Home Geology and Mineralogy MIF and AHP methods for delineation of groundwater potential zones using remote sensing and GIS techniques in Tirunelveli, Tenkasi District, India
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MIF and AHP methods for delineation of groundwater potential zones using remote sensing and GIS techniques in Tirunelveli, Tenkasi District, India

  • Samuel Prabaharan Jebaraj EMAIL logo and Viji Rajagopal
Published/Copyright: May 23, 2024
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Open Geosciences
From the journal Open Geosciences Volume 16 Issue 1

Abstract

The present study aims to identify whether the delineation of potential groundwater potential zones (GWPZs) is essential for monitoring surface and conserving underground water resources. This study analysed the morphology of earth surface characteristics such as geomorphology, lineament density, lithology, slope, soil types, land use and land cover, drainage density, land surface temperature, normalized difference vegetation index, rainfall, and topographic wetness index parameters to delineate the potential groundwater zones. This article applies the analytical hierarchy process (AHP) and multi-influence factor (MIF) methods to identify potential groundwater zones in the Tirunelveli and Tenkasi districts of Tamil Nadu, India. In the AHP method, individual parameter's geometric mean and normalized weights were determined using the pair-wise matrix analytical method. Remote sensing-geographic information system (RS-GIS) techniques were used to generate thematic map layers from normalized weights to delineate GWPZs. The GWPZs were classified as Very Low, Low, Medium, High, and Very High. The result shows that the GWPZs were identified as 3.57, 0.55, 6.62, 58.09, and 31.21% in the study area for the five classes, respectively. In this study, the thematic maps were also prepared by assigning fixed scores and weights from the MIF approach. In the MIF approach, GWPZs were classified into five classes and identified as 3.16, 0.33, 2.14, 61.21, and 33.16% in the study area, respectively. GWPZ maps were evaluated for both MIF and AHP techniques using the Kappa statistics method with agreement values of 0.77 and 0.72%, respectively. This study's GIS-RS method is more proficient and efficient in delineating the GWPZs.

Keywords: remote sensing; GIS; groundwater potential zones; data statistical analysis; AHP; MIF; geospatial mapping

1 Introduction

Groundwater is an essential natural resource for the dependable and cost-effective delivery of potable water in urban and rural areas [1,2]. Currently, groundwater contributes approximately 34% of the total annual water supply and is a significant source of potable water [3]. Consequently, it is fundamental to human existence and the health of specific aquatic and terrestrial ecosystems. Due to human development activities and climate change, water consumption has been grown within the previous decade [4]. Therefore, evaluating this resource is crucial for the sustainable management of groundwater systems. Geographic information system (GIS) and remote sensing (RS) techniques are widely employed to manage diverse natural resources.

This research primarily aims to prepare 11 thematic layers such as geomorphology, lineament density, lithology, slope, soil types, land use and land cover, drainage density, land surface temperature, normalized difference vegetation index (NDVI), rainfall, and topographic wetness index (TWI). Using a GIS system, potential groundwater zones were calculated using the multi-influence factor (MIF) and analytical hierarchy process (AHP) techniques. These endeavours aimed to create an accurate groundwater potential map using GIS data. The AHP and MIF, verified using kappa statics, created maps of possible groundwater zones [5, 6, 7].

2 Study area

The study region is located in southern Tamil Nadu, which is bordered by Virudhunagar District to the north, Western Ghats to the west, Kanyakumari District to the south, and Thoothukudi District to the east. The study area encompasses a total land area of 6,823 km2 and is covered at longitudes 77°05′ to 78°25′ east and latitudes 8°05′ to 9°30′ north.

The study area has a peculiar climate, which receives 514 mm of precipitation annually and also has temperatures that range from 23 to 38.56°C. The land has been cultivated throughout all three seasons by farmers. To produce more yield, it is essential to prioritize the mapping of groundwater-suitable zones to preserve the ecology and depth of groundwater in the research region. Figure 1 shows the study area region.

Figure 1 Study area.
Figure 1

Study area.

3 Materials and methods

To conduct this study, we collected the necessary thematic maps from multiple sources, digitized them, and pre-processed them using GIS tools. The primary and secondary sources of data are detailed in Table 1. Eleven geographical characteristics include geomorphology, lineament density, lithology, slope, soil, land use and land cover, drainage density, land surface temperature, NDVI, rainfall, and TWI thematic maps were prepared for this study. Using AHP and MIF, the 11 thematic layers have been merged as influencing variables to assess the potential zones for groundwater in the research region. To create thematic maps to show groundwater potential zone (GWPZ) area, we used the spatial analysis tool in ArcGIS 10.8. The raster format was applied to each theme layer once they were transformed. The next step was to assign a weight to each item and each sub-class of the topics using one of two distinct AHP and MIF algorithms. The result was calculated using the spatial analysis tool, which considered all the weights. Kappa statics could properly outline the GWPZ maps, thanks to their ability to monitor the correctness of two GWPZ maps. To determine which approaches are most effective for mapping potential groundwater zones, we will employ AHP and MIF methodologies.

