Flood Susceptibility Assessment of Chandpur Sadar Upazila Using Analytical Hierarchy Process and Frequency Ratio Model

: Flood susceptibility is a critical aspect of natural hazard assessment and disaster risk reduction. The objective of this study finds out flood risk zone of Chandpur sadar upazila using the Flood frequency ratio (FFR) and Analytical hierarchy process (AHP). The article explores the complex idea of flood susceptibility, focusing on analytical techniques used. The integration of twelve spatial data layers, such as Sediment Transport Index (STI), elevation, Topographic Wetness Index (TWI), slope, aspect, Land Use Land Cover (LULC), soil type, rainfall patterns, Normalize Difference Vegetation Index (NDVI), drainage density and distance from the river, is required for the assessment of flood vulnerability. A flood inventory map was buildup and using these methods, two vulnerability map was created, it was discovered that 60% & 48% of Chandpur's territory is categorized by very high to high-risk zone. Flood has dangerous effect on livelihood. I was hoped that it will be a helpful study for researcher and people of this upazila will be benefited.


Introduction:
One of the most dangerous hydro-meteorological hazards in the world is flooding.Frequently, flooding causes severe human casualties as well as socioeconomic destruction.According to data from the United Nations Office for Catastrophe Risk Reduction (UNISDR), there were approximately 150,061 flood events globally happened from 1995 to 2015 and around 157,000 mortalities connected to floods, which represented 11.1% of all catastrophe losses.(Anand & Pradhan, 2023).According to numerous investigations, global floods cause harm to almost 200 million people each year.(Abu Reza Md et al., 2020).Bangladesh is the extreme vulnerable country for natural disasters among the worldwide.Floods in Bangladesh are mostly caused by flat terrain, shallow riverbeds, intense monsoonal rainfall, and massive sediment release (Rahman et al., 2019).According to Statista, an index was developed between 0 and 10, greater the score and the higher the risk, established on the projected amount of individual occurrence of floods annually.Bangladesh receives a higher score (10) indicating a high risk of flood, built on exposure and demographic data from 2015 to 2021 (Statista, 2023).The three-river basin Ganges, Brahmaputra, and Meghna are located downstream of Bangladesh.These basins have a combined catchment area is 1.72 million km2, roughly 93% of which is located outside Bangladeshi territory.Only 7.5% of these basins are located in Bangladesh; the continuing 92.5% are in Nepal, China, Bhutan, and India.(Mirza, 1997).Among the overall flood, major flood occurred at1988, 1998, 2004, 2007, and 2014 year in Bangladesh (Hossain & Adhikary, 2022).According to the Bangladesh Department of Disaster Management (2014), every year 18% of the country gets flooded on average.Typically, during the monsoon season, approximately 20-25% of the country is inundated.At least eight extreme flood events that have affected 50% of the land area have occurred in the past fifty years.Up to 65% of the nation was submerged in the biggest flood ever recorded, which occurred in 1998.The IPCC AR4 (2007) also reported that significant and frequent floods were detected in 2002, 2003, and 2004.Approximately $175 million is lost to flooding in a typical year, but in severe cases-like in 1998, when almost two-thirds of the nation was submergeddamage can reach two billion dollars.(Climate: Observations, projections and impacts: Bangladesh) Flood vulnerability mapping is a crucial instrument for mitigating and preventing upcoming flood situations because it identifies the maximum risk area constructed on consistent data, enabling societies and administration agencies to exactly appliance strategies besides analyze flood anticipation methods.Several studies have prepared flood susceptibility mapping during the past few decades using various techniques.These techniques include frequency ratio techniques and the integrated Analytical Hierarchy Process.(Addis, 2023)  2).This upazila is prone to flooding, particularly during the monsoon season.This area is crisscrossed by numerous rivers and their tributaries, including the Meghna, Dakatia, and Kachua rivers, which often overflow their banks during heavy rains, causing flooding in the surrounding areas.With a 308.78 sq km area, Chandpur Sadar Upazila is situated between 23°07' and 23°20' north latitude and 90°34' and 90°48' east longitude.This upazila broderd by north by the upazilas of Matlab Dakshin and Matlab Uttar, on the south by the upazilas of Haimchar and Faridganj, on the east by the upazilas of Hajiganj and Faridganj, and on the west by the upazilas of Bhedarganj (Shariatpur) and Gosairhat (Banglapedia, 2022).Bangladesh Agro-Meterological Information Service (BAMIS), conducted a study that this Upazila is classified as "Riverine flood-prone" (Figure 1) and this area gets flooding from both river overflow and rainfall during the monsoon season, which can cause harm to crops, residences, and infrastructure.Additionally, Chandpur Sadar Upazila's • Email: editor@ijfmr.com

