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Flood risk assessment through multi-Criteria analysis: A Comprehensive approach for Flood Analysis using GIS in SRIVAIKUNTAM Taluk at Tamil Nadu, India.
-ALIN AL,
Student of Masters In Geospatial Analysis Training at AGSRT
ABSTRACT
Floods pose significant threats to human lives, infrastructure, and ecosystems, necessitating effective flood management strategies. Multi-criteria analysis (MCA) offers a systematic framework for evaluating diverse factors involved in flood management decision-making. This study employs MCA to assess various criteria, including economic costs, social impacts, environmental consequences, and technical feasibility, to prioritize flood management alternatives. Through the integration of stakeholder preferences and expert knowledge, MCA facilitates the identification of robust flood management strategies that balance multiple objectives. The results provide valuable insights for policymakers, enabling informed decisions to mitigate flood risks and enhance resilience in vulnerable regions.
Introduction:
Floods remain one of the most recurrent and devastating natural disasters worldwide, causing significant loss of life, property damage, and disruption of essential services (Smith et al., 2019). With climate change projections indicating an increase in the frequency and intensity of extreme weather events, the urgency to develop effective flood management strategies has become more pressing than ever (IPCC, 2021). Traditional flood risk assessment methods often focus on single criteria, such as rainfall intensity or river discharge, overlooking the multifaceted nature of flood vulnerability (Mysiak et al., 2019). In response to this challenge, multi-criteria analysis (MCA) has emerged as a valuable tool for comprehensively assessing flood-prone areas by integrating diverse factors and criteria (Yang et al., 2020). Modern technologies primarily concern remote sensing techniques that employ satellite imagery, and high-resolution DEM (Digital Elevation Model) compatible with GIS (Geographic Information System) software are used for in-depth investigation of large areas or inaccessible zones (Zomer et al., 2002; Al-Saady et al., 2016; Zhang et al., 2019; Bhardwaj et al., 2019; Tapete et al., 2021; Kant et al., 2023; Patil and Panhalkar, 2023; Kholia et al., 2023). The most frequently used datasets in GIS are Global Terrain Model (GTM), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global DEM, Shutter Radar Topography Mission (SRTM), and Advanced Land Observing Satellite (ALOS) World 3D DEM (AW3D30).
Nowadays, nearly every nation is affected by problems related to natural disasters and climate change, such as global temperature rise, ocean pattern variation, and primarily changes in weather patterns leading to droughts and floods (Saeedrashed and Guven, 2013).
Conventional flood risk assessment approaches frequently fail to capture the complexity of flood dynamics and the interactions between various socio-economic, environmental, and infrastructural factors that contribute to vulnerability (UNDRR, 2019). This limitation hinders the development of targeted and resilient flood management strategies tailored to specific contexts (Jonkman et al., 2020). Moreover, the increasing urbanization and land-use changes exacerbate flood risks in many regions, underscoring the need for innovative methodologies capable of accommodating dynamic spatial and temporal dynamics (Di Baldassarre et al., 2021).
Objective:
This research paper aims to explore the application of multi-criteria analysis techniques for flood risk assessment in order to enhance the understanding of flood vulnerability. By adopting an integrated approach that considers multiple criteria, including topography, land use, socio-economic characteristics, infrastructure resilience, and community exposure, this study seeks to provide decision-makers with actionable insights to mitigate flood risks and enhance resilience (Scolobig et al.,2020).
Scope and Methodology:
The research methodology will involve a comprehensive review of existing literature on flood risk assessment and multi-criteria analysis methods. Subsequently, a case study approach will be employed to demonstrate the application of MCA techniques in assessing flood-prone areas. The study area will be selected based on its susceptibility to flooding and the availability of relevant data. Data collection will encompass spatial and non-spatial datasets obtained from remote sensing, geographic information systems (GIS).Multi-criteria analysis methods, such as Analytical Hierarchy Process (AHP) and Weighted Overlay Analysis (WOA), will be utilized to integrate and analyze the collected data, enabling the identification of high-risk areas. By employing multi-criteria analysis techniques, this research aims to contribute to the advancement of flood risk assessment methodologies to find out the flood prone areas .The findings of this study are expected to inform urban planners, and disaster management authorities in their decision-making processes, facilitating the implementation of proactive measures to reduce flood vulnerability and enhance community resilience.