Table 1

Data used to identify probable groundwater zones

Data type Parameters Data source Source location Extracted final layer
Conventional and attribute data Geomorphology Bhuvan-ISRO https://bhuvan.nrsc.gov.in Geomorphology map
Geo-portal NRSC, Hyderabad
Lithology 1:50,000 Lithology map
Soil type Topo-sheets Survey of India Soil map
1:50,000
Rainfall In millimetres http://indiawris.gov.in/wris/#/India WRIS Rainfall map
WebGIS Portal
Remotely sensed spatial data Linement density Bhuvan-ISRO https://bhuvan.nrsc.gov.in Lineament density map
Drainage density Geo-portal Drainage density map
Slope 1:50,000 NRSC, Hyderabad Slope map
TWI TWI map
Land surface temperature Landsat 5 USGS EarthExplorer Land surface temperature map
Land use and land cover SRTM DEM https://earthexplorer.usgs.gov/ LULC map
NDVI In meters NDVI map

4 Factors influencing groundwater potentiality

4.1 Geomorphology

The geomorphology has a significant impact on how water seeps into the ground and recharges underground aquifers [8]. The study region contains geographical features such as Dissected Hills and Valleys, Aeolian Sand Dunes, Waterbodies, Pediplain, and Bajada. Due to Dissected Hills being located towards the west direction and having more significant runoff, the groundwater is low in level. However, infiltration is possible due to the cracks in the denudational hills having a gradual slope and modest plant cover. Pediplains are commonly there in the study area with a moderate slope, abundant flora, and great groundwater potential. Bajada zones are sediment deposits found along stream channels that have high potential. In this study region, a high rating is given to Pediplain, followed by Bajada, Denudational Hills, and Dissected Hills, which receive a lower rating. The prepared geomorphology thematic map is shown in Figure 2(a).

Figure 2 (a) Geomorphology, (b) lineament density, (c) lithology, (d) slope, (e) soil, (f) land use land cover, (g) drainage density, (h) NDVI, (i) land surface, (j) rainfall, and (k) TWI of the study area.
Figure 2

(a) Geomorphology, (b) lineament density, (c) lithology, (d) slope, (e) soil, (f) land use land cover, (g) drainage density, (h) NDVI, (i) land surface, (j) rainfall, and (k) TWI of the study area.

4.2 Lineament density

The lineament density on the earth’s surface has a significant impact on groundwater recharge because of cracks in the ground that help to enable water to seep through. These regions with distinct features have a higher capacity to absorb water, leading to increased groundwater storage. The prepared lineament density thematic map is shown in Figure 2(b). From the observation, the study area's western and northwestern regions have the highest lineament density. From the study area, the lineament density is identified 240.68 km2 in 3.44% as Very High, 660.16 km2 in 9.43% as High, 2092.48 km2 in 29.91% as Medium, 2730.50 km2 in 39.03% as Low, and 1248.29 km2 in 29.91% as Very Low.

4.3 Lithology

Lithology influences groundwater potential directly. It impacts rock permeability and porosity [9]. Chamockite, Khondalite, Migmatities, and Alluvium are available in the study area, as shown in Figure 2(c). Around 70% of the total area is covered with migmatite followed by Chamockite and Khondalite. Migmatite spread over and almost central part of the study area.

4.4 Slope

The slope is an important factor that directly impacts the rate at which water can be absorbed into the ground. Usually, the steeper slope slower the infiltration rate, and flat areas with a gentle slope are best for recharging because they keep water there for a long time. In contrast, steep slopes reduce the infiltration rate by increasing runoff. Due to the region’s rolling landscape, slope gradients vary throughout. There are five categories of slope range identified in the study area such as <5°, 5°–10°, 10°–20°, 20°–30°, and >30°. Areas with a slope of <5° are suitable for recharge as they reduce runoff and facilitate recharge. However, locations with a slope of >20° are less favourable for recharge. Thematic slope map of the study area is shown in Figure 2(d).

4.5 Soil types

As a consequence of the weathering process in the soil, the infiltration rate depends on the soil’s permeability and water-holding capability. Since soil properties govern water-holding capacity and are ranked according to their compositional and water-retention capabilities, they are crucial in defining groundwater potential. The soil in the study region divulges six main soil classes: clayey soil (48.5%), gravelly clayey soil (14.8%), loamy soil (10%), gravelly loam soil (12.4%), rock land (11.1%), and sandy soil (1.1%), and the thematic map is shown in Figure 2(e). Based on their infiltration rate levels, soil type that is ranked as loamy soil is prioritized due to its high infiltration, while sandy soil is given a lower priority because of its low infiltration.

4.6 Land use and land cover

Land use and land cover changes are the most critical factors that affect groundwater recharge [10]. Agricultural land, built-up land, forest, wasteland, and water bodies are the most common forms of land use and land cover. In this, the majority of the area’s land is used for farming and depends on groundwater including water bodies and vegetated areas. Waterbodies store abundant surface water during the monsoon season and aid in infiltration year-round, and also, vegetation cover enhances the rate of infiltration by decreasing runoff. Agricultural land is reasonably favourable for groundwater recharge; however, the pace varies by field type. Due to their potential to accelerate recharge, greater weight has been attributed to water bodies and vegetation land in this study. So, the forest land, agricultural land, and water bodies are given substantial weight as the built-up land, wasteland, and rocky terrain are given a low weight. The water bodies cover 6% of the study area, wasteland 8.6%, forest 18.4%, built-up land 3.5%, and agricultural land 59.8%. Figure 2(f) shows the thematic land use and land cover map of the study area.