IJFMR240114028
Volume 6, Issue 1, January-February 2024 3 geography is primarily flat, which intensified it more vulnerable to flooding.Flooding is more likely to happen in low-lying locations, especially those area is close to rivers.

Figure 2: Flood vulnerability Map
Here we see that now a days various models are applied to find out susceptibility hazard in a definite area but in order to analyze flood vulnerability in the Chandpur District, this study applied AHP and FRM model using GIS-based mapping.

2.2: Data source and Methodology
The persistence of primary data collection, a scientific questionnaire was organized and a field survey is conducted.Secondary were used to realize the flood pattern of the selected area and evaluate the modification process.Secondary data were collected from distributed reports, papers and books along with documents of related organizations.To prepare thematic map of the study area like elevation, Curvature, slope, TWI, STI, drainage density, distance from the river maps were Using DEM data and NDVI, LULC map using Landsat 8 image with 30m resolution.Information about rainfall gathered from the Bangladesh Meteorological Department.All the map layers are integrated in Arc map 10.8.Using AHP and FFR, a flood susceptibility map was produced.Subsequently, examine the map's build-up roc curve.

Analytical Hierarchy Process
The Analytical Hierarchy Process (AHP) provides an organized approach to assess and rank different elements that are essential to understanding and reducing flood hazards in flood analysis.AHP makes it easier to compare several factors in an organized manner, including population density, infrastructure resilience, geographic vulnerability, emergency response capability, and environmental effect.Through the hierarchical leveling of the complex decision-making process, AHP allows stakeholders to weight various elements according to their relative weight.Broader issues like regional infrastructure development and governmental regulations may be considered at a higher level, while elements like land usage patterns, historical flood data, and proximity to water bodies may be evaluated at the local level.AHP helps to quantify subjective assessments and preferences through pairwise comparisons and mathematical calculations.This helps to identify the best flood management techniques and resource allocation to reduce vulnerabilities and increase community resilience against flooding events.The weight vector is determined by averaging each row of the normalized matrix.The weight vector and comparison matrix are multiplied to get the: The maximum eigenvalue( λmax) is: The weight vector is W, the pairwise comparison matrix is A, and the criteria number is n in these equations.The top right eigenvector system is the name given to this method for figuring out the weight vector of a pairwise comparison matrix (EM).Given that the decision-maker might not provide paired comparisons that are obviously harmonious, it is advised that pairwise comparison matrix A have a reasonable consistency, which is verifiable by the consistency rate (CR).(Aydin & Sevgi Birincioğlu, 2022) The comparisons are considered consistent if the coefficient of consistency (CR) remains within an acceptable range, usually less than 0.10.(Subramanian & Ramakrishnan, 2012)

Flood frequency analysis:
The flood frequency ratio is a crucial statistic in flood analysis that's used to evaluate the probability and frequency of flooding episodes over a specific period of time.The magnitude of one flood occurrence is compared to the magnitude of previous floods that have happened in the past to determine this ratio.Hydrologists and analysts determine the flood frequency ratio by examining past flood data in order to determine how common or uncommon a particular flood occurrence is.The observed flood, for example, has a magnitude 1.5 times larger than the usual or expected magnitude for that particular recurrence interval (e.g., a 100-year flood) when the flood frequency ratio is 1.5.In flood-prone areas, this ratio helps determine the likelihood that a flood of a specific size will occur within a given time frame, which is important information for risk reduction, infrastructure construction, and urban planning.Knowing the flood frequency ratio helps stakeholders and decision-makers make well-informed decisions about managing floodplains, zoning for land use, and designing robust infrastructure to lessen the effects of possible future flood events.

