Delineation of groundwater potential zones using remote sensing and Geographic Information Systems (GIS) in Kadaladi region, Southern India
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Abstract: The primary objective of this research is to delineate potential groundwater recharge zones in the Kadaladi taluk of Ramanathapuram, Tamil Nadu, India, using a combination of remote sensing and Geographic Information Systems (GIS) with the Analytical Hierarchical Process (AHP). Various factors such as geology, geomorphology, soil, drainage, density, lineament density, slope, rainfall were analyzed at a specific scale. Thematic layers were evaluated for quality and relevance using Saaty's scale, and then integrated using the weighted linear combination technique. The weights assigned to each layer and features were standardized using AHP and the Eigen vector technique, resulting in the final groundwater potential zone map. The AHP method was used to normalize the scores following the assignment of weights to each criterion or factor based on Saaty's 9-point scale. Pair-wise matrix analysis was utilized to calculate the geometric mean and normalized weight for various parameters. The groundwater recharge potential zone map was created by mathematically overlaying the normalized weighted layers. Thematic layers indicating major elements influencing groundwater occurrence and recharge were derived from satellite images. Results indicate that approximately 21.8 km2 of the total area exhibits high potential for groundwater recharge. Groundwater recharge is viable in areas with moderate slopes, particularly in the central and southeastern regions.
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Key words:
- Groundwater /
- Satellite image /
- Remote sensing /
- GIS techniques /
- Analytical Hierarchy Process (AHP)
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Table 1. The fundamental scale of AHP (Saaty, 1980)
Intensity of importance Definition Explanation 1 Equal importance Two activities contribute equally to the objective 3 Moderate importance of one over another Experience and judgment strongly favor one activity over another 5 Essential of strong importance Experience and judgment strongly favor one activity over another 7 Very strong importance An activity is strongly favored and its dominance demonstrated in practice 9 Extreme importance The evidence favoring one activity over another is of the highest possible order of affirmation 2,4,6,8 Intermediate values between the two adjacent judgments When compromise is needed Table 2. Saaty's ratio index for different values of N
N 1 2 3 4 5 6 7 8 9 10 RI 0 0 0.58 0.89 1.12 1.24 1.32 1.41 1.45 1.49 Table 3. The resulting weights are based on the principal Eigen vector of the decision matrix
Thematic layers Geomorphology Lithology LULC Lineament density Drainage density Slope Rainfall DEM Geomorphology 1 2 3 6 5 4 3 5 Lithology 0.5 1 2 6 5 4 3 2 LULC 0.33 0.5 1 2 2 4 3 2 Lineament density 0.17 0.17 0.5 1 1 2 3 2 Drainage density 0.2 0.2 0.5 1 1 4 3 2 Slope 0.25 0.25 0.25 0.5 0.25 1 3 2 DEM 0.33 0.33 0.33 0.33 0.33 0.33 1 1 Rainfall 0.2 0.5 0.5 0.5 0.5 0.5 1 1 Table 4. These are the resulting weights for the criteria based on your pairwise comparisons
Categories Priority Rank 1 Geomorphology 30.90% 1 2 Lithology 23.30% 2 3 LULC 13.20% 3 4 Lineament density 7.70% 5 5 Drainage density 9.30% 4 6 Slope 6.00% 6 7 DEM 4.50% 8 8 Rainfall 5.20% 7 Table 5. List of parameters and APH ratings and weights
Thematic Layer Factors Rank Weight Overall Geomorphology Aeolian Interdunal Depression and Palaya 3 21 43 Aeolian sand dune 3 43 Alluvial Plain 4 44 Coastal Plain 4 44 Deltaic Plain 5 105 Flood Plain 1 21 Pediment Pediplain Complex 2 42 Salt Pan 2 42 Water Bodies - Other 1 21 Water Body – River 4 48 Lithology Black Clay 3 19 57 Black Clayey Sand 5 95 Black Silty Clay 4 76 Brown Fine Sand 4 76 Brown silty clay 3 57 Coarse sand with rock fragments 3 57 Grey fine sand 3 57 Hornblende biotite gneiss 2 38 Channel bar / Point bar 2 38 Terri dune sand 3 38 Sand with coarals 2 38 Silty clay 2 38 LULC Waters 5 16 80 Vegetation 4 64 Crops 3 48 Built area 3 48 Bare ground 2 32 Range land 2 32 Lineament density Very low 1 14 14 Low 2 28 Medium 3 42 High 4 56 Very high 5 70 Drainage density Very low 1 14 14 Low 2 28 Medium 3 42 High 4 56 Very high 5 70 Rainfall Very low 1 2 7 Low 2 14 Moderate 3 21 High 4 28 Very high 5 35 Slope Very low 1 8 12 Low 2 24 Moderate 3 36 High 4 48 Very high 5 60 DEM Very low 1 6 9 Low 2 18 Moderate 3 27 High 4 36 Very high 5 45 Table 6. Classification of groundwater potential zone