Study Area:
Srivaikuntam is situated in the southern part of Tamil Nadu, approximately 40 kilometers northwest of the district headquarters, Thoothukudi (also known as Tuticorin). It lies within the geographical coordinates of approximately 8.6242° North latitude and 77.9762° East longitude.The topography of Srivaikuntam and its surrounding areas is predominantly flat, typical of the coastal plains of Tamil Nadu. Srivaikuntam's geography is characterized by its coastal location, fertile plains, and agricultural importance within the Thoothukudi district of Tamil Nadu.The town is situated on relatively low-lying terrain, with gentle undulations in the landscape. The region is characterized by fertile agricultural land, which supports the cultivation of various crops.Srivaikuntam experiences a tropical climate, influenced by its proximity to the Arabian Sea. Summers (March to June) are typically hot and humid, with temperatures often exceeding 30°C (86°F).
Monsoon season (June to September) brings heavy rainfall, vital for agriculture. Winters (December to February) are relatively mild and dry, with temperatures ranging from 20°C to 25°C (68°F to 77°F). While Srivaikuntam town itself is not located directly on the coast, it is relatively close to the Arabian Sea. The town may have access to nearby rivers particularly Tamiraparani which is perennial, streams, or irrigation channels that contribute to its agricultural productivity. Additionally, there may be ponds or small water bodies within or around the town, providing water for domestic use and irrigation.The vegetation in Srivaikuntam and its surroundings primarily consists of agricultural crops such as paddy, sugarcane, coconut, and various vegetables. Additionally, there may be patches of scrubland or natural vegetation interspersed throughout the area.Srivaikuntam is well- connected by road networks to nearby towns and cities. The nearest major transportation hub is Thoothukudi, which offers railway connectivity as well. While the town itself may not have significant industrial or commercial infrastructure, its connectivity to larger urban centers facilitates economic activities and transportation of goods and services.
Materials And Methods:
In the present study,the flooding area in the Srivaikuntam taluk has been created using the weighted overlay examination of various thematic grid layer comprising of elevation, slope, rainfall data, drainage density, land use-land cover(LULC), soil type, Topographic Wetness Index(TWI), Normalized Difference Vegetation Index (NDVI), distance from river, distance from road using ArcGIS 10.8. Using multi-criteria analysis and GIS-based spatial data, the current study identified flood hazard zones. Using the Analytic Hierarchy Process (AHP) method (Saaty, 1980), the relative weights of the criteria were determined by systematically analysing the links between the chosen criteria while taking the flood's impact into account. The thematic layers of the selected criteria were prepared, and their value range was clustered for assigning ratings. Jenks’ natural break clustering technique (Jenks, 1967) was used for all criteria, except qualitative parameters like land use type, geology, and so on that have predefined discrete class values. The Natural breaks classification technique is popularly applied in MCA-based flood studies because it divides the data range into clusters based on natural groups (Huan, Wang, & Teng, 2012; Papaioannou et al., 2015; Stefanidis & Stathis, 2013). This study used natural breaks technique because it reduces variance within the classes and maximizes interclass variability (Huan et al., 2012). Therefore, this method minimizes the mean deviation of each class from its mean concomitant with maximizing deviation from the means of other class groups (Stefanidis & Stathis, 2013).