FR = a
Nfopix Ntfopix b Ncpix Ntcpix (Eq: 3) Where Ncpix is an area of the category inside the study area, Ntcpix is the total pixel area within the complete study area (b), FR is the frequency ratio, and Nfopix is a flood pixel area.Ntfopix is the total area (a).The FR for each causative factor class in the current exploration work was determined using an equation, and the outcomes are summarized.The FR of each factor type or class is added as shown in equation to produce the FSI.Aspect: The pathways of actual slopes face are determined by aspect.Paths with a unique aspect that are categorized depending on slope angle.Aspect influences the directions how floodwaters flow and maintains soil moisture.Thus, the aspect is indirectly causing the floods.A shaded slope location with high soil humidity and significant runoff will be used as an example.

Slope:
The likelihood of water stagnation is reduced, infiltration is reduced, and flow velocity is increased with increasing slope angle.There will be a greater chance of flooding in lower-lying region and flat areas like the Chandpur sadar upazila.Figure: 6 (c), shows that the blue color zone has a lower slope area with upper chance of occur flooding whereas the red color zone has a greater slope area with a lower chance of occur flooding.

Curvature:
The dynamics of floods can be significantly and quietly affected by the curvature of the land's surface.During rainstorm events, a landscape's topographic relief or curvature affects the flow and collection of water.Water tends to accumulate and form natural basins in places with concave or bowlshaped terrain, which can make flooding worse, especially in the lack of sufficient drainage.On the other hand, water is more likely to flow quickly downhill in areas with convex or sloping topography, which could result in flash floods as it builds up in valleys.Additionally, the pace and direction of floodwaters can be impacted by the curvature of riverbanks and channels, affecting where and how flooding occurs.
Topographic Wetness Index: An essential tool for understanding and controlling flood dynamics within a geographic environment is the Topographic Wetness Index (TWI).In order to assess the wetness or water saturation of the land, TWI uses topographic parameters including slope, elevation, and contributing regions as a terrain analysis measure.This methodology is very useful for forecasting and comprehending flood disasters.TWI assists in defining areas prone to flooding by detecting flow channels and places prone to water accumulation during rainfall or high groundwater conditions.The TWI ranges from 4.4643 to 21.346 value in this upazila area.
Sediment Transport Index: River erosion and deposition procedure are characterized by STI.As we know that Chandpur sadar upazila highly vulnerable for Meghna River erosion.The STI rate of the area differs from 0 to 1300.

Land use Land Cover:
Flood patterns now certain areas are significantly influenced by land use and land cover.The decisions we make when regulating and developing land have a direct impact on how vulnerable a region is to flooding.The natural equilibrium of water absorption and runoff is disturbed when natural land cover is replaced by impermeable surfaces like roads and buildings.Deforestation lessens the beneficial function of forests in absorbing rainfall and controlling water flow.This process is frequently driven by agricultural expansion or urban growth.Similar to this, the loss of wetlands destroys essential natural buffers that absorb up extra water during torrential rains.Ineffective management of agricultural activities can result in increased runoff and erosion.In the study area the five classes in the LULC map are vegetation, settlement, wetland, agricultural land, and water body.
Distance from the river: A region's susceptibility to floods can be considerably influenced by its proximity of the river.Floods are higher chances to take place in regions close to rivers, known as floodplains, particularly during times of severe rainfall.Such circumstances cause rivers to naturally overflow their banks, and the ensuing floodwaters can submerge surrounding regions.However, the risk of flooding typically decreases as one travels further away from the river.

