Assessment of Vulnerability to Urban Floods in Greater Mumbai, India Using Geospatial Techniques

Assessment of Vulnerability to Urban Floods in Greater Mumbai, India Using Geospatial Techniques | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessment of Vulnerability to Urban Floods in Greater Mumbai, India Using Geospatial Techniques Rohit Mann, Anju Gupta This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2171279/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Urban flooding as well as its socio-economic repercussions is dramatically escalating globally in several coastal cities due to changes in rainfall patterns triggered by climate change. The principal aim of the study is to evaluate the flood vulnerable locations in Mumbai by using a multi-criteria evaluation (MCE) approach. The weights of flood-vulnerable impacting parameters like rainfall, slope, land use/cover (LULC), vicinity to sewers and storm water drainage, vicinity to natural drainage, vegetation, and soil are evaluated using the Analytical Hierarchy Process (AHP). The weights for said parameters are determined by using AHP, and they are as follows: rainfall (29.42%), slope (20.96%), LULC (17.52%), vicinity to sewers and storm water drainage (13.99%), vicinity to natural drainage (8.97%), vegetation (5.58%), and soil (3.56%). In the study area, it is estimated that 46.72% of the area is classified as being severe vulnerable, 18.74% of the area as high to very high and just 34.54% of the area as slight to moderate. Researchers were able to validate the modelling conclusion by examination of all 234 water-logged locations in the study area. Most of the water-logged spots i.e. 85.46% were found in areas that fall under the category of severe to very high vulnerability and only 14% of locations fall in other three categories as per the data of the flooding sites that are collected from MCGM authorities. These model-based flood vulnerable scenario maps are crucial for planning flood conservation and control measures to prioritize the area based on the degree of vulnerability. Urban Flooding Multi-Criteria Assessment (MCE) Analytical Hierarchy Process (AHP) Vulnerability GIS Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction The newly induced phenomena of global warming altered the rainfall patterns all over the world which in turn affects the city life that often results in stagnation of water in many coastal cities (Rakhecha and Pisharoty 1996; Fowler and Kilsby 2003 ; Goswami et al. 2006 ; Guhathakurta and Rajeevan 2008 ; Rajeevan et al. 2008), leading to socio-economic impacts and consequences like traffic congestion, halt of city life and many more. The primary causes of flood in a region are population growth, fast urbanisation, changes in river channel morphology brought on by anthropogenic and natural interventions, as well as heavy rainfall (Starr et al. 1978; Korhonen and Lewandowski 1989 ; Schmold et al. 2001; Young 2006; Young et al. 2009, 2011; Ahmadisharaf et al. 2015 ). As a result, the connection between urbanisation and local climate change has caught the interest of many academicians worldwide (Kalnay and Cai 2003 ; He et al. 2007 ). Rapid urbanisation has an impact on land surface qualities, which can change the diurnal, seasonal and long-term meteorological and climatic conditions at local, regional, and global scale (Lowry 1977; Ohashi and Kida 2002; Chen et al. 2006 ; Rosenzweig et al. 2008; Mutizwa-Mangiza et al. 2011). Mumbai is extremely vulnerable to frequent floods during monsoon season because of its unique geographical control, strong building activity, and significant monsoon rainfall. It is encountering three major types of floods: localized flooding brought on by poor drainage; flooding brought on by Mithi River overflows where settlements have been established in flood plains; and flooding brought on by a combination of high tide and river overland flow. Localized flooding is mostly caused by unplanned settlements in drain paths, an inadequate drainage system, and reduced drain capacity from waterlogging. The flood threat has been impacted and made worse by the city's land use strategies, solid waste management procedures, and drainage upkeep (Zope et al. 2017). As a result, the city is quite vulnerable to flooding, which can have negative consequences on settlements, the road system, and everyday life for residents (Wadge et al. 1993; Blazkova and Beven 1997 ). The creation of a map of the flood vulnerable zones is essential for future planning and construction projects as well as the flood hazard management to lessen the effects of floods (Farajzadeh 2002 ; Tehrany et al. 2014). Geographic information system (GIS) and remote sensing technology have recently added fresh perspective to flood investigations (Masmoudi and Habaieb 1993; Islam and Sado 2001 , 2002 ). Due to the multi-temporal dataset involved in flood susceptibility mapping, the GIS tool makes it easier to create, manage, and integrate a database of flood episodes as well as various contributing elements (Moore 1986; Merwade et al. 2008). Weights-of-evidence (Porwal et al. 2010; Oh and Lee 2010; Armas 2012 ; Lee et al. 2012a ; Fu et al. 2013 ; Pourghasemi et al. 2013b: Rahmati et al. 2016), analytic hierarchy process (AHP) (here after uses AHP) (Schmold et al. 2001; Young 2006; Young et al. 2009: Anagnostopoulos and Vavatsikos 2012 ; Pourghasemi et al. 2012, 2013a; Park et al. 2013; Althuwaynee et al. 2014 ), Frequency ratio (FR) (Poudyal et al. 2010; Lee et al. 2012b ; Ozdemir and Altural 2013; Park et al. 2013; Jaafari et al. 2014 ; Regmi et al. 2014; Naghibi et al. 2015: Rahmati et al. 2016), fuzzy logic (Ercanoglu and Gokceoglu 2002 ; Pourghasemi et al. 2012; Sharma et al. 2013; Zou et al. 2013; Ramazi and Amini 2014) logistic regression (Porwal et al. 2010; Ozdemir and Altural 2013; Park et al. 2013; Pourghasemi et al. 2013a), artificial neural network (Kia et al. 2012 ; Zare et al. 2013), decision tree (Yeon et al. 2010; Shafapour et al. 2013), evidential belief function (EBF) (Tehrany and Kumar 2018), adaptive neuro fuzzy inference system (Termeh et al. 2018) and support vector machine (Choubin et al. 2019 ) are the most widely used techniques that make use of geospatial data useful among the various GIS-based flood vulnerable models used in the literature. As reported in the literature, all of these models were widely utilized in the analytical vulnerability assessment of any common hazard. Numerous studies on floods in Mumbai and Navi Mumbai have been conducted in the past, with a major focus on rainfall modelling (Shahapure et al. 2011), urban flood resilience management and planning (Gupta 2007 ), vulnerability mapping for disaster estimation (Reshma and Deepankar 2015), the impacts of urbanisation on flooding (Zope et al. 2015), integrated flood assessment modelling tool for the coastal urban flood simulation (Kulkarni et al. 2014a ), integrated flood inundation model (Kulkarni et al. 2014b ) and hydrological repercussions of land use land cover (LULC) change on hazard (Zope et al. 2017) related to urban floods. These investigations were conducted independently, without establishing any connection to the underlying mechanisms or variables that are frequently claimed to have an impact on urban flooding. 1.1 Multi-Criteria Evaluation (MCE) Approach based on GIS The geographical analysis and 2-D and 3-D display of geographic data to enhance environmental decision-making is one of the most significant uses of GIS. A decision is a choice between two or more alternatives, which could be two different courses of action, places to go, things to buy, etc. GIS can provide greater information about circumstances requiring decision-making because 80% of the data used by decision-makers is location-based (Starr et al. 1978). With the help of the overlay process in GIS, the decision-maker can locate a list that satisfies a pre-determined set of requirements (Abrishamchi et al. 2005 ). Multi-criteria evaluation (MCE) (here after uses MCE) techniques based on GIS have been used in a plethora of studies (Fernandez and Lutz 2010 ; Afshar et al. 2011 ; Chung et al. 2011 ; Ahammed et al. 2012 ; Ahmadisharaf et al. 2015 ). All criteria are permitted to be Boolean (i.e., logical true/false) statements of fitness for the decision being considered. Techniques for resource appraisal and site selection that rely on conventional Boolean logic, meanwhile, have already been identified to have issues (Jansssen and Rietveld 1990 ). Loss of data may occur in circumstances when the minimum value is not exact. Additionally, the method provides no analytical opportunity to ascertain that which of the locations meeting the criteria is most suitable for the intended purpose. Boolean logic has been replaced with MCE techniques due to issues with Boolean overlay (Pereira and Itami 1991). In that both an index model and a binary model require MCE for appropriateness and vulnerability analysis, they are comparable to one another. Instead of a simple yes or no, an index model generates an index score for each unit area. The process for calculating the index score is the main factor to take into account while creating an index model, and further to identify whether it is vector or raster based. The weighted linear combination approach, which generates a ranking map based on the index scores, is undoubtedly the most frequently used method for calculating the index score for each unit area (Althuwaynee et al. 2014 ). The following formula is used to determine the index score: n is the criterion, W are the weight, and X seems to be the standard score, where \({I}_{i}\) is the index score. $${I}_{i}= \sum _{i = 1}^{n}{W}_{i}{X}_{i} \left(1\right)$$ In essence, the computation of index score involves three steps: Step 1: Compare each criterion's relative weight to that of the others. Step 2: Each criterion's values should be standardized. Step 3: Multiplying the results of the weighting and standard value into an index score. One of the most widely used techniques for determining criteria weights in MCE is the AHP (Park et al. 2013), which uses an authoritative pair-wise comparison matrix with their weights. Relying on the AHP, Siddiqui et al. (1996) offered an additive solution to a spatial problem. According to Rao et al. (1991) the process of pair-wise comparisons in AHP is a reasonable one for the establishment of criteria weights. The weighted linear conjunction operator, which is frequently employed with these factors, has been demonstrated to lie on a continuous with these operators, where it reflects the scenario of full trade-off between the parameters under consideration as well as intermediate end or Boolean logic. Nevertheless, despite certain unknowns, numerous researchers have gathered the AHP success narratives in a variety of sectors. These studies recognized the AHP model combined with weighted linear combination in GIS as having a strong theoretical foundation and offering logic for the normalization of parameters, a justification for the expression of decision risk, and a high level of versatility in the site adaptability and vulnerability assessment (Hughes 1986 ; Carver 1991 ; Malczewski 2000). The most crucial elements influencing the quality of spatial data, as per Burrough and McDonnell ( 1998 ), are wholeness, coherence, transparency, precision, clarity, and process method. The primary aim of the current study is to create a crucial analytical spatial database of Mumbai that will aid in the identification of various contributing factors and the relative importance of each in terms of impacting flood situations, as well as the identification of flood vulnerable zones using RS and GIS. In order to designate flood-prone areas that should be avoided in future development planning, demarcation of flood vulnerable areas is necessary. With the aforementioned theoretical framework in mind, the current study used MCE in GIS with incorporation of AHP criterion and weighted linear combination method to identify flood-prone sites/areas using some triggering factors like rainfall, slope, LULC, vicinity to sewers and storm water drainage, vicinity to natural drainage, vegetation, and soil. 2 Study Area Greater Mumbai, which has a surface area of around 470 sq.km, stretches between latitude 18 ◦ 53ꞌ47ꞌꞌ − 19 ◦ 16ꞌ16ꞌꞌ to longitude 72 ◦ 48ꞌ30ꞌꞌ − 72 ◦ 51ꞌ11ꞌꞌ. The westernmost coastal region of the Indian state of Maharashtra is generally referred to as Mumbai. With Thane district to the east, Palghar to the west, and Raigad to the south-west, the city is considered to be an extreme coastal area and is surrounded by the western branch of the Indian Ocean and the Arabian Sea on three sides. As a result, the city sometimes considered as mini peninsula. The city also hosts a significant span of mangroves on its eastern and western coastlines, which seems to be 149 km in length. As an archipelago of islands, Mumbai's relief and terrain consisted of local hills, coastal cliffs, and ridges with marsh land in between; it possessed a total of 22 hills well before the fast development period, that has left Mumbai only with three hill ranges: the Ghatkopar hills in the northern part, the Trombay hills in south-east, as well as the highest Powai hills in north around Borivali or Sanjay Gandhi National Park. All of these hilly locations mostly encroach on the foothills, and degradation keeps seeping inside. The Mithi River, which rises near Vihar Lake in the north and rushes southward to reach the Arabian Sea, making an estuary along the Mahim Creek, is one of two major rivers that drain the study area. In contrast, the Oshiwara River begins from Powai hills and flows northwest to the Manori Creek. In addition to this, the highlands have a lot of smaller streams. These rivers have been transformed into sewers filled with tonnes of solid waste due to the hasty and quick urban expansion. Usually, the climate of the study area is tropical, with two distinct seasons wet and dry as well as places with high to extremely high rainfall. The seasons are marked by moderate heat and high levels of humidity. Due to its tropical climate, the average annual temperature of Mumbai is 27.2 ◦ C. As per IMD, Mumbai, the study area experiences 242.2 cm of rainfall annually. The metropolis of Mumbai has the seventh-highest population in the world. To facilitate administrative convenience, the entire region in Mumbai and Mumbai (Suburban) has been divided into wards. These wards are labeled A, B, C, etc. in alphabetical order. Mumbai district includes the wards from A to G/South and Mumbai (Suburban) district includes the wards from H/West to T. 3 Database And Methodology The current study is supported by the secondary data (table. 