Underlying lithology, soil properties, lineaments, and drainage densities are principally responsible for controlling the occurrence and flow of groundwater(Jhariya, Mondal, et al. 2021). While recharge is regulated by rainfall, land use & land cover type(Jhariya, Mondal, et al. 2021). The current study developed and combined a total of twelve thematic layers like (slope, rainfall, curvature, soil, drainage density, lineament density, temperature of the ground surface, the topographic moisture index, elevation, land use & land cover, lithology, and groundwater fluctuation) using ArcGIS 10.3 software(Jhariya, Mondal, et al. 2021).
3.1 Data collection and processing in geographic information system (GIS)
The twelve thematic maps a GIS platform was used to create the data necessary to make the GWPZs map (ArcMap 10.3 software) (Jhariya, Mondal, et al. 2021). The Curvature, slope, Drainage density, Topographic wetness index and Elevation thematic maps were created using the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (Dem) with a 30 m spatial resolution (Dingle et al. 2018; Stewart 2017). The soil map and the Lithology map of the research area were extracted FAO Indian Soil map and USGS world geologic maps respectively(Thilagavathi et al. 2015). Lineaments were manually produced in the study region from the spatial analyst tool utilizing the equation below(Jhariya, Mondal, et al. 2021; Dingle et al. 2018; Stewart 2017).
\(\text{L}\text{d}={\sum }_{i=1}^{n}\text{L}\text{i}/\text{A}\) -1
Where Li is the length each lineament and A is the study area to be considered.
Land sat 8 data was used to create the LULC map of the research region, which was created using supervised classification of a composite of the band 5, 6, and 4 in GIS software (Jhariya, Mondal, et al. 2021). To assess the spatial rainfall pattern of the research region, the average annual rainfall over a 10-year period was calculated using information from the Andhra Pradesh State Development and Planning Society (APSDPS)(Jhariya, Mondal, et al. 2021). For Land surface Temperature, data from USGS earth Explorer Land sat 8 collected and it is processed. Groundwater depth data was collected from India Water Resources Information System. All the shape files and non-spatial data by using the analytic hierarchy approach, maps were converted to raster format and the proper weights were assigned in order of their hierarchy in groundwater potentiality (AHP) (Jhariya, Mondal, et al. 2021).
3.2 Role of GIS and Analytic Hierarchy process
For ranking the numerous factors taken into consideration in this study, a combination of the Analytical Hierarchy Process (AHP) approach and Multi-Criteria Decision-Making Analysis (MCDM) is used (Sivakumar, Radha Krishnappa, and Nallanathel 2021). The weighted layers are then statistically evaluated using GIS to create the drought vulnerability assessment map. The analytical hierarchy process (AHP) and geographic information system (GIS) work well together to monitor groundwater, map disasters like landslides, floods, and droughts, and even determine if a piece of land is suitable for farming. AHP is employed to resolve a variety of issues where decision-making based on a number of factors necessitates weighting of the parameters based on their significance for the given condition on a pair-wise comparison means for each of the parameters under consideration investigation. Consequently, in the AHP, the grading is done in accordance with Saaty's idea and scored from 1 to 9, where 1 means the least contribution and 9 represents the most contribution, as shown in Table 1. (Jhariya, Mondal, et al. 2021; Sivakumar, Radha Krishnappa, and Nallanathel 2021). A pairwise comparison matrix is also included in the table 2(Jhariya, Mondal, et al. 2021).
Table 2 Random index (RI)(Penki, Basina, and Tanniru 2022; Ramu, Sai Santosh, and Chalapathi 2022; Ramu et al. 2020; Ravinder, Ramu, and Srinivasarao 2020)
N
|
1
|
2
|
3
|
4
|
5
|
6
|
7
|
8
|
9
|
10
|
11
|
12
|
RI
|
0.00
|
0.00
|
0.58
|
0.90
|
1.12
|
1.24
|
1.32
|
1.41
|
1.45
|
1.49
|
1.51
|
1.54
|
Table 3 Pair wise comparison matrix(Das et al. 2018)
|
1
|
2
|
3
|
4
|
5
|
6
|
7
|
8
|
9
|
10
|
11
|
12
|
1
|
1
|
1
|
0.25
|
0.33
|
0.25
|
0.33
|
1
|
0.125
|
0.25
|
0.33
|
3
|
1
|
2
|
1
|
1
|
0.5
|
0.25
|
1
|
0.33
|
1
|
0.142
|
0.25
|
0.25
|
0.33
|
1
|
3
|
4
|
2
|
1
|
0.5
|
3
|
3
|
1
|
0.25
|
0.33
|
1
|
3
|
2
|
4
|
3
|
4
|
2
|
1
|
3
|
5
|
3
|
0.2
|
1
|
1
|
3
|
2
|
5
|
4
|
1
|
0.33
|
0.33
|
1
|
1
|
1
|
0.142
|
0.5
|
1
|
1
|
3
|
6
|
3
|
3
|
0.33
|
0.2
|
1
|
1
|
1
|
0.142
|
0.5
|
1
|
1
|
3
|
7
|
1
|
1
|
1
|
0.33
|
1
|
1
|
1
|
0.142
|
0.2
|
1
|
2
|
2
|
8
|
8
|
7
|
4
|
5
|
7
|
7
|
7
|
1
|
2
|
4
|
9
|
8
|
9
|
4
|
4
|
3
|
1
|
2
|
2
|
5
|
0.5
|
1
|
1
|
3
|
3
|
10
|
3
|
4
|
1
|
1
|
1
|
1
|
1
|
0.25
|
1
|
1
|
2
|
2
|
11
|
0.33
|
3
|
0.33
|
0.33
|
1
|
1
|
0.5
|
0.111
|
0.33
|
0.5
|
1
|
2
|
12
|
1
|
1
|
0.5
|
0.5
|
0.33
|
0.33
|
0.5
|
0.125
|
0.33
|
0.5
|
0.5
|
1
|
Note: 1=curvature, 2=drainage density, 3= elevation,4= land surface temperature, 5= lineament density, 6= LULC, 7= lithology, 8= Rainfall, 9= Slope, 10= Soil, 11= Topographic wetness index, 12= Groundwater fluctuation.