4.7 Drainage density

The term drainage density refers to the proportion of the stream’s length to the region’s total area [11]. By the way, a drainage network is shaped by the rock structures, how water flows through, and how it increases in a particular area, which can give details about how quickly water is absorbed [12]. The following equation is used to estimate the drainage density:

Dd = Li/ A ,

where Dd is the drainage density, Li is the total length of the water courses in km, and A is the surface area of the basin in km2.

From the observation, drainage density has been categorized as follows: 0–22 (Very Low), 22–45 (Low), 45–67 (Medium), 67–90 (High), and 90–112 (Very High). Groundwater zones are predicted based on drainage density and infiltration rates. High infiltration and little runoff are given more weight, while high surface runoff and little infiltration are given less weight. High drainage density occurs by impermeable rock, while low density occurs by a porous foundation. Figure 2(g) shows the drainage density map of the research area.

4.8 Land surface temperature

In this study, the ground surface temperature has been calculated from 12 to 34°C in the study area. It has been identified that the North and South-East regions of the study area have the most extraordinary surface temperature, which is estimated to range from 30 to 34°C. This is due mainly to the agricultural barren land and the lack of vegetation in those areas. The central-north and central-south areas of the study have been estimated to have a moderate surface temperature range from 25 to 29°C. Because of the forest and the thick vegetation towards the west, the surface temperatures were recorded to be lower (between 12 and 20°C).

Due to the soil’s larger thermal capacity and also wet condition, the groundwater potential is modest in range. Because of the lowest surface temperature in water bodies and marshy areas, these areas have an enormous groundwater potential [13]. The geographical spread of the surface temperature, as processed from Landsat imagery, can be seen in Figure 2(i).

4.9 NDVI

Healthy plants require water in the spaces between soil particles to thrive, while a lack of water will cause plant deterioration. An increase in the NDVI value indicates that a higher potential groundwater is available on the ground surface [14]. The NDVI value for the measured region ranges between 1 and 50%, as shown in the thematic map (Figure 2(h)). The NDVI was obtained from Sentinel imagery using the following equation:

NDVI = NIR – RED/NIR + RED .

4.10 Rainfall

In the hydrological cycle, rainfall is the primary source of water and plays a crucial role in groundwater formation in a given area. The rainfall data collected in the year 2021 have been utilized for this research to create the rainfall thematic map. From the data, the yearly precipitation in the study area has been identified to range from 239 to 1,662 mm.

Using the IDW interpolation technique, the geographical distribution of rainfall data has been processed and shown in the map. The amount of rainfall has been classified into five categories based on distributed rainfall values using the reclassifying technique in the GIS Software. These categories include Very Low (239–245 mm), Low (524–809 mm), Moderate (809–1,092 mm), High (1,092–1,376 mm), and Very High (1,376–1,662 mm) rainfall. Rainfall data are high in intensity and short in duration resulting in less water seeping into the ground and more water flowing on the surface. On the other hand, rainfall that is low in intensity and long in duration has the opposite effect [15]. Figure 2(j) shows the Rainfall thematic map of the study area.

4.11 TWI

The TWI, also referred to as TWI, is commonly utilized to calculate the influence of topography on hydrological processes. It indicates the groundwater potential infiltration resulting from topographic effects [15]. The TWI in the study area ranged from −8.22 to 12.15 level. The values were reclassified into five categories such as −8.22 to −4.15 (Very Low), −4.15 to −0.07 (Low), −0.0.07 to 3.99 (Medium), 3.99 to 8.07 (High), and 8.07 to 12.15 (Very High). Figure 2(k) shows the TWI Thematic map of the study area.

5 AHP

The AHP method achieves accurate results and relies on using the appropriate weight calculation techniques for the thematic layers. A pair-wise comparison matrix method serves as the foundation for this AHP approach. After reviewing the literature, a basic scaling technique was used to assign rankings for the individual layers based on the degree of significance. The degree of significance of the thematic layers is classified into nine scales and shown in Table 2. By using this scaling method, a pair-wise comparison matrix was generated as shown in Table 3, for the 11 thematic layers. A normalized pair-wise comparison matrix generated from the pair-wise comparison matrix is formulated by dividing individual factor's weights with respect to their total weight. The normalized pair-wise comparison matrix is shown in Table 4. Using the following formula:

X i j = X i mat / Σ X i ,

where X ij is the normalized value of the ith row and jth column, X i mat is the value of each cell in each theme’s matrix, and ΣX i is the total value of each column. Table 3 displays the normalized weights of each topic, which were calculated using the following formula:

W i = Σ X i j / N ,

where W i is the average weight, the normalized value of the ith row and the jth column is X ij , and the number of things that affect it is N.