1). Secondary data is gathered through the internet, published reports, and the relevant public departments. Fig. 2 graphically depicts the steps taken to determine which locations are most susceptible to flooding. Table 1 Descriptive details of the data collected Data Source Specifications Output (Flood Influencing Factor) IMD, Pune Hourly Data Rainfall SRTM (Shuttle Radar Topography Mission) DEM (Digital Elevation Model) Spatial Resolution: 30 m Slope Satellite Image Landsat 5 and 8 TM & OLI/TIRS Landsat TM and OLI, Spatial Resolution: 30mt. Land Use Land Cover (LULC) BRIMSTOWAD-II (Brihanmumbai Storm Water Disposal System) Draft Master Plan Maps of Storm water drainage & Sewers Scale - 1: 50000, Year - 2014 Vicinity to sewers and storm water drainage Survey of India OSM Sheet, SRTM DEM Number - E43A/16(47A/16) Scale - 1:50,000, Spatial Resolution: 30 m Vicinity to Natural Drainage Satellite Image Landsat 5 and 8 NDVI; Spatial Resolution: 30mt. Vegetation National Bureau of Soil Survey Maharashtra Scale – 1:50000, Year - 1996 Soil Municipal Corporation of Greater Mumbai (MCGM) Ward-wise flood locations Map of waterlogging spots 3.1 Producing Maps of Flood Influencing Factors For each component, GIS maps are initially constructed using a standard geo-referencing approach. Rainfall and soil maps were prepared from the data obtained from IMD, Pune and National Bureau of Soil Survey, Maharashtra. By using surface-slope tool in ArcGIS, slope in percentage for the study area was retrieved from the computed SRTM DEM and SOI OSM sheet, which was later modified, using high resolution satellite images, and again used to digitize the map of natural drainage. The Euclidean distance measure in ARCGIS was used to calculate the vicinity for a distance equivalent to 1000 metres for natural drainage. The vegetation map is produced using TM Landsat 8 (30 mt.) image for the year 2020 and Normalized Difference Vegetation Index (NDVI) is performed using Erdas Imagine 2010 software as NDVI= (NIR-VIS)/(NIR+VIS). Using Landsat 8 (OLI/TIRS 30 mt.) multi-spectral data, the map of LULC for the study area was retrieved in ARCGIS software by employing supervised classification. Finally, the maps of sewers and storm water drainage from the BRIMSTOWAD-II Draft Plan were used to retrieve the map of artificial drainage. It is a component of the storm water drains project being undertaken in 2014 by the office of storm water drains under department of disaster management, MCGM. Using the Euclidean distance measure, the vicinity for a distance equivalent to 500 mt. was calculated. All maps are transformed to grid-based integer raster format with the same pixel size of 30 mt. for each parameter. At last, all the maps were overlaid to produce a combined map of flood vulnerable zones. Also, a ward-wise map of 234 flood locations is prepared from the data gathered from MCGM and then manually assesses the relative importance of each parameter against the flood locations. MCA is used to create and combine spatial data for characterizing the causative aspects in order to determine the vulnerability of flooding. In GIS context, the Weighted Linear Combination (WLC) Approach was employed to implement the AHP Pairwise Comparison Method. 3.2 Description and Order of Influencing Parameters Each aspect that is taken into account is ranked according to the preference of decision makers. Each component is rated according to the expected significance influence on floods in order to establish criterion scores for each sub-class category. These factors received an inverted ranking. Each sub-class is ranked 1–5 in decreasing order of impact based on reviewed literature and knowledge, where 5 represents high vulnerability to floods and 1 represents low vulnerability. Table 2 displays the grading scheme. The current study makes the assumption that the areas that are most susceptible to flooding depend on a variety of variables, including slope, amount of rainfall, vegetation, soil types, LULC practices, natural and artificial drainage network. As a result, depending on these variables, flood can vary considerably over time and location. The following seven parameters were used in the current study, and each of them is shown and saved in a distinct map with order of their sub-category is shown in fig. 3 and table 2. Table 2 Ranking of Flood Parameters and their Sub Categories Vulnerability Parameters Sub-category of Parameters Ranking 1. Rainfall >2300.01 5 2200.01-2300 4 2100.01-2200 3 2000.01-2100 2 <2000 1 2. Slope Very Gentle [below 5] 5 Gentle [5.01-10.00] 4 Moderate [10.01-15.00] 3 Steep [15.01-20.00] 2 Very Steep [20.01 & above] 1 3. LULC Built-up Area 5 Open Land 4 Cultivated Land 3 Water body 2 Vegetation Cover 1 4. Vicinity to Sewers & Storm Water Drainage 0-125 mt. 5 125-250 mt. 4 250-375 mt. 3 375-500 mt. 2 >500mt. 1 5. Vicinity to Natural Drainage 0-250 mt. 5 250-500 mt. 4 500-750 mt. 3 750-1000 mt. 2 >1000 mt. 1 6. Vegetation Lowest dense vegetation cover 5 Lower dense Vegetation Cover 4 Dense Vegetation Cover 3 Higher Dense Vegetation Cover 2 Highest dense Vegetation Cover 1 7. Soil Settlement Coastal Alluvium 5 Mud Marsh 4 Vertic Halaquepts 3 Vertic Ustrepepts 2 Typic Ustorthents 1 3.2.1 Rainfall Rainfall is the primary hydrological component that is most frequently employed in studies of floods. Rainfall is the term used to describe the dispersion of liquid droplets over space and time, which regulates the surface runoff (Goswami et al. 2006). Since areas with higher rainfall than the annual average are more likely to experience flooding, high rainfall amounts are a marker of substantial flood susceptibility. As a result, a category weight of 5 is allocated to heavy rainfall zone and 1 is allocated to relatively low rainfall zone (table. 2). 3.2.2 Slope The slope is the most important aspect in hydrology since it directly affects the surface runoff and floods. Since sites of low-elevation often have a gentle or level slope, they are more susceptible to flooding and water logging because steep slopes generates huge velocity of runoff than flat or gentle slopes and dispose of storm runoff more quickly (Altaf et al. 2013). Runoff from a level or gently sloping land is accumulated and released gradually over time (Tehrany and Kumar 2018). In contrast to high gradient slopes, low gradient slopes even more susceptible to flooding. Historically, the study area was an archipelago of seven islands that has been reclaimed and established as a land of concrete slabs over a span of five centuries. Additionally, the steeper slope was exploited to provide flat homes for a large number of migrants. As a result, places with very gentle slopes were assigned a class rating of 5, whereas locations with high relief were given lower ranking i.e., 1. 3.3.3 LULC Recognizing the activities taking on in a location and the various categories of LULC being impacted by recurrent floods is crucial for vulnerability mapping. Due to their significant use of impermeable surfaces, urban areas are impacted by storm water runoff (Fernandez and Lutz 2010). Built-up area dominates the LULC category in the study area. Slums or nucleated communities are the main components of dense built-up areas, which are primarily found in the city's central and Southern parts. Evidently, areas with dense built-up space are at a larger risk of flooding than areas with less built-up land cover. Therefore, rankings are allocated as shown in table 2 based on the kind of land use and its susceptibility to floods. 3.3.4 Vicinity to sewers and storm water drainage With growing pollution and a lack of concern for it, the sewerage system has emerged as a crucial component and responsibility of the city administration. Sewers are man-made drains that are used to move sewage from homes to disposal sites. The Storm Water Drains (SWD) is specialized man-made drains that assist in moving and draining extra storm runoff from the city to the countryside and thus minimizes floods. The water-logging in most parts of a megacity like Mumbai is caused by the overflow from these drains, which are primarily ineffective at holding or draining water, therefore it is crucial to take this into account while analysing the vulnerability of flooding. 3.3.5 Vicinity to natural drainage The intensity of flooding was thought to be impacted by vicinity to the natural drainage, implying that the area around some stream or river is quite vulnerable to floods. Here, all streams and rivers are considered to be a part of natural drainage. 3.3.6 Vegetation Cover Vegetation cover is the parameter that has the significant impact on flood vulnerability. Flood susceptibility may grow as vegetation cover declines. This connection is the basis for the widespread inclusion of tree cover and land use in studies of flood susceptibility (Young et al. 2009). Insufficient tree cover under barren or open land makes the area highly vulnerable to flooding; a maximum class weight of 5 is allocated to it, with subsequent classes receiving less weight in the sequence of increasing the area under tree cover. 3.3.7 Soil Type Since soil properties, particularly those that are more likely to erode, are more vulnerable to floods because the rate of permeability are depends on soil characteristic of a region. Therefore, soil type was chosen as an influencing parameter (Chung et al. 2011). The study area has two main soil types’ viz vertic halaquepts and coastal alluvium (Soil group-Inceptisols). These soils are very fine, slightly deep, poorly drained and moderately salinized. They are located on very gently sloping areas in residual hills. These soils generally equate to black and laterite in the context of Indian soil classification (Pal 2013). 4.1 Application of AHP to Evaluate the Parameters Weight Determining the weights for flood influential parameters is a complex problem that involves different criterion functions. If such a scenario is not handled with a reasonable and well-processed methodology, often results in miscalculation of the facts. The MCE technique has the potential to logically resolve this matter regarding multiple criteria. The AHP technique which Saaty devised was applied in the current investigation (Saaty 1977). With the use of a preference matrix, in which all recognized relevant criteria are contrasted against one another with replicable preference parameters, AHP is a very well-known and widely used statistical method to determine the necessary weights of each parameter. A pair-wise evaluation matrix, a measure to represent the proportional priority among the components, examines all parameters that are thought to be essential for a decision against one another. As a result, each parameter needs to be given a quantitative score conveying a decision of the importance of one variable compared to another. A scale for comparison with scores ranging from 1 to 9 which represent the intensity of importance was proposed by Saaty and Vargas (1991). Since it has been recognized through psychological studies, a person cannot compare more than 7 ± 2 variables at once. One represents "equal importance," whereas nine represents variable that are "extremely important" in comparison to other criteria (table. 3). 4.2 Construction of a Pair-wise Comparison Matrix A pair-wise comparison matrix of order 7 is shown in table 4 and compares seven parameters (C1, C2, C3, C4, C5, C6, and C7). When parameter C1 and parameter C2 are directly compared, parameter C1 is seen as equal to moderate relevance and the remaining parameter is allocated the same relative weight. The reciprocal of 1/4, or 0.25, is immediately applied to the transposed position. 4.3 Standardized Pair-Wise Comparison Matrix Intensity of Importance Explanation 1 Equal Importance 2 Equal to moderate Importance 3 Moderate Importance 4 Moderate to Strong Importance 5 Strong Importance 6 Strong to very Strong Importance 7 Very Strong Importance 8 Very to Extremely strong Importance 9 Extreme Importance Corresponding Values for Inverse Comparison The stated preference scores are combined in the following phase to arrive at a quantitative score that represents the weights of the parameters. As a result, Eigen scores and vectors of square preference matrix, that disclose key information about patterns in the data matrix, are computed. Seven eigen scores are provided by the square matrix of order seven mentioned below, which can be used to calculate seven eigen vectors, each of which has seven vector components. Since this Eigen vector provides enough information to show by its eigen vector components - the relative emphases of the parameters being investigated, it is viewed as adequate to compute only its eigen vector deriving from the biggest eigen score (Saaty and Vargas 1991). The pair-wise matrix is standardized, and the parameter weights are represented by the standardized matrix's eigen scores, which are produced as shown in table Table 3 A sampled comparison scale Intensity of Importance Explanation 1 Equal Importance 2 Equal to moderate Importance 3 Moderate Importance 4 Moderate to Strong Importance 5 Strong Importance 6 Strong to very Strong Importance 7 Very Strong Importance 8 Very to Extremely strong Importance 9 Extreme Importance Corresponding Values for Inverse Comparison Table 4 Construction of a Pair-Wise Comparison Matrix Table 5 Standardized Pair-Wise Matrix Parameters Rainfall Slope LULC Vicinity to Sewers & Storm Water Drainage Vicinity to Natural Drainage Vegetation Soil Total Priority Vector Weight (%) C1 C2 C3 C4 C5 C6 C7 Rainfall C1 0.33 0.41 0.31 0.34 0.27 0.20 0.19 2.06 0.29 29.42 Slope C2 0.17 0.21 0.31 0.23 0.21 0.20 0.15 1.47 0.21 20.96 LULC C3 0.17 0.10 0.16 0.23 0.14 0.25 0.19 1.23 0.18 17.52 Vicinity to Sewers & Storm Water Drainage C4 0.11 0.10 0.08 0.11 0.27 0.15 0.15 0.98 0.14 13.99 Vicinity to Natural Drainage C5 0.08 0.07 0.08 0.03 0.07 0.15 0.15 0.63 0.09 8.97 Vegetation C6 0.08 0.05 0.03 0.04 0.02 0.05 0.12 0.39 0.06 5.58 Soil C7 0.07 0.05 0.03 0.03 0.02 0.02 0.04 0.25 0.04 3.56 Total 1.00 1.00 1.00 1.00 1.00 1.00 1.00 7.00 1.00 100.00 4.4 Computing Consistency Ratio To evaluate how reliable the assessments have been in comparison to sizable samples of merely random assessments, the consistency ratio (CR) is determined at this point. The AHP always permits a certain degree of consistency, but it shouldn't go beyond a certain point. The consistency ratio (CR), that evaluates the level of consistency, is determined using the Random inconsistency index (RI) (table 6) created by Saaty (1980). If the CR is substantially greater than 0.1, assessments are unreliable because they are too close to randomness. If the CR value is less than or equivalent to 0.1, the inconsistency is acceptable, or else the pair-wise comparison may be altered (Saaty, 1980). The weights are therefore acceptable. Table 6 Different size matrices with random indices n 2 3 4 5 6 7 8 9 10 RI 0.00 0.52 0.90 1.12 1.24 1.32 1.41 1.45 1.49 Source: Saaty and Vargas 1991. Table 7 Computing the Consistency Ratio (CR) Parameters C1 C2 C3 C4 C5 C6 C7 Criteria Weight Criteria weight Consistency Vector Rainfall C1 1 2 2 3 4 4 5 0.29 2.24 7.63 Slope C2 0.50 1 2 2 3 4 4 0.21 1.62 7.74 LULC C3 0.50 0.5 1 2 2 5 5 0.18 1.34 7.67 Vicinity to Sewers & Storm Water Drainage C4 0.33 0.50 0.50 1 4 3 4 0.14 1.10 7.85 Vicinity to Natural Drainage C5 0.25 0.33 0.50 0.25 1 3 4 0.09 0.61 6.75 Vegetation C6 0.25 0.25 0.20 0.33 0.33 1 3 0.06 0.39 7.04 Soil C7 0.20 0.25 0.20 0.25 0.25 0.33 1 0.04 0.26 7.24 Total 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 51.92 5 Results And Discussion 5.1 Association of influencing parameters with flood locations The causal relationships between flood occurrences and each contributing parameter were investigated and assessed. Additionally, it was discovered that almost 90% (i.e. 211 out of 234) of flood locations fit into the below-5 and 5.01–10% categories following the spatial distribution analysis of flood locations with slope conditioning factor, making the slope one of the main flood impacting parameters (Fig. 3 , 4 b; table. 