Classification Total area covered /km2 Area percentage/% Very low 74.158614 12.769079 Low 136.132953 23.440195 Medium 160.855328 27.697043 High 176.653441 30.417257 Very High 21.761407 3.747011 -
Abishek SR, Ravindran AA. 2023. Assessment of groundwater potential zones for urban development site suitability analysis in Srivaikundam region, Thoothukudi district, South India. Urban Climate, 49: 101443. DOI: 10.1016/j.uclim.2023.101443. Alharbi T, Abdelrahman K, El-Sorogy AS, et al. 2023. Identification of groundwater potential zones in the Rabigh-Yanbu area on the western coast of Saudi Arabia using remote sensing (RS) and geographic information system (GIS). Frontiers in Earth Science, 11: 1131200. DOI: 10.3389/feart.2023.1131200. Arulbalaji P, Padmalal D, Sreelash K. 2019. GIS and AHP techniques based delineation of groundwater potential zones: A case study from southern Western Ghats, India. Scientific reports, 9(1): 2082. DOI: 10.1038/s41598-019-38567-x. Balasubramanian N, Sivasubramanian P, Soundranayagam JP, et al. 2015. Groundwater classification and its suitability in Kadaladi, Ramanathapuram, India using GIS techniques. Environmental Earth Sciences, 74: 3263−3285. DOI: 10.1007/s12665-015-4394-7. Biswajit N, Zheng N, Ramesh PS, et al. 2018. Land use and land cover changes, and environment and risk evaluation of Dujiangyan City (SW China) using remote sensing and GIS techniques. Sustainability, 10(12): 4631. DOI: 10.3390/su10124631. Chenini I, Msaddek MH. 2020. Groundwater recharge susceptibility mapping using logistic regression model and bivariate statistical analysis. Quarterly Journal of Engineering Geology and Hydrogeology, 53(2): 167−175. DOI: 10.1144/qjegh2019-047. Fu CC, Li XQ, Cheng X. 2023. Unraveling the mechanisms underlying lake expansion from 2001 to 2020 and its impact on the ecological environment in a typical alpine basin on the Tibetan Plateau. China Geology, 6(2): 216−227. DOI: 10.31035/cg2023015. Muduli A, Chattopadhyay PB, Pal U. 2023. Mapping of heterogeneity on groundwater level and potential zones along expeditiously urbanizing tropical coastal regions. Groundwater for Sustainable Development, 23: 101002. DOI: 10.1016/j.gsd.2023.101002. Murmu P, Kumar M, Lal D, et al. 2019. Delineation of groundwater potential zones using geospatial techniques and analytical hierarchy process in Dumka district, Jharkhand, India. Groundwater for Sustainable Development, 9: 100239. DOI: 10.1016/j.gsd.2019.100239. Rajasekhar M, Raju GS, Raju RS, et al. 2018. Data on artificial recharge sites identified by geospatial tools in semi-arid region of Anantapur District, Andhra Pradesh, India. Data in Brief, 19: 462−474. DOI: 10.1016/j.dib.2018.04.050. Saaty TL. 1980. The analytic hierarchy process. McGraw-Hill International Book Company, New York. Saaty TL. 2001. Fundamentals of the analytic hierarchy process. The analytic hierarchy process in natural resource and environmental decision making, 15−35. Saaty TL. 2008. Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1): 83−98. DOI: 10.1504/IJSSCI.2008.017590. Saranya T, Saravanan S. 2020. Groundwater potential zone mapping using analytical hierarchy process (AHP) and GIS for Kancheepuram District, Tamilnadu, India. Modeling Earth Systems and Environment, 6(2): 1105−1122. DOI: 10.1007/s40808-020-00744-7. Sathiyamoorthy M, Masilamani US, Chadee AA, et al. 2023. Sustainability of groundwater potential zones in coastal areas of Cuddalore District, Tamil Nadu, South India using integrated approach of Remote Sensing, GIS and AHP Techniques. Sustainability, 15(6): 5339. DOI: 10.3390/su15065339.