Table 2

Basic scale of the pair-wise comparison

The degree of significance Definitions
Very little significant 1/9
1/8
very less significant 1/7
1/6
Much less significant 1/5
1/4
comparatively less significant 1/3
1/2
Equal significance 1
Somewhat significant 2
3
Strongly significant 4
5
Very strongly significant 6
7
Extremely significant 8
9
Table 3

Pair-wise comparison matrix

Factors Geomorphology Lineament density Lithology Slope Soil LULC Drainage density NDVI LST TWI Rainfall
Geomorphology 1.00 5.00 0.50 2 6 3 4 4 3 5 8
Lineament density 0.20 1.00 0.33 0.50 1 5 1 5 3 3 3
Lithology 2 3 1.00 2 3 4 6 7 5 3 9
Slope 0.50 2.00 0.50 1.00 6 3 3 3 4 5 8
Soil 0.17 1.00 0.33 0.17 1.00 5 4 4 6 5 3
LULC 0.33 0.20 0.25 0.33 0.20 1.00 1 1 2 1 7
Drainage density 0.25 1.00 0.17 0.33 0.25 1 1.00 1 1 3 6
NDVI 0.25 0.20 0.14 0.33 0.25 1 1 1.00 1 1 6
Land surface temperature 0.33 0.33 0.20 0.25 0.17 0.50 1.00 1 1.00 2 2
TWI 0.20 0.33 0.33 0.20 0.20 1 0.33 1 0.5 1.00 2
Rainfall 0.13 0.33 0.11 0.13 0.33 0.14 0.17 0.17 0.50 0.5 1.00
Total 5.36 14.40 3.87 7.24 18.40 24.64 22.50 28.17 27.00 29.50 55.00
Table 4

Normalized pair-wise comparison matrix

Factors Geomorphology Lineament density Lithology Slope Soil LULC Drainage density NDVI LST TWI Rainfall Criteria weight
Geomorphology 0.19 0.35 0.13 0.28 0.33 0.12 0.18 0.14 0.11 0.17 0.15 0.19
Lineament density 0.04 0.07 0.09 0.07 0.05 0.20 0.04 0.18 0.11 0.10 0.05 0.09
Lithology 0.37 0.21 0.26 0.28 0.16 0.16 0.27 0.25 0.19 0.10 0.16 0.22
Slope 0.09 0.14 0.13 0.14 0.33 0.12 0.13 0.11 0.15 0.17 0.15 0.15
Soil 0.03 0.07 0.09 0.02 0.05 0.20 0.18 0.14 0.22 0.17 0.05 0.11
LULC 0.06 0.01 0.06 0.05 0.01 0.04 0.04 0.04 0.07 0.03 0.13 0.05
Drainage density 0.05 0.07 0.04 0.05 0.01 0.04 0.04 0.04 0.04 0.10 0.11 0.05
NDVI 0.05 0.01 0.04 0.05 0.01 0.04 0.04 0.04 0.04 0.03 0.11 0.04
LST 0.06 0.02 0.05 0.03 0.01 0.02 0.04 0.04 0.04 0.07 0.04 0.04
TWI 0.04 0.02 0.09 0.03 0.01 0.04 0.01 0.04 0.02 0.03 0.04 0.03
Rainfall 0.02 0.02 0.03 0.02 0.02 0.01 0.01 0.01 0.02 0.02 0.02 0.02

In the AHP approach, calculating the consistency ratio is an important component in evaluating the method's accuracy. Satty claims that the AHP approach is best adapted if the consistency value is less than 10%. The consistency index has been calculated using the following inferred formula:

CI = λ max − N / N – 1 ,

where N is the number of observations and λ max denotes the maximum eigenvalue of the comparison matrix.

The consistency ratio has been calculated using the following formula:

Consistency ratio = CI/RI,

where CI is the consistency index, RI is the random inconsistency. In general, the consistency ratio should be at least be equal to 0.1 but no higher. In this study, the CI value is 0.1491, and the consistency ratio is 0.09. The computed CR score of 0.09 indicates that a reasonable degree of consistency served as the foundation for the weighting of the criterion. Figure 3 shows the flow diagram representing the process of creating and verifying GWPZ maps (AHP).

Figure 3 Flow diagram represents the process of creating and verifying GWPZ maps.
Figure 3

Flow diagram represents the process of creating and verifying GWPZ maps.

The finalized normalized weight for the individual factors of all 11 thematic layers was calculated and shown in Table 5. Each factor's normalized weight is assigned in the thematic map, which is processed using GIS software. The overall weights are calculated by adding the individual locations in the thematic layers. Finally, the GWPZ map was generated from the 11 thematic layers using the overlay method in the GIS Software.