For the present analysis the satellite data is accessed through Google Earth Pro, and DEM (ALOS30) has been extracted from Advanced Spaceborne Thermal Emission and Reflect Radiometer (ASTER (www.opentopography.org),landsat-8 downloaded from United States Geological Survey (USGS) portal with a 30 m resolution data. From ASTER DEM, slope map, aspect map, hill shade map has been generated. By using landsat8 data NDVI map has been generated. Boundary files has been downloaded from Geological Survey of India (GSI). Soil type data has been downloaded from Food and Agricultural Organization (FAO), A high-quality and accurate Sentinel 2 land use and land cover (LULC) map of 2021 with 10 m spatial resolution was downloaded from the Environmental System Research Institute (ESRI) website land cover has five classes as waterbodies, vegetation, agricultural land, settlement, barren/rage land. Rainfall data has been downloaded from Indian Meteorological Department (IMD) Pune. The river map with 15 arc-second resolutions was downloaded from the HydroSHEDS website. Using river, stream and road data of Srivaikuntam are used to produce distance from river and distance from road map. The analytical hierarchy process (AHP) model was used to reclassify all flood-controlling factor maps and then assigned a relative weight of importance to each factor All raster factor maps were reclassified to a common measurement scale from 1 (very low) to 5 (very high) using Reclassify tool of Spatial Analyst Tools.With help of ArcGIS tools TWI of the present study region is calculated and represented. Combining the relative weights of the criteria and parameter ratings, the flood hazard map was prepared in GIS environment. ArcGIS 10.8 and Microsoft Excel software were exhaustively used to process, create, and overlay digital raster layers, and for AHP analysis, respectively.
There is no set list of factors to take into account when employing multi-criteria analysis for flood susceptibility mapping, nor is there a standard method for choosing them. Eleven criteria that are closely linked to the occurrence of floods were chosen for this study based on the evaluation of earlier research, the availability of data, professional judgement, and the physical and natural configuration of the study area. The following describes the factors taken into account for this study as well as the procedures used to process and generate each factor map. To create an elevation factor map, the district's digital elevation model (DEM), which is a raster representation of a continuous surface where each cell represents the elevation of a specific location, has been reclassified into five flood susceptibility classes and rescaled. Using the slope and curvature tools in the ArcGIS environment's Spatial Analyst Tools, the slope and curvature maps were directly generated from the DEM map of the study area.
After the district's DEM map was filled in to produce a depression less/free of sinks DEM, the flow direction map—which shows the way the stream flows in each cell—was produced. The flow direction map was then used to construct the flow accumulation raster map. Hydrology functions like Fill, Flow Direction, and Flow Accumulation, respectively, in the Spatial Analyst functions of ArcGIS programme were used to fill the DEM map, create flow direction, and calculate flow accumulation.
In order to create a continuous rainfall map of the current research region, the rainfall data from IMD Pune is interpolated using the Inverse Distance Weighted (IDW) method in the ArcGIS10.8 environment and clipped using the district boundary shapefile. Utilizing the Raster calculator tool in the ArcGIS environment, the drainage network map was produced from the flow accumulation map. Afterwards, the drainage density map was obtained from the drainage network map by utilising the Line Density tool in the Spatial Analyst Tools of ArcGIS 10.8 programme. Following the extraction of study area rivers and roads from the river network and road network data downloaded from the HydroSHEDS website and Diva GIS website, the Euclidean Distance tool in the Spatial Analyst Tools of ArcGIS environment was used to create the distance to the river and distance to road map.
The district's Topographic Wetness Index (TWI) map was created using equation which was recommended by Moore et al. (1991). The TWI map was created using the Raster Calculator of the Spatial Analyst Tools in the ArcGIS environment.
Where "As" is the cumulative upslope area draining via a location (per unit contour length), B is the local slope angle in degrees, and W is the topographic wetness index.
The district's land use and land cover (LULC) map was first taken from the LULC map that was acquired from the ESRI website in order to create the LULC factor map. Next, as the classes are determined by numbers in the downloaded LULC map, appropriate land use and land cover (LULC) class names were allocated to each land use and land cover using ArcGIS software. Soil map has been downloaded from FAO and extracted according to the study area’s and allotted appropriate legends in reference with the parent site.