8). The locations along the western coastline (i.e., Santacruz) have seen 2922.30 mm of annual rainfall on average over the past five years (2016–2020), with the biggest amounts coming in the years 2020 (3721.99 mm) and 2019 (3428.22 mm), which were both catastrophic flood years following the Mumbai floods of July 2005. By combining the spatial patterns of normal annual rainfall and flood locations, it is apparent that 95 percent (i.e. 223) of flood locations are concentrated in regions where the distribution of rainfall varies between 2000 and 2200 mm (Fig. 3 , 4 b; table. 8). This indicates that drainage pits and encroachment of low lying areas by municipal authorities are to blame for flood events that have occurred in and area around Mumbai city. The majority of the drainage systems in the study area are either constrained or covered by transportation infrastructures, gummed up by solid waste, as a result, the higher values given above can be attributed to this cause, making these areas extremely vulnerable to floods. In contrast, the frequency of floods is higher due to the influence of stormwater runoff in and around urban peripheries. As a result, this parameter has a relatively strong association and influence over flood vulnerability. This shows that although while the main underlying cause of the flood is the intensity of the rainfall, it is not the only crucial parameter affecting the flood scenario in a region like Greater Mumbai. In places with settlements and the coastal alluvium type of soil, flooding occurs more frequently. The soil there is also affected by the type of land use, and when the erosion changes, the flood vulnerability are likely to rise. Around 88.46% (or 207) of all flood locations, are discovered in those areas where the land under built up category is dominated (Fig. 3 , 4 b; table. 8). Saltpan and marshes are another class where widespread flooding can be observed. This is primarily because these marshlands were created by digging and cleaning activities along the banks of the Mithi River in the study area, which typically over-flows onto these marshlands and the nearby settlements, during heavy rainfall events. Since there is typically very little percolation and infiltration due to compact concretization, it was discovered that around 88.46% of flood locations are in the built-up class, making the areas highly vulnerable to floods. The city has essentially no level of infiltration, as seen by the recurrent water logging and sometimes flood situations even from a light rain event. While 62.82% ( or 147) of the flood locations are in the far vicinity category, or less than 100 metres away, it is clear that extensive artificial drainage systems or natural streams turned into sewers have a significant role in improving the ecosystem. Furthermore, the flood locations with vicinity to natural drainage indicates that 24.79% i.e. 58 of locations lie in the 250–500 mt. (Fig. 3 , 4 b; table. 8) vicinity, with the increase in water level in these streams owing to heavy rainfall, the nearest neighboring settlement gets flooded and the rest of the locations lie in other categories. Table 8 Ward-wise Waterlogging flood locations of Greater Mumbai Sr. No. District Name of the Ward Denoted letter Area in (ha) Locations Number of Waterlogging Spots 1 Mumbai Colaba A 1121 CST, Church Gate 7 2 Sandhurst Road B 266 Sandhurst rd., Masjid 4 3 Marine Lines C 191 Marine Lines 7 4 Grant Road D 830 Bombay Central, Grant rd. Charni rd. 12 5 ByCulla E 717 Byculla 6 6 Parel F/S 965 Cotton Green 3 7 Matunga F/N 1201 Mahim, Matunga rd., 7 8 Dadar/Plaza G/N 876 Wadala. Dadar 10 9 Elphinstone G/S 929 Elphinstone rd., Curry rd., Sewri, Mahalaxmi 20 All Wards 7096/ 67.79 sq. km 76 10 Mumbai (Sub-urban) Bandra H/W 865 Bandra, Santacruz, Khar 9 11 Khar/Santacruz H/E 1289 Khar East, Kherwadi 10 12 Andheri (East) K/E 2400 Chakala 5 13 Andheri (West) K/W 2442 Jogeshwari, Andheri W., Villeparle 12 14 Goregaon P/S 2589 Goregaon 17 15 Malad P/N 4672 Malad 9 16 Kandivali R/S 1831 Kandivali 9 17 Borivali R/C 4803 Borivali 21 18 Dahisar R/N 1418 Dahisar 7 19 Kurla L 1556 Tilak Nagar, Chuna Khatti 19 20 Chembur M/E 3389 Mankhurd, Govandi 3 21 Chembur (West) M/W 1740 Chembur 1 22 Ghatkopar N 2535 Pantnagar, Vikhroli 8 23 Bhandup S NA Kanjur Marg, Bhandup 16 24 Mulund T NA Mulund 12 All Wards 38731/ 370 sq. km 158 Table 9 Area under flood vulnerability using AHP Sr. No. Flood Vulnerable Zones Area (Sq. Km) Area (%) No. of Waterlogged Locations % of Waterlogged Locations under Flood Vulnerable Zone 1 Slight 127.59 29.15 5 2.14 2 Moderate 23.59 5.39 7 2.99 3 High 29.90 6.83 22 9.40 4 Very High 52.13 11.91 70 29.91 5 Severe 204.50 46.72 130 55.55 Total 437.71 100 234 100 5.2 Vulnerability Analysis Based on AHP technique weights are computed in percent as 29.42, 20.96, 17.52, 13.99, 8.97, 5.58, and 3.56 for rainfall, slope, LULC, vicinity to sewers and storm water drainage, vicinity to natural drainage, vegetation, and soil type, of the study area, and consistency ratio (CR) is computed as 0.053. This demonstrated a respectable level of accuracy in the pair-wise comparison matrix. Using the arithmetic weighted sum overlay technique in the Arc GIS 10 software, each parameter's raster layer in grid format is multiplied by its assigned weight prior to getting added together. Smaller values denote low vulnerability to floods, and higher values denote high vulnerability to floods. The resulting composite values are generated in the range of 144 to 384. The merged map is reclassified into five categories—severe, very high, high, medium, and slight (Fig. 4 a) - using quantitative method of Standard Deviation (SD) (table. 9). In the study area, the area susceptible to flooding is computed as follows: 46.72% (severe), 11.91% (very high), 6.83% (high), 5.39% (moderate), and 29.15% (Slight) (table. 9). Most of the water-logged spots i.e. 85.46% were found in areas that fall under the category of severe to very high vulnerability and only 14% of locations fall in other three categories as per the data of the flooding sites that are collected from MCGM authorities. According to the findings of the study, that is based on combined weighted of all parameters, the north and north-eastern part of the study area lie in slight to moderate vulnerable zone whereas south and south-western part of the study area come under the category of severe to very high vulnerability. 6 Conclusion In this study, urban flood vulnerable zones of Greater Mumbai, a highly flood-prone metropolis, were delineated using the AHP technique. The slope categories of less than 5 metres and 5.01–10 metres, the built-up land category in LULC, the vicinity of sewers and storm water drainage of less than 100 metres, the category of natural drainage of 250–500 metres, rainfall of 2000–2200 mm category, the category of lowest dense vegetation, and the category of coastal alluvium in soils all exhibit higher interactions with flood locations. The present study draws the conclusion from the causal relationship of all the parameters that built-up land use and vicinity to artificial sewerage systems are the main influencing parameters for flood vulnerability in the study area, with a strong association with the natural parameters like rainfall, slope, and soil. We may comprehend the underlying triggering and impacting parameters that are typically neglected in the vagueness of blind infra-structure investment by comprehending the flood vulnerability assessments that are conducted in mainstream and academic study of metropolises like Mumbai, Hyderabad, Bangalore, Delhi etc. Megacities are frequently viewed as the sole loci of economic and financial development in developing countries, where environmental changes are made while obnoxiously ignoring the risk of natural disasters. The outcomes of this study, which frequently suffers from administrative problems, indicate that urban expansion, whether planned or unplanned, can significantly alter the hydrological processes of a watershed. In the scenario of Mumbai, this damage has gone beyond the reasonable limits, culminating in rivers and streams that have been transformed into city sewers blocked with solid waste. Even a brief period of rainfall causes floods in this metropolis since the land surface has achieved zero levels of infiltration. However, despite the fact that the urban land use change is the most essential parameter in the study of flood vulnerability, it simply cannot be chosen in isolation. As a result, another significant conclusion that can be drawn is that in order to understand the full geographic and climatic narrative of urban floods, it is necessary to include all the other parameters and correlate their relationship with urban floods. The MCGM and the relevant authorities should therefore repair the current abnormalities in the system, such as the storm water drainage system, with the aid of such scientific investigations carried out at multi-scalar levels, in order to minimize the risk of future damage. The following suggestions are made by the authors of the work are: Identifying flood-prone locations on large-scale maps and identifying areas at risk of flooding at different frequency rates. Make laws governing construction activities in flood plains and enforce them. Provide specific areas for the flood plain, such as by forestation, lands sloping, and construction of small reservoirs, check dams, and ponds, in order to increase the water holding capacity of a watershed. The potential of the high runoff should be matched while improving the sewers and storm water drainage system and two parameters taking into account while planning the city structures like to know the trend in population growth and the changing climate. Demarcate vulnerable Zones to halt the ruthless encroachment onto marshlands and other fragile water bodies. To plan for restoration in the form of shelter dwellings both before and after disasters. Declarations Acknowledgement: Author Contributions : All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Rohit Mann and Dr. Anju Gupta. The first draft of the manuscript was written by Rohit Mann and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Declarations Competing interests : The authors declare no competing interests. Ethics Approval : Not Applicable. Consent to participate : The authors express their consent to participate for research and review. Consent for publication : The authors express their consent for publication of research work. Data Availability : The data will be made available on demand. Code Availability : Not Applicable. References Ahmadi O, Mortazavi SB, Mahabadi HA, Hosseinpouri M (2020) Development of a dynamic quantitative risk assessment methodology using fuzzy DEMATEL-BN and leading indicators. Process Saf Environ Prot 142:15–44. https://doi.org/10.1016/j.psep.2020.04.038 Abrishamchi A, Ebrahimian A, Tajrishi M, Marino MA (2005) Case Study: Application of Multicriteria Decision Making to Urban Water Supply. 