Soil:
In determining flood susceptibility, soil composition is a key factor.The capability of soil to hold and absorb water, sometimes referred to as its permeability or infiltration capacity, can have a big impact on how rain is managed in a specific location.Sand or loamy soils, which have good drainage, can quickly absorb rainwater, lowering surface runoff and the chance of flooding.Clay or areas that are densely populated, on the other hand, have poor drainage or compacted soils that prevent water from soaking into the ground, causing rain to collect on the surface and perhaps producing rapid runoff and flash floods.Additionally, soil characteristics have an impact on groundwater levels.Saturated soils increase the risk of flooding because they reduce the ability of the earth to absorb more water after heavy rains.Figure (13) presents soil map Chandpur sadar upazila mostly area is slit clay loam and it can be the reason for flooding.
NDVI: NDVI is a vital instrument for managing and monitoring floods.Based on satellite or remote sensing data, NDVI gauges the condition and density of a given area's vegetation cover.This index offers important details regarding the state of the land's surface, particularly the quantity and quality of vegetation.When there has been flooding, NDVI can be used to gauge the damage caused and forecast future flooding.Reduced vegetation cover, which may be the result of flooding or waterlogging, is indicated by a lower NDVI value.Identifying flood-prone areas and evaluating the effects of floods on ecosystems and agriculture can both be done by monitoring variations in NDVI over time.NDVI value ranges -1 to +1.
Drainage Density: Flooding and drainage density are directly related.Higher drainage density, which denotes significant surface runoff, is directly related to an increased chance of floods.The SRTM DEM's stream network was recovered, and ArcGIS (10.8) spatial analyzer was used to apply line density to create a drainage density map.A natural breathing pattern was used to divide the drainage density map into five classes.(Ullah & Zhang, 2020) Rainfall: Rainfall is the catalyst behind many destructive flood events worldwide.When precipitation, whether from heavy rainfall or snowmelt, exceeds the natural capacity of the land and water systems to manage it, floods can occur.The intensity, duration, and spatial distribution of rainfall are key factors influencing flood risk.Sudden, intense downpours can lead to flash floods, particularly in urban areas with impermeable surfaces that encourage rapid runoff.Extended periods of rainfall can saturate the soil and cause riverbanks to overflow, resulting in riverine floods.I have collected rainfall data from BMD for the last 20 years.

: Flood analysis using Flood Frequency Model
A Flood Frequency Ratio (FFR) table is a tool used in hydrology and flood frequency analysis to assess the relative rarity or frequency of different flood events based on historical data.Flood frequency ratio table calculated every class flood driving factors.get higher FR value.Loam soil mass has received higher value of FR indicating higher flood incidence probability.The intensity of rainfall is a critical factor.Those area get higher intensity rainfall and close to the river are more vulnerable.The first classes get higher FR value which is close to the river.Lower drainage density indicates higher flood occurrences.STI ratio of flood is higher in first class and chances to more flooding occurrence.Aspect is one of the significant factors of flood determination.The class get higher FR ratio that means of high chances of flood occurring.(Table : 2)  By applying the AHP, the comparative importance of each criterion was estimated after constructing criteria weights for further analyze done by arc map.After functioning the weighting sum of all factors, the map was created with, consistency ratio (CR) is 0.096 (<0.1, validated).