Table 5

GWPZ parameter classification for conducting a weighted overlay analysis

Parameter Factors Weight Assigned ranking Normalized weight
Geomorphology Aeolian sand dune 0.19 3 0.57
Anthropogenic terrain 2 0.38
Bajada 1 0.19
Coastal plain 6 1.14
Dam and reservoir 7 1.33
Flood plain 7 1.33
Highly dissected hills and valleys 6 1.14
Low-dissected hills and valleys 4 0.76
Moderately dissected hills and valleys 5 0.95
Pediment pediplain complex 6 1.14
Quarry and mine dump 5 0.95
Waterbodies-other 7 1.33
Waterbody – river 7 1.33
Lineament density Very high 0.09 5 0.45
High 4 0.36
Medium 3 0.27
Low 2 0.18
Very low 1 0.09
Lithology Chamockite 0.22 1 0.22
Khondalite 2 0.44
Migmatites 3 0.66
Alluvium 4 0.88
Slope 0–5 0.15 5 0.75
5–10 4 0.6
10–20 3 0.45
20–30 2 0.3
>30 1 0.15
Soil Clayey soil 0.11 3 0.33
Gravelly clay soil 2 0.22
Loamy soil 6 0.66
Gravelly loam soil 5 0.55
Rock land 1 0.11
Sandy soil 4 0.44
LULC Agri land 0.05 4 0.2
Built up land 1 0.05
Forest 3 0.15
Waste land 2 0.1
Water 5 0.25
Drainage density Very high 0.05 1 0.05
High 2 0.1
Medium 3 0.15
Low 4 0.2
Very low 5 0.25
LST Very high 0.04 1 0.04
High 2 0.08
Medium 3 0.12
Low 4 0.16
Very low 5 0.2
NDVI Very high 0.04 5 0.2
High 4 0.16
Medium 3 0.12
Low 2 0.08
Very low 1 0.04
Rainfall Very high 0.02 5 0.1
High 4 0.08
Medium 3 0.06
Low 2 0.04
Very low 1 0.02
TWI Very high 0.03 1 0.03
High 2 0.06
Medium 3 0.09
Low 4 0.12
Very low 5 0.15

The GWPZ map shown in Figure 5(a) was generated using the AHP approach. The generated GWPZ map is classified into five categories, Very Low, Low, Medium, High, and Very High, based on their weights using the normal distribution method.

6 MIF

To rank each sub-class of factors, we used the MIF methodology to estimate the statistical weights of each component. In this study, expertise ideas and a survey of pertinent literature were used to establish the interaction between the several factor classes and assign rankings to the various factor sub-classes. Factors with a significant influence were labelled as having a considerable effect and weighed to 1.0. On the other hand, minor impacts were labelled as having a minor effect and given a weight of 0.5. The calculated weights are shown in Table 6. Table 7 shows the rates of each factor, calculated by adding up both major and minor effects. It also includes the proposed score calculation for each influencing factor, determined using a specific formula

Proposed score ( Pi )  =  ( A + B ) / sum ( A + B ) × 100 .

where A stands for a factor’s major effect, whereas B stands for a factor’s minor effect. Pi also denotes the weight of each particular parameter. On the contrary, each influencing factor’s subclasses must be given weight. The factor’s weight has now been given to the sub-class with the most impact. After that, the following method was used to determine the second-highest significant sub-class. For the most influencing subclass

Si 1 = Pi,

where Si1 denotes the most influential sub-class, and Pi denotes the factor weight.

Table 6

Recommended relative rates, major and minor effects of the influencing elements, and the corresponding score [3]

Influencing factor Major effect (A) Minor effect (B) Proposed relative rates (A + B) Proposed score of each influencing factor ( A + B ) / sum ( A + B ) × 100
Geomorphology 6 0.5 6.5 27
Lineament density 2 0.5 2.5 10
Lithology 4 0.5 4.5 19
Slope 2 0.5 2.5 10
Soil 1 0 1 4
LULC 0 0.5 0.5 2
Drainage density 1 0.5 1.5 6
NDVI 2 0.5 2.5 10
Land surface temperature 0 0.5 0.5 2
TWI 0 0.5 0.5 2
Rainfall 1 0.5 1.5 6
Table 7

Thematic layers with factors, its weightage, assigned ranking and normalized weight

Parameter Factors Weight Assigned ranking Normalized weight
Geomorphology Aeolian sand dune 27 3 81
Anthropogenic terrain 2 54
Bajada 1 27
Coastal plain 6 162
Dam and reservoir 7 189
Flood plain 7 189
Highly dissected hills and valleys 6 162
Low-dissected hills and valleys 4 108
Moderately dissected hills and valleys 5 135
Pediment pediplain complex 6 162
Quarry and mine dump 5 135
Waterbodies – other 7 189
Waterbody – river 7 189
Lineament density Very High 10 5 50
High 4 40
Medium 3 30
Low 2 20
Very Low 1 10
Lithology Chamockite 19 1 19
Khondalite 2 38
Migmatites 3 57
Alluvium 4 76
Slope 0–5 10 5 50
5–10 4 40
10–20 3 30
20–30 2 20
>30 1 10
Soil Clayey soil 4 3 12
Gravelly clay soil 2 8
Loamy soil 6 24
Gravelly loam soil 5 20
Rock land 1 4
Sandy soil 4 16
LULC Agri land 2 4 8
Built up land 1 2
Forest 3 6
Waste land 2 4
Water 5 10
Drainage density Very High 6 1 6
High 2 12
Medium 3 18
Low 4 24
Very Low 5 30
LST Very High 2 1 2
High 2 4
Medium 3 6
Low 4 8
Very Low 5 10
NDVI Very High 10 5 50
High 4 40
Medium 3 30
Low 2 20
Very Low 1 10
Rainfall Very High 6 5 30
High 4 24
Medium 3 18
Low 2 12
Very Low 1 6
TWI Very High 2 1 2
High 2 4
Medium 3 6
Low 4 8
Very Low 5 10