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Doi: 10.1007/s00477-012-0598-5 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2171279","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":144891500,"identity":"f78bb424-c401-4943-9bda-90265e208ef6","order_by":0,"name":"Rohit Mann","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYPCCAzwM7M0HHwBZPHxEa+HhOZZsANLCRqwWBh6JHDUJEJOgFt32s8ceV1TckbGXyGGr/JpjJ8PGwPzw0Q08WszO5KUbnjnzDOiwt8duy25LBjqMzdg4B5+WAzlmko1th3l42PPSbktuYwZq4WGTxqvl/BuoFoYcs2LJbfVEaLkBs4Ujx4zx47bDxGgB2tJwBqjlzLFkacZtx3nYmAn55TzQloaKw/bs7c0HP/7cVm3Pz9788DE+LSiAmQdMEqscBBh/kKJ6FIyCUTAKRgwAAN1yRgYFR6IXAAAAAElFTkSuQmCC","orcid":"","institution":"Kurukshetra University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Rohit","middleName":"","lastName":"Mann","suffix":""},{"id":144891503,"identity":"92f90455-5bbb-45c5-9cc7-7b3f25fe383a","order_by":1,"name":"Anju Gupta","email":"","orcid":"","institution":"Kurukshetra University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Anju","middleName":"","lastName":"Gupta","suffix":""}],"badges":[],"createdAt":"2022-10-16 11:14:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2171279/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2171279/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":27996766,"identity":"d44191bb-2530-4e78-8b64-0ba0728e952e","added_by":"auto","created_at":"2022-10-19 14:50:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":581079,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study area\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-2171279/v1/60964dc77fcda459d3084a67.png"},{"id":27996248,"identity":"2bfd1363-d135-440a-a2c3-142d027db8fd","added_by":"auto","created_at":"2022-10-19 14:45:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":69542,"visible":true,"origin":"","legend":"\u003cp\u003eDiagram illustrating the adopted methodology for creating the map of flood vulnerability of the study area\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-2171279/v1/bcfa62e943af73ff972fca70.png"},{"id":27996767,"identity":"53ee7689-2801-4e0c-b46e-dccd8ff1b6e9","added_by":"auto","created_at":"2022-10-19 14:50:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":878007,"visible":true,"origin":"","legend":"\u003cp\u003eMaps of flood influencing Parameters\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-2171279/v1/a8a886c7394f35b7aa7f00ae.png"},{"id":27996249,"identity":"796d20ce-ba7f-4d69-b1d3-0ae5880acbca","added_by":"auto","created_at":"2022-10-19 14:45:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":652320,"visible":true,"origin":"","legend":"\u003cp\u003eMap of (a) Flood Vulnerable Zones (using AHP) and, (b) Ward-wise Waterlogging spots\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-2171279/v1/cc08b2f484ac090b734275b9.png"},{"id":34036817,"identity":"63856661-194a-4feb-b81e-24030ec31dfe","added_by":"auto","created_at":"2023-03-09 21:14:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2666258,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2171279/v1/26e0ba38-c333-4ec3-a881-16f363372c6d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessment of Vulnerability to Urban Floods in Greater Mumbai, India Using Geospatial Techniques","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe newly induced phenomena of global warming altered the rainfall patterns all over the world which in turn affects the city life that often results in stagnation of water in many coastal cities (Rakhecha and Pisharoty 1996; Fowler and Kilsby \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Goswami et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Guhathakurta and Rajeevan \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Rajeevan et al. 2008), leading to socio-economic impacts and consequences like traffic congestion, halt of city life and many more. The primary causes of flood in a region are population growth, fast urbanisation, changes in river channel morphology brought on by anthropogenic and natural interventions, as well as heavy rainfall (Starr et al. 1978; Korhonen and Lewandowski \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e1989\u003c/span\u003e; Schmold et al. 2001; Young 2006; Young et al. 2009, 2011; Ahmadisharaf et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). As a result, the connection between urbanisation and local climate change has caught the interest of many academicians worldwide (Kalnay and Cai \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; He et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Rapid urbanisation has an impact on land surface qualities, which can change the diurnal, seasonal and long-term meteorological and climatic conditions at local, regional, and global scale (Lowry 1977; Ohashi and Kida 2002; Chen et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Rosenzweig et al. 2008; Mutizwa-Mangiza et al. 2011). Mumbai is extremely vulnerable to frequent floods during monsoon season because of its unique geographical control, strong building activity, and significant monsoon rainfall. It is encountering three major types of floods: localized flooding brought on by poor drainage; flooding brought on by Mithi River overflows where settlements have been established in flood plains; and flooding brought on by a combination of high tide and river overland flow. Localized flooding is mostly caused by unplanned settlements in drain paths, an inadequate drainage system, and reduced drain capacity from waterlogging. The flood threat has been impacted and made worse by the city's land use strategies, solid waste management procedures, and drainage upkeep (Zope et al. 2017). As a result, the city is quite vulnerable to flooding, which can have negative consequences on settlements, the road system, and everyday life for residents (Wadge et al. 1993; Blazkova and Beven \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). The creation of a map of the flood vulnerable zones is essential for future planning and construction projects as well as the flood hazard management to lessen the effects of floods (Farajzadeh \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Tehrany et al. 2014).\u003c/p\u003e \u003cp\u003eGeographic information system (GIS) and remote sensing technology have recently added fresh perspective to flood investigations (Masmoudi and Habaieb 1993; Islam and Sado \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2001\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Due to the multi-temporal dataset involved in flood susceptibility mapping, the GIS tool makes it easier to create, manage, and integrate a database of flood episodes as well as various contributing elements (Moore 1986; Merwade et al. 2008). Weights-of-evidence (Porwal et al. 2010; Oh and Lee 2010; Armas \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Lee et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2012a\u003c/span\u003e; Fu et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Pourghasemi et al. 2013b: Rahmati et al. 2016), analytic hierarchy process (AHP) (here after uses AHP) (Schmold et al. 2001; Young 2006; Young et al. 2009: Anagnostopoulos and Vavatsikos \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Pourghasemi et al. 2012, 2013a; Park et al. 2013; Althuwaynee et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), Frequency ratio (FR) (Poudyal et al. 2010; Lee et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012b\u003c/span\u003e; Ozdemir and Altural 2013; Park et al. 2013; Jaafari et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Regmi et al. 2014; Naghibi et al. 2015: Rahmati et al. 2016), fuzzy logic (Ercanoglu and Gokceoglu \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Pourghasemi et al. 2012; Sharma et al. 2013; Zou et al. 2013; Ramazi and Amini 2014) logistic regression (Porwal et al. 2010; Ozdemir and Altural 2013; Park et al. 2013; Pourghasemi et al. 2013a), artificial neural network (Kia et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Zare et al. 2013), decision tree (Yeon et al. 2010; Shafapour et al. 2013), evidential belief function (EBF) (Tehrany and Kumar 2018), adaptive neuro fuzzy inference system (Termeh et al. 2018) and support vector machine (Choubin et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) are the most widely used techniques that make use of geospatial data useful among the various GIS-based flood vulnerable models used in the literature. As reported in the literature, all of these models were widely utilized in the analytical vulnerability assessment of any common hazard.\u003c/p\u003e \u003cp\u003eNumerous studies on floods in Mumbai and Navi Mumbai have been conducted in the past, with a major focus on rainfall modelling (Shahapure et al. 2011), urban flood resilience management and planning (Gupta \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), vulnerability mapping for disaster estimation (Reshma and Deepankar 2015), the impacts of urbanisation on flooding (Zope et al. 2015), integrated flood assessment modelling tool for the coastal urban flood simulation (Kulkarni et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2014a\u003c/span\u003e), integrated flood inundation model (Kulkarni et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014b\u003c/span\u003e) and hydrological repercussions of land use land cover (LULC) change on hazard (Zope et al. 2017) related to urban floods. These investigations were conducted independently, without establishing any connection to the underlying mechanisms or variables that are frequently claimed to have an impact on urban flooding.\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Multi-Criteria Evaluation (MCE) Approach based on GIS\u003c/h2\u003e \u003cp\u003eThe geographical analysis and 2-D and 3-D display of geographic data to enhance environmental decision-making is one of the most significant uses of GIS. A decision is a choice between two or more alternatives, which could be two different courses of action, places to go, things to buy, etc. GIS can provide greater information about circumstances requiring decision-making because 80% of the data used by decision-makers is location-based (Starr et al. 1978). With the help of the overlay process in GIS, the decision-maker can locate a list that satisfies a pre-determined set of requirements (Abrishamchi et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Multi-criteria evaluation (MCE) (here after uses MCE) techniques based on GIS have been used in a plethora of studies (Fernandez and Lutz \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Afshar et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Chung et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Ahammed et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Ahmadisharaf et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAll criteria are permitted to be Boolean (i.e., logical true/false) statements of fitness for the decision being considered. Techniques for resource appraisal and site selection that rely on conventional Boolean logic, meanwhile, have already been identified to have issues (Jansssen and Rietveld \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Loss of data may occur in circumstances when the minimum value is not exact. Additionally, the method provides no analytical opportunity to ascertain that which of the locations meeting the criteria is most suitable for the intended purpose. Boolean logic has been replaced with MCE techniques due to issues with Boolean overlay (Pereira and Itami 1991). In that both an index model and a binary model require MCE for appropriateness and vulnerability analysis, they are comparable to one another. Instead of a simple yes or no, an index model generates an index score for each unit area. The process for calculating the index score is the main factor to take into account while creating an index model, and further to identify whether it is vector or raster based. The weighted linear combination approach, which generates a ranking map based on the index scores, is undoubtedly the most frequently used method for calculating the index score for each unit area (Althuwaynee et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe following formula is used to determine the index score: n is the criterion, W are the weight, and X seems to be the standard score, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({I}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the index score.\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${I}_{i}= \\sum _{i = 1}^{n}{W}_{i}{X}_{i} \\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn essence, the computation of index score involves three steps:\u003c/p\u003e \u003cp\u003eStep 1: Compare each criterion's relative weight to that of the others.\u003c/p\u003e \u003cp\u003eStep 2: Each criterion's values should be standardized.\u003c/p\u003e \u003cp\u003eStep 3: Multiplying the results of the weighting and standard value into an index score.\u003c/p\u003e \u003cp\u003eOne of the most widely used techniques for determining criteria weights in MCE is the AHP (Park et al. 2013), which uses an authoritative pair-wise comparison matrix with their weights. Relying on the AHP, Siddiqui et al. (1996) offered an additive solution to a spatial problem. According to Rao et al. (1991) the process of pair-wise comparisons in AHP is a reasonable one for the establishment of criteria weights. The weighted linear conjunction operator, which is frequently employed with these factors, has been demonstrated to lie on a continuous with these operators, where it reflects the scenario of full trade-off between the parameters under consideration as well as intermediate end or Boolean logic. Nevertheless, despite certain unknowns, numerous researchers have gathered the AHP success narratives in a variety of sectors. These studies recognized the AHP model combined with weighted linear combination in GIS as having a strong theoretical foundation and offering logic for the normalization of parameters, a justification for the expression of decision risk, and a high level of versatility in the site adaptability and vulnerability assessment (Hughes \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Carver \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Malczewski 2000). The most crucial elements influencing the quality of spatial data, as per Burrough and McDonnell (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), are wholeness, coherence, transparency, precision, clarity, and process method.\u003c/p\u003e \u003cp\u003eThe primary aim of the current study is to create a crucial analytical spatial database of Mumbai that will aid in the identification of various contributing factors and the relative importance of each in terms of impacting flood situations, as well as the identification of flood vulnerable zones using RS and GIS. In order to designate flood-prone areas that should be avoided in future development planning, demarcation of flood vulnerable areas is necessary. With the aforementioned theoretical framework in mind, the current study used MCE in GIS with incorporation of AHP criterion and weighted linear combination method to identify flood-prone sites/areas using some triggering factors like rainfall, slope, LULC, vicinity to sewers and storm water drainage, vicinity to natural drainage, vegetation, and soil.\u003c/p\u003e \u003c/div\u003e"},{"header":"2 Study Area","content":"\u003cp\u003eGreater Mumbai, which has a surface area of around 470 sq.km, stretches between latitude 18\u003csup\u003e◦\u003c/sup\u003e53ꞌ47ꞌꞌ \u0026minus;\u0026thinsp;19\u003csup\u003e◦\u003c/sup\u003e16ꞌ16ꞌꞌ to longitude 72\u003csup\u003e◦\u003c/sup\u003e48ꞌ30ꞌꞌ \u0026minus;\u0026thinsp;72\u003csup\u003e◦\u003c/sup\u003e51ꞌ11ꞌꞌ. The westernmost coastal region of the Indian state of Maharashtra is generally referred to as Mumbai. With Thane district to the east, Palghar to the west, and Raigad to the south-west, the city is considered to be an extreme coastal area and is surrounded by the western branch of the Indian Ocean and the Arabian Sea on three sides. As a result, the city sometimes considered as mini peninsula. The city also hosts a significant span of mangroves on its eastern and western coastlines, which seems to be 149 km in length.