For NDVI extraction Landsat 8 image has been used its is prepared in ArcGIS 10.8 By applying the below given formula using raster calculator:
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NDVI=B5-B4/B5+B4
The flood-controlling parameters were prepared in raster format and then categorised into five common measurement scales, ranging from 1 (extremely low susceptibility to flooding) to 5 (very high susceptibility to flooding). No information was removed from the scales. A higher classified ranking value of (5) indicates that an area is more vulnerable to flooding, whilst a lower value of (1) indicates that an area is less vulnerable to flooding. The classifications of all flood-controlling elements were established based on the evaluation of earlier research and the regional context of the study area, as there is no standard reclassification scale for these components.
The most widely used and efficient method in the multi-criteria decision-making (MCDM) process is the analytical hierarchy process (AHP), proposed by Saaty (1987). Many previous studies (Abdelkarim et al. 2020; Ajibade et al. 2021; Allafta and Opp 2021; Astutik et al. 2021; Aydin and BirincioÄŸlu 2022; Danumah et al. 2016; Das and Gupta 2021; Elsheikh et al. 2015; Karymbalis et al. 2021; Mahmoud and Gan 2018; Ogato et al. 2020) have used this method to weight each flood-controlling factors and, finally, to identify and map flood-prone areas.
The weights assigned to the variables used in the multi-criteria decision-making process for mapping flood vulnerability were determined by the physical attributes of the study region and the evaluation of earlier research. The following systematic methods were utilised to determine relative weights for each flood-controlling element that was used in this investigation, as recommended by Saaty (1987).
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To create the pairwise comparison matrix, a value between 1 and 9 was assigned to each element based on their relative relevance. The scale states that 1 denotes equal significance and 9 denotes great importance.
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Next, by dividing each value in each column of the pairwise comparison matrix by the total of the columns, the normalised pairwise comparison matrix table was created.
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The third stage included calculating the weight of each component by dividing the total of each row in the table of normalised pairwise comparisons by the number of factors (in this case, eleven).
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Following the weight calculations for each flood-controlling element, the consistency of the comparison was checked using the following equations to see if it was accurate and consistent. Equation (provided by Saaty, 1987) is used to determine the consistency index (CI).
where λmax is the greatest eigenvalue of the pairwise comparison matrix, n is the number of components being compared in the matrix, and CI is the consistency index.
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Following the advice of Saaty (1987), the following steps were taken to determine the comparison matrix's highest eigenvalue (λmax) :
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Applying the weight criterion to each value in the column (in the unnormalized matrix table)
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Adding the values in the rows to get the weighted total value.
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Computing the ratio between each weighted total value and the weight assigned to that criterion, and
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Taking the average of the weighted aggregate value divided by the weight criterion.
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Lastly, to confirm the consistency of the comparison, the consistency ratio (CR) was calculated using the equation proposed by Saaty (1987).
where RI is the random index that changes depending on how many components are utilised in the pairwise comparison matrix, CI is the consistency index, and CR is the consistency ratio. The pairwise comparison matrix has satisfactory consistency if the CR is less than 0.10. In the event that the CR is larger than or equal to 0.10, pairwise comparison is not consistent enough, and comparisons need to be made again until the CR value falls below 0.10 (Saaty 1987).
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The flood susceptibility map of the study area was obtained by integrating and overlaying the spatial layers using the weighted overlay technique in the Spatial Analyst Extension of the ArcGIS environment after each flood-controlling factor was prepared, reclassified using ArcGIS software to a common measurement scale of 1 (very low) to 5 (very high), and the factors were weighted using the AHP approach. This equation was used to create the flood vulnerability map in several earlier research (Ali et al. 2020; Allafta and Opp 2021; Aydin and BirincioÄŸlu 2022; Das and Gupta 2021; Dash and Sar 2020; Hadipour et al. 2020; Kanani- Sadat et al. 2019).
where n is the number of decision criteria, xi is the specific normalised criterion, wi is the corresponding weight of the criterion, and FS is the flood susceptibility. The flood susceptibility output raster map is produced by multiplying the cell/pixel values of the raster layers by the weight/percentage effect determined by AHP analysis, and then adding the findings.