For the following most influencing subclass:

Si 2 = Si 1 − ( Pi / n ) ,

where Si2 refers to the next most influencing sub-class, Si1 refers to the most influencing sub-class, and Pi refers to the factor weight. The weights of each thematic layer’s subclasses have been computed and shown in Table 7 per this pattern.

MIF is mainly based on how the theme factors relate and depend on each other. Because of this, the idea of multicollinearity has come up. In the same way, the combination is not a problem with multilinear regression. Instead, multicollinearity helps us make better regional GWPZs. Figure 4 shows the relationship and dependence between various factors that contribute to GWPZs (MIF process).

Figure 4 The relationship and dependence between various factors that contribute to GWPZs.
Figure 4

The relationship and dependence between various factors that contribute to GWPZs.

7 GWPZs

GIS was used to create GWPZ maps using the MIF and AHP methods. The processed GWPZ maps are shown as thematic maps in Figure 5(a) and (b). First, we classified the thematic layers with weighted values before making the maps. Then, we combined all of the theme-weighted layers using the raster calculator.

Figure 5 GWPZ map ((a) AHP and (b) MIF methods).
Figure 5

GWPZ map ((a) AHP and (b) MIF methods).

According to Table 8, the MIF method showed that the tested area was divided into GWPZs of 3.16, 0.33, 2.14, 61.21, and 33.16%. On the other hand, the AHP method revealed that 3.57, 0.55, 6.62, 58.09, and 31.21% of the area fell inside the Very Low, Low, Medium, High, and Very High GWPZ, respectively (Table 8).

Table 8

GWPZ coverage by area

GWPZ coverage Area (%)
MIF AHP
Very Low 3.16 3.57
Low 0.33 0.55
Medium 2.14 6.62
High 61.21 58.09
Very High 33.16 31.21

8 Accuracy assessment

Eighty-four wells of water depth data obtained from the State Ground and Surface Water Resources Data Centre were used to evaluate the accuracy of maps produced in the study region. The depths of the wells ranged from 30 to 300 ft and were categorized as Very Low (>200 ft), Low (150–200 ft), Medium (100–150 ft), High (50–100 ft), and Very High (<50 ft).

The accuracy of the GWPZ maps, which were produced using MIF and AHP approaches, is shown in Table 9. Now, Kappa statistics were computed to validate it.

