Delineation of Potential Groundwater Zones using GIS-based Fuzzy AHP technique for urban expansion in the South Western fringe of Guwahati City, India

doi:10.21203/rs.3.rs-3239773/v1

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Delineation of Potential Groundwater Zones using GIS-based Fuzzy AHP technique for urban expansion in the South Western fringe of Guwahati City, India

https://doi.org/10.21203/rs.3.rs-3239773/v1

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Due to unprecedented urban growth many localities within the heart of Guwahati city witness groundwater scarcity, mainly during the dry seasons. This study aims to identify potential groundwater zones in the southwestern fringe of the city where the Guwahati Metropolitan Development Authority (GMDA) has adopted plans for future expansion.. Rani and Chayani Barduar are two administrative blocks adjacent to the city possessing a vast tract of unsettled agricultural land ideal for future township development. Multi-criteria decision-making technique using a Fuzzy Analytical Hierarchy Process (FAHP) in Remote Sensing and Geographic Information System (GIS) environment is used to produce the groundwater potential map. A total of eight thematic layers important for groundwater recharge: lithology, geomorphology, slope, rainfall, lineament density, soil, drainage density, and Land Use Land Cover are prepared using satellite data, fieldwork, and other suitable techniques and used as input. The study area is classified into five groundwater potential zones – very high (42.52%), high (28.67%), moderate (17.23%), poor (10.21%), and very poor (1.37%). Validation of the result using a yield map derived from the exploratory wells of Central Ground Water Board (CGWB) shows strong agreement with the prediction accuracy (AUC = 73.36%). Field-derived water level data also show a high negative correlation (R² = 0.71) with yield data indicating high specific yield in wells with shallow water levels. The study results will help planners and policymakers with future urban development strategies and sustainable groundwater management practices.

Hydrology

Groundwater potential zones

Groundwater recharge

GIS

AHP

Urban expansion

Southwest Guwahati

Groundwater is a vital renewable resource that requires proper management to ensure long-term use (Mays 2013). Though groundwater occupies only a small portion (0.06%) of Earth’s available water, it is the most critical freshwater source used for drinking purposes (Schwartz and Zhang 2002). Expanding population, unscientific exploitation, and improper management of groundwater in developing countries have led to the deterioration of both quality and quantity and thus demand a critical assessment for sustainable management (Tolche 2021). Groundwater will be the key parameter in achieving many of the Sustainable Development Goals targeted by the United Nations (Gupy et al. 2018). The underground water stock depends on percolation through porous spaces of soils and rocks; which in turn determines urban water supply and agricultural activity (Da Costa et al. 2019). Assessment of groundwater potential zone is important to counteract water scarcity problems caused by various factors such as rapid urbanization, climate change, uneven distribution of water resources, etc. (Raju et al. 2019). In India, about 400 million people live in urban areas resulting in changes in LULC including deforestation and an increase in impervious surfaces affecting groundwater recharge and thus causing an acute shortage of water (Roy et al. 2022). Guwahati, the gateway to northeast India, is one of the fastest-growing cities in India. Guwahati had a population of 43,615(1951 census) and subsequently increased to 9,63,429 in the 2011 census (Desai et al. 2014).It is estimated that Guwahati will house approximately 22 lakh residents by 2025. To tackle this rapidly growing scenario, Guwahati Metropolitan Development Authority (GMDA) has adopted specific Master Plans for Greater Guwahati with extended spatial periphery to previously untouched rural areas. The greater Guwahati metropolitan area which presently covers an area of 262 sq. km is going to cover 328 square kilometers by 2025 (GMA Master Plan 2025). Topographically the south-western fringe of Guwahati city has vast tracts of unsettled agricultural land and thus is ideal for future expansion. Urbanization will come with different facets such as industrialization, sprawl, waste management, irrigation, contamination, and their complex interactions. Thus an assessment of the groundwater potential zone is mandatory for the city planners to counteract any future negative consequences. The integrated use of remote sensing and Geographic Information Systems (GIS) in groundwater studies has been well documented in various literatures (Freitas et al. 2019; Moghaddam et al. 2013; Nas and Berktay 2008; Rahmati et al. 2014; Sandoval and Tiburan Jr. 2019), especially due to its time and cost-effectiveness (Kumar et al. 2016). Researchers have adopted different multivariate statistical techniques in estimating groundwater potential; however, the Analytical Hierarchy Process (AHP) is preferred due to its ease of integration with remote sensing and the GIS environment (Celik 2019; Dar et al. 2020; Lentswe and Molwalefhe 2020). GIS plays a vital role in spatial decision-making where a large number of alternatives need to be analyzed (Rikalovic et al. 2014). Though Analytical Hierarchy Process (AHP), developed by Saaty, is one of the most versatile decision-making techniques that has been used to solve complex problems involving competition for multiple choices (Rezaei and Tahsili 2018), there is a chance of vagueness and uncertainty in crisp judgment by human reasoning which can be overcome by introducing fuzziness to the process (Torabi-Kaveh et al. 2016). Fuzzy set theory is a specially designed mathematical tool to deal with such uncertainty, vagueness, and imprecision (Kahraman et al. 2004). It was originally introduced by Lotfi Zadeh in the 1960s with the implementation of classes or groups of data that are not sharply defined & have a blurred boundary resembling closely real-world problems (Brasil et al. 1998).