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Potential areas susceptible to flooding were identified and mapped using the eleven flood- controlling factors used in the study, which included elevation, slope, flow accumulation, distance from rivers, rainfall, drainage density, topographic wetness index, land use and land cover, Normalised Difference Vegetation Index, soil type, and distance from road map. The spatial distribution of flood vulnerability in the research region was determined and mapped by examining and evaluating these variables. The examination of each component is shown in further detail below.
Result And Discussion:
Elevation:
Elevation is a factor in determining flood danger. Because lower elevated areas have a higher river discharge and flood more quickly from high water flows, they generally have a higher probability of experiencing floods than higher elevated areas (Hong et al. 2018a, b; Lee and Rezaie, 2022; Zzaman et al. 2021).The altitude of the area ranges from 316 to -33 therefore most of the area near in the present study is low lying area. Hence in accordance with elevation(one of the criteria for the study) the present study area will be high possible zone for flood.
Elevation Map
Slope:
The speed at which surface water flows is determined by the land's slope. The volume of water covering the land and the likelihood of a flood rise when the slope declines and surface water flow velocity drops (Astutik et al. 2021; Das and Gupta 2021; Zzaman et al. 2021). Lowlands and flatlands with mild slopes are more likely to experience flooding than mountainous places, which often have steeper slopes that prevent water from collecting (Wang et al. 2015). Slope map shows the area in Srivaikuntam has lower of 1.3 to higher of 38 degree slope. The higher the value the lesser the flood possibility because of the high flow velocity in the steeper zone.
Rainfall:
Since we cannot imagine a flood occurring without rainfall, it is imperative that rainfall be taken into account as a component in flood susceptibility studies. Flood inundation is caused by a massive volume of runoff flows as a result of overly heavy rainfall or protracted rainfall, making it the most important element that triggers the occurrence of floods (Allafta and Opp 2021; Hong et al. 2018a, b).The rainfall value ranging in between 63mm to 71 mm.
Slope map of the study area
Rainfall map
Distance from Road:
​Since surplus water from rivers first reaches beside river banks and adjacent lowland regions, places near rivers have a higher likelihood of flooding than areas farther away from rivers (Mahmoud and Gan 2018). This is due to the fact that the slope and elevation rise with increasing distance (Lee and Rezaie 2022; Zzaman et al. 2021).Hence analysing the nearby road helps to identify roads nearby which has more chances of flood.
Distance of river to the nearby road map
Distance from River:
In order to as certain if and to what degree a region may be impacted by flooding, it is crucial to consider its distance from the stream. An region is less likely to be harmed by flooding if it is farther from a river. Studies indicate that because of overflow, the regions closest to these rivers are most impacted by floods.
The Euclidean distance tool in ArcGIS was used to create this map, and it revealed five buffer groups as like mentioned in the map.
Distance of nearby area from the river
Drainage Density:
According to Zzaman et al. (2021) the drainage density is the ratio of the total length of streams in a given region to the area's size. Surface runoff and the likelihood of floods increase with drainage density (Abdelkarim et al. 2020; Das and Gupta 2021; Lee and Rezaie 2022; Mahmoud and Gan 2018). In a research published in 2020, Ali et al. classified as very low, low, moderate, high, and very high vulnerability to flooding regions with drainage density values less than 0.45 km/km2, 0.45–1.01 km/km2, 1.01–1.64 km/km2, 1.64–2.47 km/km2, and higher than 2.47 km/km2, respectively.In the present study the rage values are represented into five class.
