Statistical information of historical data like mean values, skewness, standard deviations are calculated for the determination of FFA. Advanced Microsoft Excel programming was used for the FFA estimation and the value for the return period of 2, 5, 10, 25, 50, and 100 years of water level data was extracted. Straightline regression was applied to extract the value of missing year data. Normal Distribution Method, EVI Distribution and LPIII Distribution are used for the frequency analysis.
3.1 Normal distribution: The normal distribution is perhaps the main circulation in any measurable investigation since it fits numerous natural events. It has mainly two parameters, the mean and the standard deviation. The following equations are used to calculate a normal distribution for the return period of 2, 5, 10, 25, 50, and 100 years from the obtained data. The magnitude of the flood was obtained from Eq. 3.1.1
$${X}_{T}={P}_{avg}+KS$$ 3.1.1
Where K is the frequency factor of the specified return period, Pavg, and S are the average value and standard deviation of the data respectively. The frequency factor expressed as
$$K=W\frac{2.515517+.802853W+.01032{W}^{2}}{1+1.432788W+.189269{W}^{2}+.001308{W}^{3}}$$ 3.1.2
Where,
$$W={\left[\text{ln}\left(\frac{1}{{p}^{2}}\right)\right]}^{\frac{1}{2}}0<P\le .5$$ 3.1.3
\(P=\frac{1}{T}\) (T is the return period) (3.1.4)
3.2 Extreme Value TypeI Distribution (EVI): The EVI (Eq. 3.1.1), 5, 10, 25, 50, and 100 years return periods for every span period and requires few computations. With a predefined return period T (in year), the most extreme streamflow XT (mm) is given by the accompanying condition.
$${X}_{T}={P}_{avg}+KS$$ 3.1.1
Where K signifies as Gumbel frequency factor and surrendered Pavg is the average of the most extreme streamflow compared to a particular period.
$$K=\frac{\sqrt{6}}{\pi }\left[.5772+\text{ln}\left\{\text{ln}\left(\frac{T}{T1}\right)\right\}\right]$$ 3.2.1
$${P}_{avg}=\frac{1}{n}\sum _{i=1}^{n}{P}_{i}$$ 3.2.2
Where is the Pi singular limit worth of streamflow and n is the number of occasions or the numbers of years. The standard deviation is determined by (3.2.3) processed utilizing the accompanying connection
$$S=\sqrt{\frac{1}{n}\sum _{i=1}^{n}{\left({P}_{I}{P}_{avg}\right)}^{2}}$$ 3.2.3
3.3 Log Person Type III Distribution (LPIII): LPIII distribution consists of the step of transforming the observed values by taking their logarithms. The mean and standard deviation of the logarithmically changed information are additionally determined. The recurring precipitation is conducted in a manner similar to Gumbel circulation by applying the LPIII approach. The improvedon articulation for this circulation is given as follows:
$${P}^{*}={\text{log}}_{10}({P}_{i})$$
3.3.1
$${P}_{t}={P}_{avg}^{*}+K{S}^{*}$$
3.3.2
$${P}_{avg}^{*}=\frac{1}{n}\sum _{i=1}^{n}{P}^{*}$$
3.3.3
$${S}^{*}={\left[\frac{1}{n1}\sum _{i=1}^{n}{\left({P}^{*}{P}_{avg}^{*}\right)}^{2}\right]}^{\frac{1}{2}}$$
3.3.4
$${X}_{t}=antilog\left({P}_{t}\right)$$
3.3.5
Where \({X}_{t}\) is the magnitude of the water level of a specified return period.\({P}^{*}\)is the log value of initial data, K is the frequency factor, which is the capacity of coefficient of skew (CS) and return period (T). The skewness coefficient is determined by utilizing (3.3.6).
$$K=f\left({C}_{s},T\right)$$
3.3.6
$${C}_{S}=\frac{n\sum _{i=1}^{n}{\left({P}^{*}{P}_{avg}^{*}\right)}^{3}}{\left(n1\right)\left(n2\right){\left({S}^{*}\right)}^{3}}$$
3.3.7
The value of K can be acquired from tables in the hydrological book [Table 7.6 by Subramanya (2017) ]. The recurrence factor, K for the LPTIII Distribution can be derived after determining the recurrence interval. In case, the skew coefficient (CS) is between two given skew coefficients in the table, linear interpolation (3.3.8) was used to get the appropriate value of K.
$$y={y}_{1}+\frac{{y}_{2}{y}_{1}}{{x}_{2}{x}_{1}}\left(x{x}_{1}\right)$$
3.3.8
3.4 Flood Map Preparation and Inundation Analysis:
ArcGIS Platform has been utilized here to prepare flood map and inundation analysis considering the numerical value of EVI distribution for 10years return period as this distribution has not only been suggested by the Government of Bangladesh (Monirul Qader Mirza 2002), it also shows the best fit in comparison with other distributions. The SRTM (shuttle radar topography mission) DEM (digital elevation model) was used to prepare a flood map. The magnitude of the flood under different categories was brought out using the spatial analysis in ArcGIS. The method demonstrated below shows how GIS information is reconstructed from the base information and spatial analysis strategy that is applied to assess the level of effect for the situation concentrated on the region.