\u003c/p\u003e \u003cp\u003eAs an archipelago of islands, Mumbai's relief and terrain consisted of local hills, coastal cliffs, and ridges with marsh land in between; it possessed a total of 22 hills well before the fast development period, that has left Mumbai only with three hill ranges: the Ghatkopar hills in the northern part, the Trombay hills in south-east, as well as the highest Powai hills in north around Borivali or Sanjay Gandhi National Park. All of these hilly locations mostly encroach on the foothills, and degradation keeps seeping inside.\u003c/p\u003e \u003cp\u003eThe Mithi River, which rises near Vihar Lake in the north and rushes southward to reach the Arabian Sea, making an estuary along the Mahim Creek, is one of two major rivers that drain the study area. In contrast, the Oshiwara River begins from Powai hills and flows northwest to the Manori Creek. In addition to this, the highlands have a lot of smaller streams. These rivers have been transformed into sewers filled with tonnes of solid waste due to the hasty and quick urban expansion. Usually, the climate of the study area is tropical, with two distinct seasons wet and dry as well as places with high to extremely high rainfall. The seasons are marked by moderate heat and high levels of humidity. Due to its tropical climate, the average annual temperature of Mumbai is 27.2\u003csup\u003e◦\u003c/sup\u003e C. As per IMD, Mumbai, the study area experiences 242.2 cm of rainfall annually. The metropolis of Mumbai has the seventh-highest population in the world. To facilitate administrative convenience, the entire region in Mumbai and Mumbai (Suburban) has been divided into wards. These wards are labeled A, B, C, etc. in alphabetical order. Mumbai district includes the wards from A to G/South and Mumbai (Suburban) district includes the wards from H/West to T.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"3 Database And Methodology","content":"\u003cp\u003eThe current study is supported by the secondary data (table. 1). Secondary data is gathered through the internet, published reports, and the relevant public departments. Fig. 2 graphically depicts the steps taken to determine which locations are most susceptible to flooding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Descriptive details of the data collected\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"685\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.8232%;\" valign=\"top\" width=\"37.865497076023395%\"\u003e\n \u003cp\u003e\u003cstrong\u003eData Source\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.4608%;\" valign=\"top\" width=\"30.84795321637427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpecifications\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 23.4608%;\" valign=\"top\" width=\"30.84795321637427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutput (Flood Influencing Factor)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.8232%;\" valign=\"top\" width=\"37.81021897810219%\"\u003e\n \u003cp\u003e\u003cstrong\u003eIMD, Pune\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 23.6842%;\" width=\"31.094890510948904%\"\u003e\n \u003cp\u003e\u003cstrong\u003eHourly Data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.9076%;\" width=\"31.094890510948904%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRainfall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.8232%;\" valign=\"top\" width=\"37.81021897810219%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSRTM (Shuttle Radar Topography Mission) DEM (Digital Elevation Model)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 23.6842%;\" width=\"31.094890510948904%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpatial Resolution: 30 m\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.9076%;\" width=\"31.094890510948904%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSlope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.8232%;\" valign=\"top\" width=\"37.81021897810219%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSatellite Image\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eLandsat 5 and 8\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 23.6842%;\" width=\"31.094890510948904%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTM \u0026amp; OLI/TIRS Landsat TM and OLI, Spatial Resolution: 30mt.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.9076%;\" width=\"31.094890510948904%\"\u003e\n \u003cp\u003e\u003cstrong\u003eLand Use Land Cover (LULC)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.8232%;\" valign=\"top\" width=\"37.81021897810219%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBRIMSTOWAD-II (Brihanmumbai Storm Water Disposal System) Draft Master Plan Maps of Storm water\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003edrainage \u0026amp; Sewers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 23.6842%;\" width=\"31.094890510948904%\"\u003e\n \u003cp\u003e\u003cstrong\u003eScale - 1: 50000,\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eYear - 2014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.9076%;\" width=\"31.094890510948904%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVicinity to sewers and storm water drainage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.8232%;\" valign=\"top\" width=\"37.81021897810219%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurvey of India OSM Sheet, SRTM DEM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 23.6842%;\" width=\"31.094890510948904%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber - E43A/16(47A/16)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eScale - 1:50,000,\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSpatial Resolution: 30 m\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.9076%;\" width=\"31.094890510948904%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVicinity to Natural Drainage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.8232%;\" valign=\"top\" width=\"37.81021897810219%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSatellite Image\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eLandsat 5 and 8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 23.6842%;\" width=\"31.094890510948904%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNDVI; Spatial Resolution: 30mt.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.9076%;\" width=\"31.094890510948904%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVegetation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.8232%;\" valign=\"top\" width=\"37.81021897810219%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNational Bureau of Soil Survey\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 23.6842%;\" width=\"31.094890510948904%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaharashtra\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eScale \u0026ndash; 1:50000, Year - 1996\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.9076%;\" width=\"31.094890510948904%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.8232%;\" valign=\"top\" width=\"37.81021897810219%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMunicipal Corporation of Greater Mumbai (MCGM)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 23.6842%;\" width=\"31.094890510948904%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWard-wise flood locations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.9076%;\" width=\"31.094890510948904%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMap of waterlogging spots\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.1\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eProducing Maps of Flood Influencing Factors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor each component, GIS maps are initially constructed using a standard geo-referencing approach. Rainfall and soil maps were prepared from the data obtained from IMD, Pune and National Bureau of Soil Survey, Maharashtra. By using surface-slope tool in ArcGIS, slope in percentage for the study area was retrieved from the computed SRTM DEM and SOI OSM sheet, which was later modified, using high resolution satellite images, and again used to digitize the map of natural drainage. The Euclidean distance measure in ARCGIS was used to calculate the vicinity for a distance equivalent to 1000 metres for natural drainage. The vegetation map is produced using TM Landsat 8 (30 mt.) image for the year 2020 and Normalized Difference Vegetation Index (NDVI) is performed using Erdas Imagine 2010 software as NDVI= (NIR-VIS)/(NIR+VIS). Using Landsat 8 (OLI/TIRS 30 mt.) multi-spectral data, the map of LULC for the study area was retrieved in ARCGIS software by employing supervised classification. Finally, the maps of sewers and storm water drainage from the BRIMSTOWAD-II Draft Plan were used to retrieve the map of artificial drainage. It is a component of the storm water drains project being undertaken in 2014 by the office of storm water drains under department of disaster management, MCGM. Using the Euclidean distance measure, the vicinity for a distance equivalent to 500 mt. was calculated. All maps are transformed to grid-based integer raster format with the same pixel size of 30 mt. for each parameter. At last, all the maps were overlaid to produce a combined map of flood vulnerable zones. Also, a ward-wise map of 234 flood locations is prepared from the data gathered from MCGM and then manually assesses the relative importance of each parameter against the flood locations. MCA is used to create and combine spatial data for characterizing the causative aspects in order to determine the vulnerability of flooding. In GIS context, the Weighted Linear Combination (WLC) Approach was employed to implement the AHP Pairwise Comparison Method.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Description and Order of Influencing Parameters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach aspect that is taken into account is ranked according to the preference of decision makers. Each component is rated according to the expected significance influence on floods in order to establish criterion scores for each sub-class category. These factors received an inverted ranking. Each sub-class is ranked 1\u0026ndash;5 in decreasing order of impact based on reviewed literature and knowledge, where 5 represents high vulnerability to floods and 1 represents low vulnerability. Table 2 displays the grading scheme. The current study makes the assumption that the areas that are most susceptible to flooding depend on a variety of variables, including slope, amount of rainfall, vegetation, soil types, LULC practices, natural and artificial drainage network. As a result, depending on these variables, flood can vary considerably over time and location. The following seven parameters were used in the current study, and each of them is shown and saved in a distinct map with order of their sub-category is shown in fig. 3 and table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e Ranking of Flood Parameters and their Sub Categories\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"534\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003eVulnerability Parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003eSub-category of Parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003eRanking\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e1. Rainfall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026gt;2300.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e\u0026nbsp;2200.01-2300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e2100.01-2200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e2000.01-2100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026lt;2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e2. Slope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e\u0026nbsp;Very Gentle [below 5]\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e\u0026nbsp;Gentle [5.01-10.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e\u0026nbsp;Moderate [10.01-15.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e\u0026nbsp;Steep [15.01-20.00]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e\u0026nbsp;Very Steep [20.01 \u0026amp; above]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e3. LULC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003eBuilt-up Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003eOpen Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003eCultivated Land\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003eWater body\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003eVegetation Cover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e4. Vicinity to Sewers \u0026amp; Storm Water Drainage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e\u0026nbsp;0-125 mt.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e\u0026nbsp;125-250 mt.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e\u0026nbsp;250-375 mt.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e\u0026nbsp;375-500 mt.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026gt;500mt.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e5. Vicinity to Natural Drainage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e\u0026nbsp;0-250 mt.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e\u0026nbsp;250-500 mt.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e\u0026nbsp;500-750 mt.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e\u0026nbsp;750-1000 mt.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e\u0026nbsp;\u0026gt;1000 mt.