Study Area

The study area includes the Rani and Chayani Barduar administrative block of Kamrup – Metro (Guwahati city) and the Kamrup-Rural Districts of Assam, covering an area of 423.03 sq. km containing 130 villages. Urbanization is prominent towards north adjacent to the Guwahati Airport. Figure 1 shows a hill shade map of the study area prepared using SRTM DEM. The Brahmaputra River flows north of the study area. Physiography of the area is mainly the floodplains of the river Brahmaputra and its tributaries, along with hilly terrains and isolated hillocks of the Meghalaya plateau in the south.

The region witnesses a tropical monsoon-type climate with rainfall occurring between June to September. The average temperature ranges from 35ºC in summer to 9ºC in winter. Though the Brahmaputra river flows adjacent to the study area, significant urban populations traditionally rely on groundwater sources due to the inadequate supply and capacity of water treatment plants. Inequitable distribution, unplanned development of aquifers, gradual reduction in subsurface infiltration due to increased urbanization, noticeable changes in seasonal rainfall patterns are some factors that led to water scarcity within the city as well as surrounding areas, especially in dry seasons (Das and Goswami 2013). Therefore, assessment of groundwater prospect zone is necessary to ensure future urban expansion potential.

Multiple parameters play a dominant role in groundwater recharge, and these are used to estimate the groundwater potential. The number of thematic layers that can be used also depends on the availability of data for the study region. As per the report by the Central Ground Water Board, the chief source of groundwater recharge in the Himalayan region is the glaciers, and in the foothill region, rainfall plays the dominant role contributing a copious amount. In Northeast India, groundwater occurrence can be witnessed in weathered residuum and fractures/ joints in consolidated formations in Assam and Meghalaya plateau, semi-consolidated porous formations of tertiary rocks, and unconsolidated formations of Quaternary age in the Brahmaputra & Barak valleys (Gupta 2014). Ahmed and Sajjad (2017) analyzed the groundwater potential zone in the lower Barapani watershed adjacent to the study area and found that gentle slopes, flood plain, monsoon rainfall, and the presence of wetlands are certain key causes for high groundwater potential. Based on the literature survey, expert opinion, and in consultation with local hydro-geologists the following parameters and their relative importance are considered for the estimation of groundwater potential in the study area: lithology, geomorphology, slope, land use land cover, rainfall, soil, lineament density, and drainage density. Lithology maps are prepared using data from the Bhukosh platform of the Geological Survey of India. For geomorphology maps, ISRO/ Bhuvan portal is used as a reference. Soil maps are prepared using datasets from the National Bureau of Soil Survey and Land Use Planning, Government of India. Slope and drainage density is prepared using the Digital Elevation Model (DEM) of the Shuttle Radar Topography Mission (SRTM). Lineament density and LULC maps are prepared using Landsat 8 satellite imagery. Rainfall data is collected from the GIOVANNI application of NASA. The groundwater yield map is prepared using data from Central Groundwater Board (CGWB). Static water table data is collected by field survey. Remote sensing and GIS are mainly used in preparing different layers. Arc GIS 10.7, ERDAS Imagine 10, and ENVI 5.6 are the prime GIS software used for the rectification, digitization, resampling, and processing of various data. Weight assignment to the parameters and their corresponding classes is carried out using Fuzzy Analytical Hierarchy Process (FAHP) based on their relative importance to groundwater recharge. A fuzzy set is characterized by a membership function that assigns to each object a grade of membership ranging between zero and one. Mathematically, if $X$ is a universal set, a fuzzy set $\stackrel{ˇ}{A}$ in$X$ is characterized by its membership function denoted by${\mu }_{\stackrel{ˇ}{A}}$, so that ${\mu }_{\stackrel{ˇ}{A}} : X \to \left[\text{0,1}\right]$(Bhargava 2013). Though there are several different forms of membership functions, triangular and trapezoidal membership functions are extensively used due to their simplicity and efficiency in real-time computation (Azam et al. 2020). In this study, the triangular fuzzy membership function is used for computational ease. Before the implementation of the FAHP method, the Analytical Hierarchy Process (AHP) developed by Saaty is used to check the consistency of the decisions by the decision maker. It uses a pairwise comparison matrix calculating the weights for each criterion involved (Chakraborty and Joshi 2014). Though the nine-point AHP scale by Saaty gives information about the dominance of each parameter above the other, the fixed value discrete scale judgment cannot simplify the information unpredictability creating a weak spot in decision making However, fuzzy set theory with its interval judgment (Table 1) deal with such impreciseness (Kaganski et al. 2018). The matrix consistency is assessed using the Consistency Index (CI) formula: $CI= \frac{{\lambda }_{\text{max}- }n}{n-1}$ where ${{\lambda }}_{\text{m}\text{a}\text{x}}$ is the principal eigenvalue of the pairwise comparison matrix, and n is the number of parameters used in the analysis. The Consistency Ratio (CR) is determined by dividing Consistency Index (CI) by Ratio Index (RI). For consistency of the weight, the CR value should be less than 0.1. If CR is higher than 0.1, re-evaluation within the matrix is needed (Tošovic´-Stevanovic' et al. 2020). The CR for this particular study is found to be 0.03, which is in the acceptable range.