Table 9

Accuracy assessment of the GWPZ maps delineated with MIF and AHP techniques

S. No. Well No. Latitude Longitude Actual water depth of drilled borehole (ft) Actual depth remark Expected depth predicted from the map (AHP) Agreement between actual and expected (AHP) Expected depth predicted from the map (MIF) Agreement between actual and expected (MIF)
1 1 8.191667 77.748056 >200 Very Low Very Low Agree Very Low Agree
2 2 8.308056 77.763889 >200 Very Low Very Low Agree Very Low Agree
3 3 8.175556 77.575556 >200 Very Low Very Low Agree Very Low Agree
4 4 8.175556 77.575556 >200 Very Low Very Low Agree Very Low Agree
5 5 8.798056 77.802778 >200 Very Low Very Low Agree Very Low Agree
6 6 8.772778 77.804167 >200 Very Low Medium Disagree Medium Disagree
7 7 8.365833 77.807778 >200 Very Low Very Low Agree Very Low Agree
8 8 8.866667 77.800000 >200 Very Low Very Low Agree Very Low Agree
9 9 8.856944 77.780556 >200 Very Low Medium Disagree Very High Disagree
10 10 8.641667 77.779722 >200 Very Low Very Low Agree Very Low Agree
11 11 8.262778 77.679444 150–200 Low Low Agree Low Agree
12 12 8.716667 77.550000 150–200 Low Low Agree Low Agree
13 13 8.720833 77.733333 150–200 Low Low Agree Low Agree
14 14 8.712500 77.605000 150–200 Low Low Agree Low Agree
15 15 8.426944 77.790833 150–200 Low Low Agree Low Agree
16 16 8.700000 77.600000 150–200 Low Medium Disagree High Disagree
17 17 8.338056 77.693611 150–200 Low Low Agree Low Agree
18 18 8.728333 77.668056 150–200 Low Low Agree Low Agree
19 19 8.525000 77.790278 150–200 Low Low Agree Low Agree
20 20 8.811111 77.730556 150–200 Low Low Agree Low Agree
21 21 8.545833 77.770000 150–200 Low Low Agree Low Agree
22 22 8.640278 77.705556 150–200 Low Low Agree Low Agree
23 23 8.745833 77.500000 150–200 Low Low Agree Low Agree
24 24 8.618611 77.643611 100–150 Medium Medium Agree Medium Agree
25 25 8.766667 77.675000 100–150 Medium Medium Agree Medium Agree
26 26 8.425833 77.731111 100–150 Medium Medium Agree Medium Agree
27 27 8.428333 77.727778 100–150 Medium Medium Agree Medium Agree
28 28 8.776389 77.675000 100–150 Medium Medium Agree Medium Agree
29 29 8.666667 77.585278 100–150 Medium Very high Disagree High Disagree
30 30 8.683333 77.514444 100–150 Medium Medium Agree Medium Agree
31 31 8.808333 77.483333 100–150 Medium Medium Agree Medium Agree
32 32 8.776944 77.518611 100–150 Medium Very High Disagree Medium Agree
33 33 8.727222 77.431389 100–150 Medium Medium Agree Medium Agree
34 34 8.541667 77.716667 100–150 Medium Medium Agree Medium Agree
35 35 8.724722 77.433333 100–150 Medium Medium Agree Medium Agree
36 36 8.783333 77.600000 100–150 Medium Very Low Disagree Very Low Disagree
37 37 8.816667 77.716667 100–150 Medium Medium Agree Medium Agree
38 38 8.650556 77.434167 100–150 Medium Medium Agree Medium Agree
39 39 8.929167 77.625000 100–150 Medium Medium Agree Medium Agree
40 40 8.713056 77.364722 100–150 Medium Very High Disagree High Disagree
41 41 8.266667 77.569167 100–150 Medium Medium Agree Medium Agree
42 42 8.456389 77.618611 100–150 Medium Medium Agree Medium Agree
43 43 8.316667 77.579722 100–150 Medium Medium Agree Medium Agree
44 44 8.797778 77.425000 100–150 Medium Medium Agree Medium Agree
45 45 8.490833 77.658889 100–150 Medium Medium Agree Medium Agree
46 46 9.259167 77.682778 50–100 High High Agree High Agree
47 47 8.866667 77.600000 50–100 High High Agree High Agree
48 48 9.083333 77.666667 50–100 High Very High Disagree Very High Disagree
49 49 8.379167 77.608889 50–100 High High Agree High Agree
50 50 8.995278 77.625000 50–100 High High Agree High Agree
51 51 8.820833 77.370833 50–100 High High Agree High Agree
52 52 8.541111 77.571667 50–100 High High Agree High Agree
53 53 9.215278 77.675000 50–100 High High Agree High Agree
54 54 8.935278 77.500000 50–100 High Very High Disagree High Agree
55 55 8.935833 77.497222 50–100 High High Agree High Agree
56 56 8.935556 77.450000 50–100 High High Agree High Agree
57 57 9.006944 77.513889 50–100 High High Agree High Agree
58 58 9.107778 77.584444 50–100 High High Agree High Agree
59 59 9.318056 77.552778 50–100 High Low Disagree High Agree
60 60 9.349167 77.558611 50–100 High High Agree High Agree
61 61 9.146111 77.602778 50–100 High High Agree High Agree
62 62 8.944722 77.386667 50–100 High High Agree High Agree
63 63 8.880278 77.456111 50–100 High High Agree High Agree
64 64 9.157778 77.609167 <50 Very High Very High Agree Very High Agree
65 65 9.250000 77.500000 <50 Very High Very High Agree Very High Agree
66 66 9.005556 77.433333 <50 Very High Very High Agree Very High Agree
67 67 8.880000 77.343333 <50 Very High Very High Agree Very High Agree
68 68 9.038889 77.391667 <50 Very High Very High Agree Very High Agree
69 69 9.099167 77.491944 <50 Very High Very High Agree Very High Agree
70 70 9.027778 77.388889 <50 Very High Very High Agree Very High Agree
71 71 9.073611 77.551389 <50 Very High Very High Agree Very High Agree
72 72 9.176944 77.535000 <50 Very High Very High Agree Very High Agree
73 73 8.932500 77.345000 <50 Very High Very High Agree Very High Agree
74 74 8.941111 77.271667 <50 Very High Very High Agree Very High Agree
75 75 8.941111 77.273611 <50 Very High Very High Agree Very High Agree
76 76 9.026389 77.326389 <50 Very High High Disagree Very Low Disagree
77 77 9.174444 77.453333 <50 Very High Very High Agree Very High Agree
78 78 9.183333 77.450000 <50 Very High Very High Agree Very High Agree
79 79 9.233333 77.416667 <50 Very High Very High Agree Very High Agree
80 80 9.076389 77.400000 <50 Very High Very High Agree Very High Agree
81 81 9.016667 77.300000 <50 Very High Very High Agree Very High Agree
82 82 9.016667 77.300000 <50 Very High Very High Agree Very High Agree
83 83 9.020833 77.198611 <50 Very High Very High Agree Very High Agree
84 84 9.079167 77.345833 <50 Very High Very High Agree Very High Agree

The kappa statistic considers the chance agreement when two measurements (actual kappa statistics are zero [16]. When two measurements are in agreement, the value of kappa is 1.0. To calculate the kappa statistics value, use the following formula:

K = Observed agreement − Chance agreement 1 − Chance agreement .