Drainage density map of the study area
TWI:
The Topographic Wetness Index quantifies the impact of topography on the volume of runoff generated and the accumulation of flow at a certain location. It illustrates how gravity pressure causes water to gravitationally pool at a certain location or flow downward (Lee and Rezaie 2022). The Topographic Wetness Index has the ability to forecast regions that might experience overland flow and those that are prone to saturated land surfaces (Hong et al. 2018a, b). The TWI and flood risk are tightly correlated; the higher the TWI value, the higher the chance of flooding (Das and Gupta 2021). Hence TWI of the present study is classified in to five class to denote the high to low possibility of flood.
TWI
LAND USE LAND COVER (LULC):
Land usage and land cover are among the most significant variables influencing the likelihood of flooding. A high density of vegetation reduces the quick flow of water and increases infiltration, therefore regions covered by it are frequently less susceptible to flooding. Conversely, impermeable surfaces and minimal infiltration cause runoff to grow in residential and urban locations (Allafta and Opp 2021; Das and Gupta 2021; Kazakis et al. 2015; Zzaman et al. 2021). According to Allafta and Opp (2021), there is extremely low, low, moderate, high, and very high sensitivity to floods for shrub land, agriculture, bare land, urban areas, and waterbodies, respectively. Waterbodies, build-up, agricultural, sparse and dense vegetation were classified as very high, high, moderate, low, and very low vulnerable to flooding by Das and Gupta (2021). Additionally, built-up regions, farming, grassland, shrubland, and forestland areas were categorized by Hagos et al. (2022) as having varying degrees of sensitivity to flooding: extremely high, high, moderate, low, and extremely low.Similarly LULC map of the present study area is categorized in to five according to the features of the area.
Land Use Land Cover map of the study area
Soil Types:
​The type of soil has a major influence on the infiltration process. The soil's fine-textured nature enhances surface runoff and decreases infiltration. Therefore, compared to areas with coarse soil texture, areas covered with fine soil texture have a higher likelihood of flooding (Allafta and Opp 2021; Hagos et al. 2022; Hong et al. 2018a, b).There area four types of soil are present in this present study area.
Soil type map
NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI):
Normalized Disparity One of the variables used to determine flood susceptibility is the vegetation index, which is an index that shows the density of vegetation over a certain area (Ali et al. 2020). Increased vegetation density slows down runoff and reduces the amount of flooding that occurs (Tehrany et al. 2017).
NDVI map
AHP:
Following the categorization of every flood-controlling component an AHP analysis
was conducted to determine the relative influence or weight of the factors under consideration for a weighted overlay. Following the steps recommended by Saaty (1987), a pairwise comparison matrix was created , the pairwise comparison's normalization and factor weight were calculated, and the comparison's consistency was checked.
Output of AHP analysis
AHP output in bar diagram
FLOOD SUSCEPTIBILITY MAP:
The final susceptibility map of Srivaikuntram Taluk has been developed by integration of eleven flood controlling factor’s thematic maps.The weighted overlay integration classifies the entire area into five flood susceptibility classes :very high(5),high(4), moderate (3), low (2), and very low (1) susceptibility.In accordance with all the factors the result of the analysis represent that the area is more prone to flood especially in rainy season.
Final output of MCA flood map
Conclusion:-
In conclusion, the application of multi-criteria analysis (MCA) in flood management decision- making offers a comprehensive approach to address the complexities inherent in flood risk reduction strategies. By considering diverse criteria such as economic, social, environmental, and technical factors, MCA enables stakeholders and policymakers to assess trade-offs and identify optimal flood management alternatives. Through engagement and expert input, this study has demonstrated the utility of MCA in prioritizing flood management options, leading to informed and robust decision-making processes. Moving forward, continued research and implementation of MCA methodologies will be crucial in enhancing flood resilience and minimizing the adverse impacts of floods on communities, infrastructure, and the environment. The integration of multi-criteria analysis and AHP into flood risk assessment represents a promising approach to address the complex challenges posed by flood hazards. By considering multiple criteria and perspectives, MCA enables a more comprehensive understanding of flood vulnerability, thereby supporting informed decision-making and adaptive risk management strategies.