Table 1

Linguistic scale for FAHP pair-wise comparison (Ayhan 2013)
Saaty Scale	Definition	Fuzzy Triangular Scale
1	Equally important	(1,1,1)
3	Weakly important	(2,3,4)
5	Fairly important	(4,5,6)
7	Strongly important	(6,7,8)
9	Absolutely important	(9,9,9)
2, 4, 6, 8	The intermittent values between two adjacent scales	(1,2,3), (3,4,5), (5,6,7), (7,8,9)

Buckley’s column geometric mean method is used to get the final normalized weight. The process uses centre of area method as the de-fuzzification technique. The following mathematical equations are used in this study (Bayer and Karamasa 2018).

The membership function for triangular fuzzy numbers is defined by the triplet $\left(l, m, u\right)$ indicating the smallest possible value, the most promising value, and the largest possible value. The membership function is defined by the following equation:

$${\mu }_{\stackrel{\sim}{M}}\left(x\right)= \left\{\begin{array}{c}\frac{\left(x-l\right)}{\left(m-l\right)}, l\le x\le m,\\ \frac{\left(u-x\right)}{\left(u-m\right)}, m\le x\le u\\ 0, otherwise\end{array}\right.$$

A triangular fuzzy number $\stackrel{\sim}{M}$ is shown in the Fig. 2

Fuzzy pair-wise matrices can be represented as

$$\stackrel{\sim}{B}= {\left({\stackrel{\sim}{b}}_{ij}\right)}_{n\times k}\left[\begin{array}{cccc}\left(\text{1,1},1\right)& \left({l}_{12},{m}_{12},{u}_{12}\right)& \dots & \left({l}_{1k}, {m}_{1k}, {u}_{1k}\right)\\ \left({l}_{21},{m}_{21},{u}_{21}\right)& \left(\text{1,1},1\right)& \cdots & \left({l}_{2k}, {m}_{2k}, {u}_{2k}\right)\\ ⋮& ⋮& \cdots & ⋮\\ \left({l}_{n1},{m}_{n1},{u}_{n1}\right)& \left({l}_{n2},{m}_{n2},{u}_{n2}\right)& \cdots & \left(\text{1,1},1\right)\end{array}\right]$$

Where${\stackrel{\sim}{b}}_{ij= }\left({l}_{ij}, {m}_{ij}, {u}_{ij}\right), {\stackrel{\sim}{b}}_{ij}^{-1}=(\frac{1}{{u}_{ji}},\frac{1}{{m}_{ji}},\frac{1}{{l}_{ji}})$

$$\text{F}\text{o}\text{r} i=\text{1,2},3,\dots .n;j=\text{1,2},3,\dots .,k \text{e}\text{l}\text{e}\text{m}\text{e}\text{n}\text{t}\text{s} \text{a}\text{n}\text{d} i \ne j$$