From Tables 10 and 11, it was found that the level of understanding of kappa statistics for the GWPZ map generated by the AHP method is 0.71690, while for the map produced by the MIF technique, it is 0.77054. However, it is generally accepted that a measure of agreement above 0.7 is required for validation. Both methods produced favourable results for the GWPZ map, with the MIF approach achieving a perfect score of 0.77054 and the AHP method also making a measurement deemed appropriate. Overall, the MIF technique proved to be more suitable for mapping GWPZs in the test area.

Table 10

Kappa statistics result for GWPZ obtained with MIF technique

Symmetric measures
Value Asymptotic standard error Approximate T Approximate significance
Measure of agreement N of valid cases Kappa 0.8790 0.071 8.041 0.000
Table 11

Kappa statistics result for GWPZ obtained with AHP technique

Symmetric measures
Value Asymptotic standard error Approximate T Approximate significance
Measure of agreement N of valid cases Kappa 0.8326 0.078 7.825 0.000

9 Conclusion

Countries such as India, which are still in the process of development, have significant challenges due to the immense strain their population puts on their natural resources. Hence, in order to meet the water demand, it is imperative to utilize groundwater in a sustainable manner through meticulous planning. With the advent of RS and GIS, it is now feasible to manage and process data of considerable magnitude. Furthermore, we have the capability to incorporate all the crucial variables and generate a map of GWPZs using RS and GIS technology. By conducting accuracy assessments, we can obtain exceptional results. The eastern portion of the test area lacks groundwater enrichment, whereas the western and central portions exhibit significant potential for groundwater. However, it is in the western and central parts where groundwater is primarily utilized for agricultural purposes. Alternatively, in order to sustain the agricultural potential of the southern region of the test area, it is imperative to enhance the utilization of groundwater for irrigation, including methods such as river raising and irrigation from ponds and tanks.

To properly plan and efficiently manage subsurface hydrological resources, mapping the potential groundwater zones is essential. The research tries to use two different statistical methods, namely MIF and AHP; yet the results are pretty close to being the same. However, the precision analysis reveals a marginal disparity between these results. The actual depth of the water level in the tube well at various locations across the test area was used to determine the correctness of the assessment. The kappa coefficient was used for the data to determine the degree to which the two models were accurate, and the results showed that MIF was more accurate than AHP method. The MIF approach is based on the interaction and interdependence of chosen groundwater-affecting characteristics. However, the AHP technique is purely dependent on the evaluation of literature and the advice of experts; as a result, there is always the possibility of human error being present here. As a result, we can conclude that the MIF statistical approach is more appropriate than the AHP method in this study area.

tel: +91 9994302867

  1. Funding information: No funding was obtained for this study.

  2. Author contributions: Samuel Prabaharan Jebaraj – preparation WOA, dataset processing, and remote sensed data analysis. Viji Rajagopal – geological data analysis.

  3. Conflict of interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this article.

  4. Ethics approval and consent to participate: The authors declare that they are ready to give ethics approval and consent to participate in this research work.

  5. Ethical responsibilities of authors: All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors.

  6. Data availability statement: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

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Received: 2023-11-02
Revised: 2024-02-05
Accepted: 2024-02-25
Published Online: 2024-05-23

© 2024 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  144. Optimizing agricultural land use: A GIS-based assessment of suitability in the Sana River Basin, Bosnia and Herzegovina
  145. Assessing risk-prone areas in the Kratovska Reka catchment (North Macedonia) by integrating advanced geospatial analytics and flash flood potential index
  146. Analysis of the intensity of erosive processes and state of vegetation cover in the zone of influence of the Kolubara Mining Basin
  147. GIS-based spatial modeling of landslide susceptibility using BWM-LSI: A case study – city of Smederevo (Serbia)
  148. Geospatial modeling of wildfire susceptibility on a national scale in Montenegro: A comparative evaluation of F-AHP and FR methodologies
  149. Geosite assessment as the first step for the development of canyoning activities in North Montenegro
  150. Urban geoheritage and degradation risk assessment of the Sokograd fortress (Sokobanja, Eastern Serbia)
  151. Multi-hazard modeling of erosion and landslide susceptibility at the national scale in the example of North Macedonia
  152. Understanding seismic hazard resilience in Montenegro: A qualitative analysis of community preparedness and response capabilities
  153. Forest soil CO2 emission in Quercus robur level II monitoring site
  154. Characterization of glomalin proteins in soil: A potential indicator of erosion intensity
  155. Power of Terroir: Case study of Grašac at the Fruška Gora wine region (North Serbia)
  156. Special Issue: Geospatial and Environmental Dynamics - Part I
  157. Qualitative insights into cultural heritage protection in Serbia: Addressing legal and institutional gaps for disaster risk resilience
https://doi.org/10.1515/geo-2022-0619
Keywords for this article
remote sensing; GIS; groundwater potential zones; data statistical analysis; AHP; MIF; geospatial mapping
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