolbox which reclassified the estimated nine flood factors, as shown in Figure 14, and estimated weights for these factors using the AHP approach.

Figure 14 Reclassification of (a) elevation, (b) slope, (c) Topographic Wetness Index, (d) drainage density, (e) Normalized Vegetation Index, (f) Land use land cover, (g) distance from the river, (h) distance from the road, and (i) rainfall maps.

Table 3 Pairwise comparison of nine flood factors.

Factor	TWI	EL	SL	PR	LULC	NDVI	DRI	DRO	DD
TWI	1	1	1	1	3	5	1	3	1
EL	1	1	1	1	2	3	1	3	1
SL	1	1	1	1	3	1	1/2	1	1
PR	1	1	1	1	3	2	2	3	1
LULC	1/3	1/2	1/3	1/3	1	1	1/3	3	1
NDVI	1/5	1/3	1	1/2	1	1	1/5	1	1
DRI	1	1	2	1/2	3	5	1	3	1
DRO	1/3	1/3	1	1/3	1/3	1	1/3	1	1
DD	1	1	1	1	1	1	1	1	1

Table 4 Normalized pairwise comparison matrix and calculated criteria weight for each factor.

Factor	TWI	EL	SL	PR	LULC	NDVI	DRI	DRO	DD	CW
TWI	0.1456	0.1395	0.1071	0.1500	0.1731	0.2500	0.1357	0.1579	0.1111	15.2240
EL	0.1456	0.1395	0.1071	0.1500	0.1154	0.1500	0.1357	0.1579	0.1111	13.4720
SL	0.1456	0.1395	0.1071	0.1500	0.1731	0.0500	0.0679	0.0526	0.1111	11.0780
PR	0.1456	0.1395	0.1071	0.1500	0.1731	0.1000	0.2715	0.1579	0.1111	15.0650
LULC	0.0485	0.0698	0.0357	0.0500	0.0577	0.0500	0.0452	0.1579	0.1111	6.9550
NDVI	0.0291	0.0465	0.1071	0.0750	0.0577	0.0500	0.0271	0.0526	0.1111	6.1820
DRI	0.1456	0.1395	0.2143	0.0750	0.1731	0.2500	0.1357	0.1579	0.1111	15.5810
DRO	0.0485	0.0465	0.1071	0.0500	0.0192	0.0500	0.0452	0.0526	0.1111	5.8940
DD	0.1456	0.1395	0.1071	0.1500	0.0577	0.0500	0.1357	0.0526	0.1111	10.5500

Table 5 Calculating the consistency of pairwise comparison (CR=0.04).

Factor	TWI	EL	SL	PR	LULC	NDVI	DRI	DRO	DD	CW	WSV	WSV/CW
TWI	0.02	0.13	0.12	0.15	0.21	0.07	0.16	0.21	0.12	15.22	1.20	7.87
EL	0.02	0.13	0.12	0.15	0.14	0.07	0.16	0.21	0.12	13.47	1.13	8.36
SL	0.02	0.13	0.12	0.15	0.21	0.04	0.08	0.07	0.12	11.08	0.94	8.45
PR	0.02	0.13	0.12	0.15	0.21	0.15	0.31	0.21	0.12	15.07	1.43	9.47
LULC	0.01	0.07	0.04	0.05	0.07	0.02	0.05	0.21	0.12	6.96	0.64	9.26
NDVI	0.01	0.04	0.12	0.08	0.07	0.01	0.03	0.07	0.12	6.18	0.56	8.98
DRI	0.02	0.13	0.24	0.08	0.21	0.07	0.16	0.21	0.12	15.58	1.25	8.00
DRO	0.01	0.04	0.12	0.05	0.02	0.02	0.05	0.07	0.12	5.89	0.51	8.72
DD	0.02	0.13	0.12	0.15	0.07	0.07	0.16	0.07	0.12	10.55	0.91	8.63

The study found the area and percentage area of flood hazard zones for each sector in the study area as shown in Figure 15 and Table 6. The total percentage of flood zones for the study area is shown in Figure 16.

Figure 15 Flood potential map.

Table 6 Area and percentage of flood zones for study area sectors.

Sector	Area (km2)	Percentage	Very low %	Low %	Moderate %	High %	Very high %
Al-Kkalis	1384.925	9%			19%	81%	0.01%
Al-Mikdadya	1396.207	9%			50%	50%	0.01%
Baaquba	658.7228	4%			38%	61%	0.01%
Balad Roz	7220.302	47%		6%	75%	19%
Kanakeen	4859.044	31%	0.0001%	22%	67%	12%	0.03%

Figure 16 Total percentage of flood zones for the study area.

8 Conclusion

Floods have negatively affected cities, including people's lives and environmental and social assets. In this study, the nine factors including elevation, slope, topographic wetness index (TWI), Land use land cover (LULC), Normalized Difference Vegetation Index (NDVI), drainage density, distance to road, distance to river and rainfall were used to develop a flood hazard map. The flood risk zone was classified into five hazard areas: very low, low, moderate, high, and very high.

The results showed that about 0%, 9%, 64%, 26%, and 0.01% of the study area faces very low, low, moderate, high, and very high flood hazards, respectively. Furthermore, the study indicates that the high flood-prone areas are situated in the Al-Mikdadya and Baquba sectors in the southeast of the study area. These areas near the river have a low slope gradient, low elevation, high drainage density, and high TWI value. The study showed the effectiveness of integrated GIS software, multi-criteria decision-making (MCDM), and the analytical hierarchy process (AHP) for finding and mapping the flood risk zones.

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