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e6. Vegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e\u0026nbsp;Lowest dense vegetation cover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e\u0026nbsp;Lower dense Vegetation Cover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e\u0026nbsp;Dense Vegetation Cover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e\u0026nbsp;Higher Dense Vegetation Cover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e\u0026nbsp;Highest dense Vegetation Cover\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e7. Soil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e\u0026nbsp;Settlement Coastal Alluvium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e\u0026nbsp;Mud Marsh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e\u0026nbsp;Vertic Halaquepts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e\u0026nbsp;Vertic Ustrepepts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"36.70411985018727%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"42.69662921348315%\"\u003e\n \u003cp\u003e\u0026nbsp;Typic Ustorthents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"20.59925093632959%\"\u003e\n \u003cp\u003e\u0026nbsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e3.2.1 Rainfall\u003c/p\u003e\n\u003cp\u003eRainfall is the primary hydrological component that is most frequently employed in studies of floods. Rainfall is the term used to describe the dispersion of liquid droplets over space and time, which regulates the surface runoff (Goswami et al. 2006). Since areas with higher rainfall than the annual average are more likely to experience flooding, high rainfall amounts are a marker of substantial flood susceptibility. As a result, a category weight of 5 is allocated to heavy rainfall zone and 1 is allocated to relatively low rainfall zone (table. 2).\u003c/p\u003e\n\u003cp\u003e3.2.2 Slope\u003c/p\u003e\n\u003cp\u003eThe slope is the most important aspect in hydrology since it directly affects the surface runoff and floods. Since sites of low-elevation often have a gentle or level slope, they are more susceptible to flooding and water logging because steep slopes generates huge velocity of runoff than flat or gentle slopes and dispose of storm runoff more quickly (Altaf et al. 2013). Runoff from a level or gently sloping land is accumulated and released gradually over time (Tehrany and Kumar 2018). In contrast to high gradient slopes, low gradient slopes even more susceptible to flooding. Historically, the study area was an archipelago of seven islands that has been reclaimed and established as a land of concrete slabs over a span of five centuries. Additionally, the steeper slope was exploited to provide flat homes for a large number of migrants. As a result, places with very gentle slopes were assigned a class rating of 5, whereas locations with high relief were given lower ranking i.e., 1.\u003c/p\u003e\n\u003cp\u003e3.3.3 LULC\u003c/p\u003e\n\u003cp\u003eRecognizing the activities taking on in a location and the various categories of LULC being impacted by recurrent floods is crucial for vulnerability mapping. Due to their significant use of impermeable surfaces, urban areas are impacted by storm water runoff (Fernandez and Lutz 2010). Built-up area dominates the LULC category in the study area. Slums or nucleated communities are the main components of dense built-up areas, which are primarily found in the city\u0026apos;s central and Southern parts. Evidently, areas with dense built-up space are at a larger risk of flooding than areas with less built-up land cover. Therefore, rankings are allocated as shown in table 2 based on the kind of land use and its susceptibility to floods.\u003c/p\u003e\n\u003cp\u003e3.3.4 Vicinity to sewers and storm water drainage\u003c/p\u003e\n\u003cp\u003eWith growing pollution and a lack of concern for it, the sewerage system has emerged as a crucial component and responsibility of the city administration. Sewers are man-made drains that are used to move sewage from homes to disposal sites. The Storm Water Drains (SWD) is specialized man-made drains that assist in moving and draining extra storm runoff from the city to the countryside and thus minimizes floods. The water-logging in most parts of a megacity like Mumbai is caused by the overflow from these drains, which are primarily ineffective at holding or draining water, therefore it is crucial to take this into account while analysing the vulnerability of flooding.\u003c/p\u003e\n\u003cp\u003e3.3.5 Vicinity to natural drainage\u003c/p\u003e\n\u003cp\u003eThe intensity of flooding was thought to be impacted by vicinity to the natural drainage, implying that the area around some stream or river is quite vulnerable to floods. Here, all streams and rivers are considered to be a part of natural drainage.\u003c/p\u003e\n\u003cp\u003e3.3.6 Vegetation Cover\u003c/p\u003e\n\u003cp\u003eVegetation cover is the parameter that has the significant impact on flood vulnerability. Flood susceptibility may grow as vegetation cover declines. This connection is the basis for the widespread inclusion of tree cover and land use in studies of flood susceptibility (Young et al. 2009). Insufficient tree cover under barren or open land makes the area highly vulnerable to flooding; a maximum class weight of 5 is allocated to it, with subsequent classes receiving less weight in the sequence of increasing the area under tree cover.\u003c/p\u003e\n\u003cp\u003e3.3.7 Soil Type\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Since soil properties, particularly those that are more likely to erode, are more vulnerable to floods because the rate of permeability are depends on soil characteristic of a region. Therefore, soil type was chosen as an influencing parameter (Chung et al. 2011). The study area has two main soil types\u0026rsquo; viz vertic halaquepts and coastal alluvium (Soil group-Inceptisols). These soils are very fine, slightly deep, poorly drained and moderately salinized. They are located on very gently sloping areas in residual hills. These soils generally equate to black and laterite in the context of Indian soil classification (Pal 2013).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1 Application of AHP to Evaluate the Parameters\u0026nbsp;Weight\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Determining the weights for flood influential parameters is a complex problem that involves different criterion functions. If such a scenario is not handled with a reasonable and well-processed methodology, often results in miscalculation of the facts. The MCE technique has the potential to logically resolve this matter regarding multiple criteria. The AHP technique which Saaty devised was applied in the current investigation (Saaty 1977). With the use of a preference matrix, in which all recognized relevant criteria are contrasted against one another with replicable preference parameters, AHP is a very well-known and widely used statistical method to determine the necessary weights of each parameter. A pair-wise evaluation matrix, a measure to represent the proportional priority among the components, examines all parameters that are thought to be essential for a decision against one another. As a result, each parameter needs to be given a quantitative score conveying a decision of the importance of one variable compared to another. A scale for comparison with scores ranging from 1 to 9 which represent the intensity of importance was proposed by Saaty and Vargas (1991). Since it has been recognized through psychological studies, a person cannot compare more than 7 \u0026plusmn; 2 variables at once. One represents \u0026quot;equal importance,\u0026quot; whereas nine represents variable that are \u0026quot;extremely important\u0026quot; in comparison to other criteria (table. 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Construction of a Pair-wise Comparison Matrix\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;A pair-wise comparison matrix of order 7 is shown in table 4 and compares seven parameters (C1, C2, C3, C4, C5, C6, and C7). When parameter C1 and parameter C2 are directly compared, parameter C1 is seen as equal to moderate relevance and the remaining parameter is allocated the same relative weight. The reciprocal of 1/4, or 0.25, is immediately applied to the transposed position.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3 Standardized Pair-Wise Comparison Matrix\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"76%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003eIntensity of Importance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"66.66666666666667%\"\u003e\n \u003cp\u003eExplanation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"66.66666666666667%\"\u003e\n \u003cp\u003eEqual Importance\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"66.66666666666667%\"\u003e\n \u003cp\u003eEqual to moderate Importance\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"66.66666666666667%\"\u003e\n \u003cp\u003eModerate Importance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"66.66666666666667%\"\u003e\n \u003cp\u003eModerate to Strong Importance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"66.66666666666667%\"\u003e\n \u003cp\u003eStrong Importance\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"66.66666666666667%\"\u003e\n \u003cp\u003eStrong to very Strong Importance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"66.66666666666667%\"\u003e\n \u003cp\u003eVery Strong Importance\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"66.66666666666667%\"\u003e\n \u003cp\u003eVery to Extremely strong Importance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"66.66666666666667%\"\u003e\n \u003cp\u003eExtreme Importance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003eCorresponding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"66.66666666666667%\"\u003e\n \u003cp\u003eValues for Inverse Comparison\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe stated preference scores are combined in the following phase to arrive at a quantitative score that represents the weights of the parameters. As a result, Eigen scores and vectors of square preference matrix, that disclose key information about patterns in the data matrix, are computed. Seven eigen scores are provided by the square matrix of order seven mentioned below, which can be used to calculate seven eigen vectors, each of which has seven vector components. Since this Eigen vector provides enough information to show by its eigen vector components - the relative emphases of the parameters being investigated, it is viewed as adequate to compute only its eigen vector deriving from the biggest eigen score (Saaty and Vargas 1991). The pair-wise matrix is standardized, and the parameter weights are represented by the standardized matrix\u0026apos;s eigen scores, which are produced as shown in table\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e A sampled comparison scale\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"76%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003eIntensity of Importance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"66.66666666666667%\"\u003e\n \u003cp\u003eExplanation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"66.66666666666667%\"\u003e\n \u003cp\u003eEqual Importance\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"66.66666666666667%\"\u003e\n \u003cp\u003eEqual to moderate Importance\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"66.66666666666667%\"\u003e\n \u003cp\u003eModerate Importance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"66.66666666666667%\"\u003e\n \u003cp\u003eModerate to Strong Importance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"66.66666666666667%\"\u003e\n \u003cp\u003eStrong Importance\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"66.66666666666667%\"\u003e\n \u003cp\u003eStrong to very Strong Importance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"66.66666666666667%\"\u003e\n \u003cp\u003eVery Strong Importance\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"66.66666666666667%\"\u003e\n \u003cp\u003eVery to Extremely strong Importance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"66.66666666666667%\"\u003e\n \u003cp\u003eExtreme Importance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"33.333333333333336%\"\u003e\n \u003cp\u003eCorresponding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"66.66666666666667%\"\u003e\n \u003cp\u003eValues for Inverse Comparison\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Construction of a Pair-Wise Comparison Matrix\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e Standardized Pair-Wise Matrix\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"103%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.76595744680851%\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.25531914893617%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003eRainfall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.382978723404255%\"\u003e\n \u003cp\u003eSlope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.446808510638298%\"\u003e\n \u003cp\u003eLULC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.574468085106384%\"\u003e\n \u003cp\u003eVicinity to Sewers \u0026amp; Storm Water Drainage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.638297872340425%\"\u003e\n \u003cp\u003eVicinity to Natural Drainage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.702127659574469%\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.319148936170213%\"\u003e\n \u003cp\u003eSoil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.382978723404255%\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003ePriority Vector\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003eWeight (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.76595744680851%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.25531914893617%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003eC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.382978723404255%\"\u003e\n \u003cp\u003eC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.446808510638298%\"\u003e\n \u003cp\u003eC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.574468085106384%\"\u003e\n \u003cp\u003eC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.638297872340425%\"\u003e\n \u003cp\u003eC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.702127659574469%\"\u003e\n \u003cp\u003eC6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.319148936170213%\"\u003e\n \u003cp\u003eC7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.382978723404255%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.51063829787234%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.51063829787234%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.76595744680851%\"\u003e\n \u003cp\u003eRainfall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.25531914893617%\"\u003e\n \u003cp\u003eC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.446808510638298%\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.574468085106384%\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.702127659574469%\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.319148936170213%\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.382978723404255%\"\u003e\n \u003cp\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003e\u003cstrong\u003e29.42\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.76595744680851%\"\u003e\n \u003cp\u003eSlope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.25531914893617%\"\u003e\n \u003cp\u003eC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.446808510638298%\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.574468085106384%\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.702127659574469%\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.319148936170213%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.382978723404255%\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003e\u003cstrong\u003e20.96\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.76595744680851%\"\u003e\n \u003cp\u003eLULC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.25531914893617%\"\u003e\n \u003cp\u003eC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.446808510638298%\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.574468085106384%\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.702127659574469%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.319148936170213%\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.382978723404255%\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003e\u003cstrong\u003e17.52\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.76595744680851%\"\u003e\n \u003cp\u003eVicinity to Sewers \u0026amp; Storm Water Drainage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.25531914893617%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.382978723404255%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.446808510638298%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.574468085106384%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.702127659574469%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.382978723404255%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e13.99\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.76595744680851%\"\u003e\n \u003cp\u003eVicinity to Natural Drainage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.25531914893617%\"\u003e\n \u003cp\u003eC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.446808510638298%\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.319148936170213%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.97\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.76595744680851%\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.25531914893617%\"\u003e\n \u003cp\u003eC6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.446808510638298%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.574468085106384%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.702127659574469%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.319148936170213%\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.58\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.76595744680851%\"\u003e\n \u003cp\u003eSoil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.25531914893617%\"\u003e\n \u003cp\u003eC7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.446808510638298%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.574468085106384%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.638297872340425%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.702127659574469%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.319148936170213%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.382978723404255%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.56\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"12.76595744680851%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.25531914893617%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.382978723404255%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"7.446808510638298%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.574468085106384%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.638297872340425%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"11.702127659574469%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"5.319148936170213%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.382978723404255%\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"8.51063829787234%\"\u003e\n \u003cp\u003e\u003cstrong\u003e100.00\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e4.4 Computing Consistency Ratio\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;To evaluate how reliable the assessments have been in comparison to sizable samples of merely random assessments, the consistency ratio (CR) is determined at this point. The AHP always permits a certain degree of consistency, but it shouldn\u0026apos;t go beyond a certain point. The consistency ratio (CR), that evaluates the level of consistency, is determined using the Random inconsistency index (RI) (table 6) created by Saaty (1980). If the CR is substantially greater than 0.1, assessments are unreliable because they are too close to randomness. If the CR value is less than or equivalent to 0.1, the inconsistency is acceptable, or else the pair-wise comparison may be altered (Saaty, 1980). The weights are therefore acceptable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u003c/strong\u003e Different size matrices with random indices\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10%\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"10%\"\u003e\n \u003cp\u003eRI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10%\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10%\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10%\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10%\"\u003e\n \u003cp\u003e1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10%\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10%\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10%\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10%\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10%\"\u003e\n \u003cp\u003e1.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource: Saaty and Vargas 1991.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7\u003c/strong\u003e Computing the Consistency Ratio (CR)\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"109%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.166666666666667%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003eC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003eC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003eC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003eC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003eC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003eC6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003eC7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003eCriteria Weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e\u0026nbsp;Criteria weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003eConsistency Vector\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eRainfall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.166666666666667%\"\u003e\n \u003cp\u003eC1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.63\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eSlope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.166666666666667%\"\u003e\n \u003cp\u003eC2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.74\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eLULC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.166666666666667%\"\u003e\n \u003cp\u003eC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.67\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eVicinity to Sewers \u0026amp; Storm Water Drainage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\"\u003e\n \u003cp\u003eC4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.85\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eVicinity to Natural Drainage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.166666666666667%\"\u003e\n \u003cp\u003eC5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.541666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.75\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eVegetation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.166666666666667%\"\u003e\n \u003cp\u003eC6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.04\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eSoil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"4.166666666666667%\"\u003e\n \u003cp\u003eC7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"6.25%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"9.375%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n 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Results And Discussion","content":"\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Association of influencing parameters with flood locations\u003c/h2\u003e \u003cp\u003eThe causal relationships between flood occurrences and each contributing parameter were investigated and assessed. Additionally, it was discovered that almost 90% (i.e. 211 out of 234) of flood locations fit into the below-5 and 5.01\u0026ndash;10% categories following the spatial distribution analysis of flood locations with slope conditioning factor, making the slope one of the main flood impacting parameters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb; table. 8). The locations along the western coastline (i.e., Santacruz) have seen 2922.30 mm of annual rainfall on average over the past five years (2016\u0026ndash;2020), with the biggest amounts coming in the years 2020 (3721.99 mm) and 2019 (3428.22 mm), which were both catastrophic flood years following the Mumbai floods of July 2005. By combining the spatial patterns of normal annual rainfall and flood locations, it is apparent that 95 percent (i.e. 223) of flood locations are concentrated in regions where the distribution of rainfall varies between 2000 and 2200 mm (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb; table. 8). This indicates that drainage pits and encroachment of low lying areas by municipal authorities are to blame for flood events that have occurred in and area around Mumbai city. The majority of the drainage systems in the study area are either constrained or covered by transportation infrastructures, gummed up by solid waste, as a result, the higher values given above can be attributed to this cause, making these areas extremely vulnerable to floods. In contrast, the frequency of floods is higher due to the influence of stormwater runoff in and around urban peripheries. As a result, this parameter has a relatively strong association and influence over flood vulnerability. This shows that although while the main underlying cause of the flood is the intensity of the rainfall, it is not the only crucial parameter affecting the flood scenario in a region like Greater Mumbai.\u003c/p\u003e \u003cp\u003eIn places with settlements and the coastal alluvium type of soil, flooding occurs more frequently. The soil there is also affected by the type of land use, and when the erosion changes, the flood vulnerability are likely to rise. Around 88.46% (or 207) of all flood locations, are discovered in those areas where the land under built up category is dominated (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb; table. 8). Saltpan and marshes are another class where widespread flooding can be observed. This is primarily because these marshlands were created by digging and cleaning activities along the banks of the Mithi River in the study area, which typically over-flows onto these marshlands and the nearby settlements, during heavy rainfall events.\u003c/p\u003e \u003cp\u003eSince there is typically very little percolation and infiltration due to compact concretization, it was discovered that around 88.46% of flood locations are in the built-up class, making the areas highly vulnerable to floods. The city has essentially no level of infiltration, as seen by the recurrent water logging and sometimes flood situations even from a light rain event. While 62.82% ( or 147) of the flood locations are in the far vicinity category, or less than 100 metres away, it is clear that extensive artificial drainage systems or natural streams turned into sewers have a significant role in improving the ecosystem. Furthermore, the flood locations with vicinity to natural drainage indicates that 24.79% i.e. 58 of locations lie in the 250\u0026ndash;500 mt. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb; table. 8) vicinity, with the increase in water level in these streams owing to heavy rainfall, the nearest neighboring settlement gets flooded and the rest of the locations lie in other categories.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWard-wise Waterlogging flood locations of Greater Mumbai\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSr. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistrict\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eName of the Ward\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDenoted letter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArea in (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLocations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNumber of Waterlogging Spots\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMumbai\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eColaba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCST, Church Gate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSandhurst Road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSandhurst rd., Masjid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMarine Lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMarine Lines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGrant Road\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBombay Central, Grant rd. Charni rd.