New pair-wise comparison matrix averaging preferences for all decisions:

$$\stackrel{\sim}{B}= \left[\begin{array}{cccc}\stackrel{\sim}{{b}_{11}}& \stackrel{\sim}{{b}_{12}}& \cdots & \stackrel{\sim}{{b}_{1n}}\\ \stackrel{\sim}{{b}_{21}}& \stackrel{\sim}{{b}_{22}}& \cdots & \stackrel{\sim}{{b}_{2n}}\\ \cdots & \cdots & \cdots & \cdots \\ \stackrel{\sim}{{b}_{m1}}& \stackrel{\sim}{{b}_{m2}}& \cdots & \stackrel{\sim}{{b}_{mn}}\end{array}\right]$$

Geometric mean of each criterion is calculated by:

$$\stackrel{\sim}{{h}_{i }}= {\left[{\prod }_{j=1}^{n}\stackrel{\sim}{{b}_{ij}}\right]}^{1/n} \text{W}\text{h}\text{e}\text{r}\text{e}, i=\text{1,2},\dots .,m$$

Fuzzy weights of each criterion are obtained by:

$$\stackrel{\sim}{{\text{w}}_{\text{i}}}= \stackrel{\sim}{{\text{h}}_{\text{i}}} \otimes {\left(\stackrel{\sim}{{\text{h}}_{\text{i}}} \oplus \stackrel{\sim}{{\text{h}}_{2}}\oplus \dots . \oplus \stackrel{\sim}{{\text{h}}_{\text{n}}}\right)}^{-1}= \left({\text{l}}_{\text{i}}, {\text{m}}_{\text{i}}, {\text{u}}_{\text{i}}\right)$$

Centre of area de-fuzzification technique to transform fuzzy weights into crisp ones:

$${k}_{i}= \frac{{l}_{i}+ {m}_{i}+ {u}_{i}}{3}$$

Crisp weights to normalized final weight:

$${z}_{i}=\frac{{k}_{i}}{{\sum }_{i=1}^{m}{k}_{i}}$$

Finally, a weighted linear combination method (Das and Pal 2019; Mallick et al. 2019) is used in the GIS environment to delineate potential groundwater zones.

$$GWPZ=\left({RF}_{w}\times {RF}_{wc}\right)+ \left({LT}_{w}\times {LT}_{wc}\right)+\left({GM}_{w}\times {GM}_{wc}\right)+\left({LD}_{w}\times {LD}_{wc}\right)+\left({DD}_{w}\times {DD}_{wc}\right)+\left({S}_{w}\times {S}_{wc}\right)+\left({SL}_{w}\times {SL}_{wc}\right)+\left({LU}_{w}\times {LU}_{wc}\right)$$

Where, GWPZ = Groundwater Potential Zone, RF = Rainfall, LT = Lithology, GM = Geomorphology, LD = Lineament Density, DD = Drainage Density, S = Soil, SL = Slope, LU = Land Use Land Cover, $w$ = Normalized weight of the parameters and $wc$ = Normalized weight of the corresponding classes of the parameters

Lithology

Lithology controls the percolation of water flow, thus influencing groundwater recharge rates (Yeh et al. 2016). Rocks with less compaction, a higher degree of weathering, and fracturing are suitable for groundwater recharge (Senanayake et al. 2016). The dominant geological formations encountered within the study area (Fig. 4a) are alluviums of the Brahmaputra river and its tributaries and Precambrian rocks of the Meghalaya plateau. The alluvial deposits can be categorized into two parts: older alluvium (covering 6.5% area), consisting of reddish and brownish coarse sand particles with irregularly distributed, unsorted pebbles deposited during Pleistocene, and newer alluvium (53.6%) consisting of light-colored sand, silt, and clay deposited during recent geological time. These alluvium deposits are ideal for groundwater recharge providing good porosity and permeability conditions. The Precambrian rocks (39.8%) mainly consist of gneisses intruded by pegmatites and quartz. Granitic gneiss is the dominant rock unit of the area, showing a concordant relationship with all other foliated rock units. Hard rock lithology is usually not suitable for groundwater recharge except for significant amounts of weathering and fracturing conditions.

Table 2

Fuzzy comparison matrix and normalized weight for different classes of lithology
Classes	Migmatite, Granitic Gneiss	Older Alluvium	Newer Alluvium	Normalized weight
Migmatite, Granitic Gneiss	1,1,1	1/6, 1/5, 1/4	1/6, 1/5, 1/4	0.10
Older Alluvium	4,5,6	1,1,1	1,1,1	0.45
Newer Alluvium	4,5,6	1,1,1	1,1,1	0.45