The based on prior work completed and material readily available on the research, the user's knowledge, skill, and acumen are used to rate the criteria, finalise them, and make decisions. Therefore, using this subjective procedure, a rational strategy to finalising the components and ranks is needed. In this study, ground water potential zoning for the Srikakulam district of Andhra Pradesh, India, was spatially analysed using geographic information systems (GIS). AHP was used to createpair-wise comparison matrices and to calculate the weightage factors of each parameter(Sivakumar, Radha Krishnappa, and Nallanathel 2021). The final step of the AHP process is to calculate the consistency of the normalized criteria weights. The weights must have a consistency ratio (CR) value less than 0.10 to be considered consistent. (Jhariya, Mondal, et al. 2021). The pair-wise comparisons must be re-calculated if the Consistency ratio values are more than 0.10.The formula for calculation of Consistency ratio is shown below(Jhariya, Mondal, et al. 2021).
CR = CI / RI -2
Where CR is consistency ratio and the CI is the consistency index developed by the following equation(Aliyev, Temizkan, and Aliyev 2020).
CI = (ᨂ max – n) / (n-1) -3
Where ᨂ max denotes the maximum Eigen value of the judgment matrix,
\(\text{ᨂ} \text{m}\text{a}\text{x}=(1\backslash n){\sum }_{i=1}^{n}\left(\text{A}\text{w}\right)\text{i}/\text{W}\text{i}\) -4
And the groundwater potential zone equation shown below,
\(\text{G}\text{W}\text{P}\text{Z}={\sum }_{I=1}^{n}\text{W}\text{i}\text{*}\text{r}\text{i}\) -5
Where Wi is the relative weights of the criterion i and ri denotes the criterion's standardized score(Raj et al. 2022).
When there are several criteria and options accessible, MCDM is utilized to select the optimal option while preventing conflicts. Numerous MCDM methodologies are already in use, and the core of MCDM investigations is the integration of geospatial technology. GIS with AHP significantly lowers the confusion in the decision-making process since MCDM is very complicated in nature owing to the volume of a variety of dependent and independent elements discovered and taken into consideration. Using many criteria and geographic information, a decision is made using a procedure called spatial multi-criteria decision-making (MCDM). Consequently, several data layers must be processed in a multi-criteria evaluation to arrive at the ground water potential zones, it is easily accomplished with the use of GIS. As a result, to solve the issue with factor identification and selection in the study, ground water potential regions were determined using MCDM, AHP, and GIS(Sivakumar, Radha Krishnappa, and Nallanathel 2021).
Parameters
|
Sub-Class
|
Rank
|
Parameter weight
|
Sub-Class weight (%)
|
Table 4
Ground water potential Mapping and subclasses of different categories based on weights(Penki, Basina, and Tanniru 2022)
Slope
|
0–3
|
1
|
0.127
|
43
|
3–8.6
|
2
|
27
|
8.6–17
|
3
|
15
|
17–26
|
4
|
11
|
26–71
|
5
|
4
|
Elevation
|
< 67
|
1
|
0.086
|
50
|
67–194
|
2
|
23
|
194–411
|
3
|
21
|
411–1033
|
4
|
6
|
TWI
|
2.5–6.6
|
5
|
0.039
|
6
|
6.6–8.5
|
4
|
10
|
8.5–11
|
3
|
16
|
11–14.3
|
2
|
26
|
> 14.3
|
1
|
42
|
LST
|
8–17
|
1
|
0.117
|
45
|
17–19
|
2
|
26
|
19–20
|
3
|
15
|
20–21
|
4
|
10
|
21–28
|
5
|
4
|
Drainage Density
|
0–4.5
|
1
|
0.029
|
51
|
4.5–9
|
2
|
26
|
9–13.5
|
3
|
16
|
> 13.5
|
4
|
7
|
LULC
|
water bodies
|
2
|
0.055
|
29
|
forest area
|
3
|
14
|
agriculture
|
5
|
9
|
built up area
|
4
|
12
|
Bare land
|
1
|
36
|
Curvature
|
< -1.10
|
5
|
0.034
|
4
|
< -0.28
|
4
|
7
|
< 0.28
|
3
|
14
|
0.28–2.9
|
2
|
20
|
2.9–23.18
|
1
|
55
|
Lineament density Map
|
0–0.09
|
1
|
0.054
|
48
|
0.09–0.26
|
2
|
26
|
0.26–0.52
|
3
|
19
|
0.52–0.96
|
4
|
7
|
Lithology
|
Water (H2O)
|
1
|
0.048
|
75
|
Undivided Precambrian rocks
|
2
|
25
|
Soil
|
Loam
|
2
|
0.076
|
34
|
Sandy clay loam
|
3
|
8
|
Sandy loam
|
1
|
58
|
Rain Fall
|
880.7–943.43
|
5
|
0.304
|
5
|
943.44–1006.17
|
4
|
8
|
1006.18–1068.92
|
3
|
15
|
1068.93–1131.65
|
2
|
23
|
1131.65–1194.39
|
1
|
49
|
Ground water Fluctuations
|
0.25–1.5
|
1
|
0.031
|
41
|
1.5–2.7
|
2
|
26
|
2.7–3.9
|
3
|
16
|
3.9–5.2
|
4
|
11
|
5.2–6.4
|
5
|
6
|