Figure 8: Criteria Weights(%) table using AHP
Criteria weights percentage is more important to analyze risk of flood.The researcher gave the higher weight to the criterion rainfall with 16.77%, elevation 14.08% slope 12.50%.The TWI, STI, distance from the river, LULC weights vary between 10% to 6%.Although all parameters have greater impact on occurrence flooding it is quite difficult to choose.River side area are more vulnerable of flood in chandpur sadar union population density is high which influence the vulnerability of flooding.Table (5), 32.71 % area of the sadar upazila fall in very high flood risk area and 16.60% fall in high-risk flood area that means total 49% area is high susceptible flood area.The analysis is deemed good and average based on the AUC values of 0.731 and 0.661.This is quite simple to confirm that the model, which was employed to create the Chandpur Sadar Upazila flood susceptibility mapping, is reliable and appropriate for identifying flood patterns.Finally, it can be believed that the FRM and AHP model is a greatly helpful process for mapping flood susceptibility.(Debabrata and Prolay, 2019)

Conclusion:
In conclusion, two methods are used to identify flood vulnerability area of Chandpur sadar upazila.One is flood frequency ratio and other is analytic hierarchy process.Twelve parameters are selected like TWI, STI, LULC, NDVI, slope, elevation, rainfall, drainage density, distance from the river, aspect, soil, curvature using GIS for further analysis.Using goggle earth and field survey flood inventory map was created.Two flood vulnerability map was created using AHP and FFR method and classified those map low, medium, high, very high flood risk zone.Almost 27% area in AHP method and 32% area in FFR method fall in high-risk zone in this upazila.So, the overall conclusion is that this area fall in is risk zone of flood vulnerability and may help the decision makers and government and nongovernment authorities to mitigate flood vulnerability.As climate change is increasing the risk of flooding Chandpur sadar upazila situated at near the Padma -Meghna Confluences.Flood Measures are need to reduce and control flood risk.Flood management like Levee and dam building, land use planning and zoning to prevent development in flood plains, early warning systems, public awareness, education, and collaboration between governments, communities, and stakeholders is essential.

Figure ( 9 )
Figure(9) represent the flood susceptibility map of Chandpur sadar upazila using AHP.In this map the dark red zone represents very high flood risk zone like chandpur sadar union,sakua union,klayanpur union, brown zone represents high risk zone like Rajshwari, Imbrahimpur, pest and green colour zone represents moderate and low zone.In this map flood free zone are included low susceptible zone of flood.River side area are more vulnerable of flood in chandpur sadar union population density is high which influence the vulnerability of flooding.Table(5), 32.71 % area of the sadar upazila fall in very high flood risk area and 16.60% fall in high-risk flood area that means total 49% area is high susceptible flood area.

Table 2 : Flood frequency ratio table
By analyzing historical data on river discharge or water levels, hydrologists can identify past flood magnitudes and apply statistical methods to estimate the probability distribution of future floods.This analysis allows for the calculation of return periods, which indicate how often floods of specific magnitudes are expected to occur.Table2, shows a strong association between flood variable class and flood, as well as risk of flood occurrence, the higher the values of FR and RF.High TWI values often likely to become saturated and contribute to surface runoff, thereby increasing the risk of flooding.As we can see that table 2, TWI increased and FR value also increased.Lower elevation locations, which are frequently closer to river, are far more likely to flood, especially during periods of high rainfall.Riverine flooding is generally less occurred in areas with higher altitudes.Table2, first as well as second class's elevation is lowest specifying higher flood region.The FR value also highest in lower elevation classes.For slope, FR value is highest for the first class is 2.21 indicate that these classes are more chances to flood occurring.In curvature the first and 2 nd class get a greater FR value has a strong correlation with flood occurrence.Concave and flat zone are potential to flood occurrence.The first and second classes distance from the river are closer to the river which get high flood pixel and greater FR value.In Land use Land cover Factors River, settlement, wet land has higher FR value, indicating higher flood occurrence probability.NDVI rate of this upazila ranges from -0.34 to 0.63.The low vegetation and water body area • Email: editor@ijfmr.com

Table 3 : Flood Susceptibile Area Using FRM 3.2: Flood analysis using Analytic Hierarchy Process
The Analytic Hierarchy Process (AHP) is a dominant decision-making tool that can be applied to flood analysis and risk assessment.AHP helps prioritize and evaluate various factors and criteria involved in flood management.