Geomorphology

In combination with structures and lithology, geomorphic features control the occurrence of groundwater (Solomon and Quiel, 2006). The distinguishable geomorphic features within the study area (Fig. 4b) are a) Floodplains: The study area witnesses periodic flooding due to the Brahmaputra river in the north, along with its tributary channels flowing from the Meghalaya plateau in the south. The floodplains contain old meanders, paleochannels, natural levees, back swamps, wetlands, and channel bars. Both active and older floodplains can be identified within the study area. b) Alluvial plains: Alluvial plains within the study area occur at a slightly higher elevation than the floodplains. Both younger and older alluviums can be identified within the area. c) Piedmont zone: Piedmont zone is mainly concentrated towards the south of the study area at the foothill region consisting of eroded materials of adjacent denudational hills. d) Denudational hills: Relict hills of the Precambrian plateau are mainly formed by stream erosion. These are moderate to highly dissected gneissic hills. Flood plains and alluvial plains are assigned the highest rank in terms of groundwater recharge followed by the piedmont zone and gneissic hills.

Table 3

Fuzzy comparison matrix and normalized weight for different classes of geomorphology
Class	Denudational Hills	Pediment Pediplain Complex	Alluvial Plain/ Flood Plain	Water bodies	Normalized weight
Denudational Hills	1, 1, 1	1/3, 1/2, 1	1/4, 1/3, 1/2	1/6, 1/5, 1/4	0.10
Pediment Pediplain Complex	1, 2, 3	1, 1, 1	1/3, 1/2, 1	1/4, 1/3, 1/2	0.14
Alluvial Plain/ Flood Plain	2, 3, 4	1, 2, 3	1, 1, 1	1/3, 1/2, 1	0.24
Water bodies	4, 5, 6	2, 3, 4	1, 2, 3	1, 1, 1	0.52

Slope

The slope of the area (Fig. 4c) directly affects the controlling factors of groundwater availability. A steeper slope results in more runoff than infiltration, causing less chance of groundwater recharge (Rani et al. 2015). The study area is divided into five classes based on slope angle: 0^o-5^o (flat), 5^o-15^o (gentle), 15^o-30^o (moderate), 30^o-45^o (steep), and 45^o-89^o (very steep) (Fig. 3c). 70.86% and 14.72% of the study areas fall under the “flat” and “gentle” category, indicating promising groundwater reserves.

Table 4

Fuzzy comparison matrix and normalized weight for different classes of slope
Slope	45^o-89^o (very steep)	30^o-45^o (steep)	15^o-30^o (moderate)	5^o-15^o (gentle)	0^o-5^o (flat)	Normalized weight
45^o-89^o (very steep)	1, 1, 1	1/3, 1/2, 1	1/4, 1/3, 1/2	1/6, 1/5, 1/4	1/7, 1/6, 1/5	0.04
30^o-45^o (steep)	1, 2, 3	1, 1, 1	1/3, 1/2, 1	1/4, 1/3, 1/2	1/6, 1/5, 1/4	0.06
15^o-30^o (moderate)	2, 3, 4	1, 2, 3	1, 1, 1	1/3, 1/2, 1	1/4, 1/3, 1/2	0.10
5^o-15^o (gentle)	4, 5, 6	2, 3, 4	1, 2, 3	1, 1, 1	1/3, 1/2, 1	0.22
0^o-5^o (flat)	5, 6, 7	4, 5, 6	2, 3, 4	1, 2, 3	1, 1, 1	0.58

Land use land cover (LULC)

Land use land cover patterns and change significantly affect groundwater quality and quantity (He et al. 2019; Singh et al. 2010). LULC of the study area (Fig. 4d) is delineated using Landsat 8 satellite imagery using ArcGIS 10.7 software based on visual image interpretation technique, followed by field verification. Major LULC classes include agricultural land/household plantation (54.91%) and forest cover (27.16%). Both classes are given a higher weight because they can hold a substantially high proportion of water (Agarwal et al. 2013; Arulbalaji et al. 2019).

Table 5

Fuzzy comparison matrix and normalized weight for different classes of LULC
LULC	Built-up	Forest	Agricultural Land/ Grassland/ Wasteland	Water bodies	Normalized weight
Built-up	1, 1, 1	1/4, 1/3, 1/2	1/5, 1/4, 1/3	1/6, 1/5, 1/4	0.06
Forest	2, 3, 4	1, 1, 1	1/4, 1/3, 1/2	1/5, 1/4, 1/3	0.10
Agricultural Land/ Grassland/ Wasteland	3, 4, 5	2, 3, 4	1, 1, 1	1/4, 1/3, 1/2	0.21
Water bodies	4, 5, 6	3, 4, 5	2, 3, 4	1, 1, 1	0.63

Rainfall

The Tropical Rainfall Measuring Mission version 7 product (TRMM V7) was used for rainfall estimation over the study area for ten years (2010–2019). TRMM V7, available in GIOVANNI (Geospatial Interactive Online Visualization and Analysis Infrastructure), is a web-based application developed by NASA. It outperforms other remote sensing precipitation products as it contains real-time gridded precipitation (3B42RTV7) with near-global coverage along with gauge-adjusted post-real-time research product (3B42V7) having high spatial (0.25°) and temporal (3 hours) resolution (Liu et al. 2016). The average annual rainfall map is prepared using the IDW interpolation technique, taking 10 years of rainfall over the study area (Fig. 4e).