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eByCulla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eByculla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eParel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF/S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCotton Green\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMatunga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF/N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMahim, Matunga rd.,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDadar/Plaza\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG/N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWadala. Dadar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElphinstone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG/S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eElphinstone rd., Curry rd., Sewri, Mahalaxmi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eAll Wards\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e7096/ 67.79 sq. km\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e76\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMumbai (Sub-urban)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBandra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH/W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBandra, Santacruz, Khar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKhar/Santacruz\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH/E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKhar East, Kherwadi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAndheri (East)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eK/E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eChakala\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAndheri (West)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eK/W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJogeshwari, Andheri W., Villeparle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGoregaon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP/S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGoregaon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMalad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP/N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMalad\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e16\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKandivali\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR/S\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKandivali\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e17\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBorivali\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR/C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBorivali\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDahisar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR/N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDahisar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e19\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKurla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTilak Nagar, Chuna Khatti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e20\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChembur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM/E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMankhurd, Govandi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e21\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChembur (West)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM/W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eChembur\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e22\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGhatkopar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePantnagar, Vikhroli\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBhandup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKanjur Marg, Bhandup\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e24\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMulund\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMulund\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eAll Wards\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e38731/ 370 sq. km\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e158\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eArea under flood vulnerability using AHP\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSr. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlood Vulnerable Zones\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003cp\u003e(Sq. Km)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo. of Waterlogged Locations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e% of Waterlogged Locations under Flood Vulnerable Zone\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery High\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e29.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e204.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e437.71\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e234\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Vulnerability Analysis\u003c/h2\u003e \u003cp\u003eBased on AHP technique weights are computed in percent as 29.42, 20.96, 17.52, 13.99, 8.97, 5.58, and 3.56 for rainfall, slope, LULC, vicinity to sewers and storm water drainage, vicinity to natural drainage, vegetation, and soil type, of the study area, and consistency ratio (CR) is computed as 0.053. This demonstrated a respectable level of accuracy in the pair-wise comparison matrix. Using the arithmetic weighted sum overlay technique in the Arc GIS 10 software, each parameter's raster layer in grid format is multiplied by its assigned weight prior to getting added together. Smaller values denote low vulnerability to floods, and higher values denote high vulnerability to floods. The resulting composite\u003c/p\u003e \u003cp\u003evalues are generated in the range of 144 to 384. The merged map is reclassified into five categories\u0026mdash;severe, very high, high, medium, and slight (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea) - using quantitative method of Standard Deviation (SD) (table. 9). In the study area, the area susceptible to flooding is computed as follows: 46.72% (severe), 11.91% (very high), 6.83% (high), 5.39% (moderate), and 29.15% (Slight) (table. 9). Most of the water-logged spots i.e. 85.46% were found in areas that fall under the category of severe to very high vulnerability and only 14% of locations fall in other three categories as per the data of the flooding sites that are collected from MCGM authorities. According to the findings of the study, that is based on combined weighted of all parameters, the north and north-eastern part of the study area lie in slight to moderate vulnerable zone whereas south and south-western part of the study area come under the category of severe to very high vulnerability.\u003c/p\u003e \u003c/div\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eIn this study, urban flood vulnerable zones of\u0026nbsp;Greater Mumbai, a highly flood-prone metropolis, were delineated using the AHP technique. The slope categories of less than 5 metres and 5.01\u0026ndash;10 metres, the built-up land category in LULC, the vicinity\u0026nbsp;of sewers and storm water drainage\u0026nbsp;of less than 100 metres, the category of natural drainage of 250\u0026ndash;500 metres, rainfall of 2000\u0026ndash;2200 mm category, the category of lowest\u0026nbsp;dense vegetation, and the category of coastal alluvium in soils all exhibit higher interactions\u0026nbsp;with flood locations. The present study draws the conclusion from the causal relationship of all the parameters that built-up land use and vicinity to artificial sewerage systems are the main influencing parameters\u0026nbsp;for flood vulnerability in the study area, with a strong association with the\u0026nbsp;natural parameters like rainfall, slope, and soil.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;We may comprehend the underlying triggering and impacting parameters that are typically neglected in the vagueness of blind infra-structure investment\u0026nbsp;by comprehending the flood vulnerability assessments that are conducted in mainstream and academic study of metropolises like Mumbai, Hyderabad, Bangalore, Delhi etc. Megacities are frequently viewed as the sole loci of economic and financial development in developing countries, where environmental changes are made while obnoxiously ignoring the risk of\u0026nbsp;natural disasters.\u0026nbsp;The outcomes of this study, which frequently suffers from administrative problems, indicate that urban expansion, whether planned or unplanned, can significantly alter the hydrological processes of a watershed.\u0026nbsp;\u0026nbsp;In the scenario of Mumbai, this damage has gone beyond the reasonable limits, culminating in rivers and streams that have been transformed into city sewers blocked with solid waste.\u0026nbsp;Even a brief period of rainfall causes floods in this metropolis since the land surface has achieved zero levels of infiltration.\u0026nbsp;However, despite the fact that the urban land use change\u0026nbsp;is the most essential parameter\u0026nbsp;in the study of flood vulnerability, it simply cannot be chosen in isolation.\u0026nbsp;\u0026nbsp;As a result, another significant conclusion that can be drawn is that in order to understand the full geographic and climatic narrative of urban floods, it is necessary to include all the other parameters and correlate their relationship with urban floods.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;The MCGM and the relevant authorities should therefore repair the current abnormalities in the system, such as the storm water drainage system, with the aid of such scientific investigations carried out at multi-scalar levels, in order to minimize the risk of future damage. The following suggestions are made by the authors of the work are:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eIdentifying flood-prone locations on large-scale maps and identifying areas at risk of flooding at different frequency rates.\u003c/li\u003e\n \u003cli\u003eMake laws governing construction activities in flood plains and enforce them.\u003c/li\u003e\n \u003cli\u003eProvide specific areas for the flood plain, such as by forestation, lands sloping, and\u0026nbsp;construction of small reservoirs, check dams, and ponds, in order to increase the water holding capacity of a watershed.\u003c/li\u003e\n \u003cli\u003eThe potential of the high runoff should be matched while improving the sewers and storm water drainage system and two parameters\u0026nbsp;taking into account while planning the city structures like to know the\u0026nbsp;trend in population growth and the changing climate.\u003c/li\u003e\n \u003cli\u003eDemarcate vulnerable\u0026nbsp;Zones to halt the ruthless encroachment onto marshlands\u0026nbsp;and other fragile\u0026nbsp;water bodies.\u003c/li\u003e\n \u003cli\u003eTo plan for restoration in the form of shelter dwellings both before and after disasters.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e: All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Rohit Mann and Dr. Anju Gupta. The first draft of the manuscript was written by Rohit Mann and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e: Not Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e: The authors express their consent to participate for research and review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: The authors express their consent for publication of research work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e: The data will be made available on demand.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode Availability\u003c/strong\u003e: Not Applicable.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAhmadi O, Mortazavi SB, Mahabadi HA, Hosseinpouri M (2020) Development of a dynamic quantitative risk assessment methodology using fuzzy DEMATEL-BN and leading indicators. 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Doi: 10.1007/s00477-012-0598-5\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Urban Flooding, Multi-Criteria Assessment (MCE), Analytical Hierarchy Process (AHP), Vulnerability, GIS","lastPublishedDoi":"10.21203/rs.3.rs-2171279/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2171279/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrban flooding as well as its socio-economic repercussions is dramatically escalating globally in several coastal cities due to changes in rainfall patterns triggered by climate change. The principal aim of the study is to evaluate the flood vulnerable locations in Mumbai by using a multi-criteria evaluation (MCE) approach. The weights of flood-vulnerable impacting parameters like rainfall, slope, land use/cover (LULC), vicinity to sewers and storm water drainage, vicinity to natural drainage, vegetation, and soil are evaluated using the Analytical Hierarchy Process (AHP). The weights for said parameters are determined by using AHP, and they are as follows: rainfall (29.42%), slope (20.96%), LULC (17.52%), vicinity to sewers and storm water drainage (13.99%), vicinity to natural drainage (8.97%), vegetation (5.58%), and soil (3.56%). In the study area, it is estimated that 46.72% of the area is classified as being severe vulnerable, 18.74% of the area as high to very high and just 34.54% of the area as slight to moderate. Researchers were able to validate the modelling conclusion by examination of all 234 water-logged locations in the study area. Most of the water-logged spots i.e. 85.46% were found in areas that fall under the category of severe to very high vulnerability and only 14% of locations fall in other three categories as per the data of the flooding sites that are collected from MCGM authorities. These model-based flood vulnerable scenario maps are crucial for planning flood conservation and control measures to prioritize the area based on the degree of vulnerability.\u003c/p\u003e","manuscriptTitle":"Assessment of Vulnerability to Urban Floods in Greater Mumbai, India Using Geospatial Techniques","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-10-19 14:45:00","doi":"10.21203/rs.3.rs-2171279/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f5e52a71-2187-43bb-9745-9fb79861d506","owner":[],"postedDate":"October 19th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-03-09T21:14:20+00:00","versionOfRecord":[],"versionCreatedAt":"2022-10-19 14:45:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2171279","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2171279","identity":"rs-2171279","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}