Posting flood depth markers in computable ArcGIS format.

The water height (calculated by EVI) is generated using the inverse distance weighting (IDW) algorithm from flood depth markings. The simulated water height surface is the result of the interpolation.

Calculate inundated regions using map algebra by subtracting the DEM from the water height surface. The result of this algorithm is a map that displays the value of each point. Positive values correspond to flooded areas, whereas negative values correspond to unflooded areas.

Identifying flooding levels through reclassification of the inundation map into F0 (00.3m), F1 (0.310.9m), F2 (0.911.8m), F3 (1.83.2m), F4 (> 3.6m) zones. Then, using a structured query language (SQL) method, extract those classes to produce a raster inundation map.

Converting the raster inundation map to polygon format using the "raster to polygon" discussion tool. This phase generates an inundation map in the Into Polygon format.
3.5 AHP Modeling: The AHP (Analytic Hierarchy Process) is a comprehensive approach to dealing with dynamics that may be applied to a wide variety of scenarios, norms, and entertainers to resolve deeply complicated challenges. The AHP technique has been applied to a variety of sectors and projects, including environmental and natural resource studies, forestry, coastal management, and disaster and risk management (Schmoldt et al. 2001; Chen et al. 2009; Ryu et al. 2011; Samari et al. 2012). The AHP was used in this study to determine the standards and components that most accurately represent a disaster hazard in a coastal location. Seven parameters that describe the best risk factor at the local level were chosen for developing the AHP method (Fig. 2). Seven factors (Inundation level, Population density, Livelihood, Household structure, Settlement elevation, Literacy, and Vulnerable People) were selected for the development of the AHP approach in our studies. The parameters are carefully chosen from the distinct literature to ensure that they are sufficient to address the region's specific environment.
One of the traditional issues in choice hypothesis or multicriteria investigation is the assurance of the general significance of every boundary. The general significance of boundaries visaávis the goal is typically addressed by a set of weights, and are standardized to a steady as:
$$\sum _{1}^{n}{W}_{i}=1$$
A significance scale is proposed for the pairwise examination of criteria, because of countless analyses (Table 2). Experts' opinion has been used to prioritize these matrices. The pairwise correlation network of n boundaries constructed based on Saaty's scaling (Table 2) proportions might be of the request (nxn) as follows in the eigen vector technique for ensuring the largest eigen value to gauge the loads:
$$A=\left[{a}_{ij}\right]i,j=\text{1,2},3\dots \dots n$$
3.5.1
Where,
$${a}_{ij}=\frac{{w}_{i}}{{w}_{j}} for all i and j$$
$${a}_{ij}=\frac{1}{{a}_{ij}}$$
And \({a}_{ij}={a}_{ik}/{a}_{jk }\)for any I, j and k
Multiplying Eq. (3.5.1) with W (weighting factor) of (n\(\times\)l) size yields
(AnI) W = 0 (3.5.2)
where I is an identity matrix of (nxn). According to matrix theory, if the comparison matrix A has the property of consistency, the system of equations has a trivial solution. The matrix A is; however, a judgment matrix and it may not be possible to determine the elements of A accurately to satisfy the property of consistency.
$${A}^{*}{W}^{*}={\lambda }_{max}{W}^{*}$$
where A* is the estimate of A, and W* is the corresponding priority vector and \({{\lambda }}_{max}\)is the largest eigenvalue for the matrix A. Eq. (3.5.2) yields the weights W which is normalized to unity. The consistency of each pairwise comparison matrix is calculated by consistency ratio (CR), which is equal to (CI/RI), where CI is the consistency index and is equal to (\({{\lambda }}_{max}\)n)/(n1) and RI is the average of the resulting consistency index depending on the order of the matrix. When CR is less than 0.10, the matrix has a reasonable consistency otherwise the matrix has to be changed (Palchaudhuri and Biswas 2016).