Table 6

Fuzzy comparison matrix and normalized weight for different classes of rainfall
Classes	3163.00–3380.50 mm	3380.50–3598.03 mm	3598.03–3815.60 mm	3815.60–4033.06 mm	4033.06–4250.58 mm	Normalized Weight
3163.00–3380.50 mm	1, 1, 1	1/3, 1/2, 1	1/4, 1/3, 1/2	1/5, 1/4, 1/3	1/6, 1/5, 1/4	0.07
3380.50–3598.03 mm	1, 2, 3	1, 1, 1	1/3, 1/2, 1	1/4, 1/3, 1/2	1/5, 1/4, 1/3	0.13
3598.03–3815.60 mm	2, 3, 4	1, 2, 3	1, 1, 1	1/3, 1/2, 1	1/5, 1/4, 1/3	0.20
3815.60–4033.06 mm	3, 4, 5	2, 3, 4	1, 2, 3	1, 1, 1	1/3, 1/2, 1	0.27
4033.06–4250.58 mm	4, 5, 6	3, 4, 5	2, 3, 4	1, 2, 3	1, 1, 1	0.33

Lineament density

Lineaments are subsurface expressions in topography associated with faults - linear fracturing, and bending deformation that indicates increased permeability of the crust (Florinsky 2016). Faults and fracture zones play important roles in groundwater dynamics and are an indicator of feeding for the aquifer (Ammar and Kamal 2018). Landsat 8 satellite with Operational Land Imager (OLI) captured bands are used to delineate lineaments by adopting various digital image enhancement techniques. Out of different techniques, Principal Component Analysis (PCA), band rationing, and directional filtering are commonly used for extracting the lineaments of the study area (Fig. 4f). PCA analysis carried out using band 1 to band 7 gives principal components, and PC (1, 2, 3) reveals structural information (Mathew and Ariffin 2018).RGB composite of band ratios (7/5, 6/4, 4/2) is suitable for enhancing lithological features (Al-Nahmi et al. 2016). Directional convolution filtering is applied on band 6 of Landsat 8 imagery using ENVI software in four directions N-S (0^o), NE-SW (45^o), E–W (90^o), and NW–SE (135^o), highlighting the main lineament directions of the study area (Javhar et al. 2019).

Table 7

Fuzzy comparison matrix and normalized weight for different classes of lineament density
Lineament Density	0–0.29	0.29–0.58	0.58–0.87	0.87–1.17	1.17–1.46	Normalized weight
0–0.29	1, 1, 1	1/3, 1/2, 1	1/4, 1/3, 1/2	1/5, 1/4, 1/3	1/6, 1/5, 1/4	0.07
0.29–0.58	1, 2, 3	1, 1, 1	1/3, 1/2, 1	1/4, 1/3, 1/2	1/5, 1/4, 1/3	0.13
0.58–0.87	2, 3, 4	1, 2, 3	1, 1, 1	1/3, 1/2, 1	1/5, 1/4, 1/3	0.20
0.87–1.17	3, 4, 5	2, 3, 4	1, 2, 3	1, 1, 1	1/3, 1/2, 1	0.27
1.17–1.46	4, 5, 6	3, 4, 5	2, 3, 4	1, 2, 3	1, 1, 1	0.33

Soil

Groundwater recharge and quality are influenced by the spatial variation of soil types (Rukundo and Dogan 2019). A Soil map of the area (Fig. 4g) is prepared using a soil classification scheme developed by the National Bureau of Soil Survey & Land Use Planning (NBSS&LUP). Soil samples were collected and analyzed in the laboratory to determine soil parameters, mainly for texture and drainage conditions. The data are plotted in a GIS environment, and the ‘natural neighbor’ interpolation technique is adopted to show the distribution of soil types. Fine-grained soils facilitate less infiltration due to less permeability in comparison to coarse-grained soil. Therefore, a lower rank is given for soils with a fine-grained texture and a higher rank for soils with a coarse-grained texture.

Table 8

Fuzzy comparison matrix and normalized weight for different classes of soil
Soil	Typic Dystrochrepts (Clayey Fine loamy)	Umbric Dystrochrepts (Fine loamy skeletal) / Fluventic Dystrochrept (Fine loamy	Aeric Haplaquepts (Fine silty	Aeric Fluvaquent (coarse silty)	Normalized weight
Typic Dystrochrepts (Clayey Fine loamy)	1, 1, 1	1/3, 1/2, 1	1/4, 1/3, 1/2	1/4, 1/3, 1/2	0.11
Umbric Dystrochrepts (Fine loamy skeletal) / Fluventic Dystrochrept (Fine loamy	1, 2, 3	1, 1, 1	1/3, 1/2, 1	1/4, 1/3, 1/2	0.15
Aeric Haplaquepts (Fine silty	2, 3, 4	1, 2, 3	1, 1, 1	1/3, 1/2, 1	0.30
Aeric Fluvaquent (coarse silty)	2, 3, 4	2, 3, 4	1, 2, 3	1, 1, 1	0.44

Drainage density

Drainage density is a measure of the total stream length to the total basin area (Strahler 1964) and is often expressed in km/km². Lower drainage density indicates higher permeability, with greater infiltration of rainfall resulting in good groundwater conditions (Magesh et al. 2012). The drainage network of the study area is extracted from SRTM DEM (30 m spatial resolution) using the Hydrology tool under the Spatial Analyst Toolbox of ArcGIS 10.7. The drainage density is calculated using the line density tool (Fig. 4h).

Table 9

Fuzzy comparison matrix and normalized weight for different classes of drainage density
Drainage Density	2.67–3.34	2.00–2.67	1.33–2.00	0.66–1.33	0–0.66	Normalized weight
2.67–3.34	1, 1, 1	1/3, 1/2, 1	1/4, 1/3, 1/2	1/5, 1/4, 1/3	1/6, 1/5, 1/4	0.07
2.00–2.67	1, 2, 3	1, 1, 1	1/3, 1/2, 1	1/4, 1/3, 1/2	1/5, 1/4, 1/3	0.13
1.33–2.00	2, 3, 4	1, 2, 3	1, 1, 1	1/3, 1/2, 1	1/5, 1/4, 1/3	0.20
0.66–1.33	3, 4, 5	2, 3, 4	1, 2, 3	1, 1, 1	1/3, 1/2, 1	0.27
0–0.66	4, 5, 6	3, 4, 5	2, 3, 4	1, 2, 3	1, 1, 1	0.33

Table 10

Fuzzy comparison matrix and normalized weight for controlling parameters
Parameters	Rainfall	Lithology	Geomorphology	Lineament Density	Drainage Density	Soil	Slope	LULC	Normalized Weights
Rainfall	1, 1, 1	1, 2, 3	1, 2, 3	2, 3, 4	2, 3, 4	4, 5, 6	5, 6, 7	6, 7, 8	0.27
Lithology	1/3, 1/2, 1	1, 1, 1	1, 2, 3	1, 2, 3	2, 3, 4	3, 4, 5	4, 5, 6	5, 6, 7	0.18
Geomorphology	1/3, 1/2, 1	1/3, 1/2, 1	1, 1, 1	1, 2, 3	2, 3, 4	3, 4, 5	4, 5, 6	5, 6, 7	0.18
Lineament Density	1/4, 1/3, 1/2	1/3, 1/2, 1	1/3, 1/2, 1	1, 1, 1	1, 2, 3	2, 3, 4	5, 6, 7	5, 6, 7	0.14
Drainage Density	1/4, 1/3, 1/2	1/4, 1/3, 1/2	1/4, 1/3, 1/2	1/3, 1/2, 1	1, 1, 1	1, 2, 3	2, 3, 4	3, 4, 5	0.09
Soil	1/6, 1/5, 1/4	1/5, 1/3, 1/3	1/5, 1/3, 1/3	1/4, 1/3, 1/2	1/3, 1/2, 1	1, 1, 1	2, 3, 4	2, 3, 4	0.09
Slope	1/7, 1/6, 1/5	1/6, 1/5, 1/4	1/6, 1/5, 1/4	1/7, 1/6, 1/5	1/4, 1/3, 1/2	1/4, 1/3, 1/2	1, 1, 1	1, 2, 3	0.03
LULC	1/8, 1/7, 1/6	1/7, 1/6, 1/5	1/7, 1/6, 1/5	1/7, 1/6, 1/5	1/7, 1/6, 1/5	1/5, 1/3, 1/3	1/4, 1/3, 1/2	1/3, 1/2, 1	0.03