Livelihood: Disasters especially affect the least fortunate and most vulnerable individuals, creating social disparities and hurting economic growth. So, the income or livelihood pattern plays a significant role in assessing any disaster risk assessment. According to the population and housing census2011 report, people living at the concerning area earn their livelihood by doing mainly 3 types of activity i.e. agriculture, business, and services. A threepoint Likert scale is used in this study where 1 indicates most vulnerable and 3 indicates least vulnerable for calculation of AHP. As agriculture is the most vulnerable for flood it has been assigned with the number 1, and service is the least vulnerable for flood analysis it has assigned with number 3.

Literacy: Recently education has been emphasized for any Disaster risk reduction (DRR) planning. As pointed out by many studies, raising public awareness through the informal campaign is not enough to minimize the vulnerability of any localities. Rather, a scientific knowledgebased approach and public education should be applied in engaging the community in the DRR plan and to eradicate superstitions regarding the disaster (Benadusi 2014b). Hence, literacy has been selected as a component of the comparison matrix. Here, the average literacy percentage of the concerned polders has been divided into 5 classes where number 5 indicates the highest (81–100%) literacy percentage, and number 1 is assigned for the lowest literacy percentage (1–20%).

Population Density: A high population density can easily increase the disaster vulnerability of an area. Population density has been calculated by the total population of an area and dividing it with the total settlement area obtained from GIS analysis. The obtained result has been classified into five categories, where the number 5 indicates the lowest population density (1250 individuals per square km) and 1 being the highest (1001–1250 individuals per square km). And the rest of the numbers fall between them.

Vulnerable people: In a disaster situation, seniors (65 + aged) are viewed as a weak populace similar to youngsters. As youngsters, the delicate old are regularly unfit to advocate for their advantages because of actual weaknesses, intellectual limits, or a combination of both. In comparison to a child, an older individual certainly relies on the assistance of an adult most of the time especially during in a situation. Two types of individuals have been considered as vulnerable people for the study, children under the age of 4 and people above the age of 65. The average percentages of both generations have been used for any single polder. We have classified those average percentages into 5 categories, where number 5 represent the lowest number percentages (0–2%) and number 1 represents the highest (8.1–10%).

Housing structure: There are mainly 4 types of housing structure found at the study area, i.e. Jhupri, Kutcha, Semipacca and Pacca. Where Jhupri has been assigned with number 1 as it is the most vulnerable housing structure for flooding and Pacca structure has been assigned with number 4 as it has the highest capacity to resist any natural hazards.

Settlement topographic elevation: Settlement topographic elevation plays a very significant role in flooding. From the GIS analysis the topographic elevation pf settlement has been calculated and are classified into 3 categories. Number 1 has been assigned with the least valued class (01.16m) and number 3 has assigned with the highest valued class (2.4m3.5m).

Inundation The inundation level for each polder are determined from frequency analysis and GIS analysis. Polders have been assigned with numbers, where number 1 are given for the highest percentages (80–100%) of inundated polder and number 5 has been assigned with the least (0–20%) percentages of inundated polders.