Delineation of Groundwater Potential Zonation

The groundwater potential zones were classified into five categories based on the GWPZ equation – Very High (179.87 km², 42.52%), High (121.28 km², 28.67%), Moderate (72.89 km², 17.23%), Poor (43.19 km², 10.21%) and Very Poor (5.80 km², 1.37%) (Fig. 5a). Very high and high groundwater potential areas are found mainly in low-lying flat/gentle flood plains and alluvial plains towards the north of the study area. Towards the south eroded hills with high lineament density are also having high groundwater potential. Proximity to the river Brahmaputra, the presence of wetlands, vast areas of unsettled agricultural land, as well as basin-like topography due to the surrounding elevated landforms of the Shillong plateau is some justification against this abundance of groundwater. Moderate groundwater potential areas are mainly found toward the south of the study area; it comprises the piedmont pediplain complex and hills having moderate lineament density. Poor and very poor groundwater potential areas are found in steeper gneissic hills with relatively moderate to low rainfall and low lineament density. These areas fall under the reserve forest and human settlement is not authorized.

Validation

The zones are validated by generating a yield map (Fig. 5b) using yield data of exploratory wells drilled by the Central Ground Water Board (CGWB) and field-derived static water table data of household dug wells. The highest yield of 24 m³/hr is observed in low-lying flood plains areas, and the lowest yield of 5 m³/hr is observed in gneissic hills, showing an agreement with FAHP-derived groundwater potential zonation. Quantitative validation of the Receiver Operating Characteristic (ROC) curve (Fig. 5c) analysis shows the Area Under the Curve (AUC) to be 73.36% indicating good prediction accuracy of the method (Andualem and Demeke 2019; Kumar and Krishna 2016). Fieldwork was conducted in 16 households dug wells in both pre-and post-monsoon, and the mean annual groundwater level is measured. Prior to the field visit for groundwater level collection, the area is divided into a 5km × 5km grid using ArcGIS software to collect samples at regular intervals. Groundwater level (mbgl) shows a negative correlation with yield data with an R² value of 0.71, indicating high specific yield in wells with shallow water levels and vice versa (Fig. 5d).

Remote Sensing and GIS-based groundwater potential zone delineation using the FAHP technique are efficient as well as cost-effective. Satellite remote sensing coupled with limited field verification and laboratory work can assist in preparing a database of selected parameters influencing groundwater recharge, such as rainfall, lithology, geomorphology, lineament density, drainage density, soil, slope, and land use land cover. GIS allows analyzing various parameters under a single platform. Different parameters have different degrees of influence in groundwater recharge, and their relative importance needs to be assessed for decision-making. Fuzzy Analytical Hierarchy Process (FAHP) is feasible in prioritizing one parameter above the other by assigning weights in a pairwise comparison manner considering the real-world subjective judgments. Similarly, different classes under each parameter are allocated weights, and a potential groundwater map is prepared in the GIS environment. Very high and high groundwater potential areas are found towards the north in low-lying flat flood plains and alluvial plains; moderate potential areas are piedmont pediplain complex and hills having moderate lineament density, poor and very poor potential areas are steeper gneissic hills having relatively moderate to low rainfall and low lineament density. Validation of the groundwater potential map with yield map and field-derived water level data gives satisfactory agreement with the obtained result. The study area lies towards the southwest of Guwahati city and has the potential for future urban expansion. As the city witnesses water scarcity due to the rapid growth of population and infrastructure, a prior assessment is needed in its fringe areas for sustainable urban expansion. This type of pilot study is helpful in decision-making processes for the professionals and policymakers involved in groundwater, urban planning, and allied sectors.

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

The authors have no relevant financial or non-financial interests to disclose.

Author Contributions

Both the authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Rakesh Kumar Sarmah. The first draft of the manuscript was written by Rakesh Kumar Sarmah. Both the authors read and approved the final manuscript.

Acknowledgements

The authors would like to thank different government and nongovernment organizations for providing valuable data; various regional experts for their valuable suggestions and guidance. The authors give special thanks to Mr. Thatey Mohamed Zuhail for his valuable guidance regarding manuscript preparation.

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Delineation of Potential Groundwater Zones using GIS-based Fuzzy AHP technique for urban expansion in the South Western fringe of Guwahati City, India

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Abstract

Figures

Introduction

Study Area

Materials and methods

Results and discussion

Lithology

Geomorphology

Slope

Land use land cover (LULC)

Rainfall

Lineament density

Soil

Drainage density

Delineation of Groundwater Potential Zonation

Validation

Conclusions

Declarations

References

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