Fuzzy Mapping Reveals Where Groundwater Hides Beneath India’s Drought-Prone Farmland

In the drought-prone heart of western India, where millions of farmers depend on wells drilled into ancient volcanic rock, a team of researchers has combined satellite imagery, field measurements and two competing mathematical approaches to answer a deceptively simple question: where, exactly, is the groundwater? Their study of Baramati tehsil in Maharashtra state, published in the journal Discover Geoscience, demonstrates that a fuzzy, uncertainty-aware version of a classic decision-making technique can outperform its conventional counterpart in mapping the hidden water resources of hard-rock terrain.

Groundwater is the invisible lifeline of India. It supplies roughly 62 percent of the country’s irrigation water, about 85 percent of rural drinking water and around half of urban drinking supplies. Yet in regions underlain by the Deccan Traps, the vast flood-basalt province that covers much of peninsular India, water does not sit in convenient underground reservoirs. Instead, it occupies weathered, fractured and jointed zones within otherwise impermeable rock, making its distribution patchy, unpredictable and notoriously difficult to assess. Climate change is intensifying the hydrological cycle, and the widening gap between water demand and natural recharge makes scientific planning of this resource increasingly urgent.

The research team, led by Nayan D. Zagade of Prof. Ramkrishna More College in Pune with colleagues from Savitribai Phule Pune University, focused on Baramati tehsil, an administrative unit of about 1,382 square kilometres in the southern part of Pune district. The area lies in a dry to semi-arid zone, receives a modest annual rainfall of roughly 333 to 417 millimetres, and is drained by the Karha River, which flows into the Nira River along the tehsil’s southern boundary. With a population of nearly 430,000 people and an economy anchored in agriculture, the region’s dependence on groundwater extracted through dug wells and bore wells is intense and growing.

To map where water is most likely to be found, the researchers assembled eight thematic layers in a geographic information system: geomorphology, slope, lineament density, vadose-zone thickness, soil, drainage density, rainfall and land use/land cover. Each layer captures a different piece of the hydrogeological puzzle. Lineaments, the linear fractures and joints visible in terrain analysis, create secondary porosity through which water can infiltrate and travel. The vadose zone, the unsaturated thickness between the land surface and the water table, was reconstructed from water-level measurements in 38 field wells. The land-cover map was derived from 2024 Sentinel-2 satellite imagery using supervised classification, achieving an overall accuracy of 92 percent and a kappa coefficient of 0.89.

The technical heart of the study lies in how these layers were weighted and combined. The team first applied the Analytical Hierarchy Process, or AHP, a multi-criteria decision-making method introduced by Thomas Saaty in 1980. In AHP, factors are compared pairwise on a one-to-nine scale, and criterion weights are extracted from the principal eigenvector of the resulting comparison matrix. Under this scheme, geomorphology received the highest weight at 0.22, followed by slope and lineament density at 0.17 each, with vadose zone and soil at 0.12, drainage density at 0.08, land use at 0.07 and rainfall at 0.05. The consistency ratio of 0.06 confirmed that the expert judgments were logically coherent.

Conventional AHP, however, forces experts to commit to crisp, single-value judgments even when their confidence is uncertain. To address this, the researchers also applied the Fuzzy Analytical Hierarchy Process, in which each pairwise comparison is expressed as a triangular fuzzy number with lower, most-probable and upper bounds. After defuzzification using the centroid method, the fuzzy model reshuffled the priorities: lineament density rose to the top with a weight of 0.15, followed by vadose zone at 0.14, slope and rainfall at 0.13 each, and geomorphology at 0.12. This shift suggests that when uncertainty is explicitly accounted for, structural controls on groundwater, the fractures that channel water through hard basalt, gain greater prominence. The fuzzy model’s consistency ratio of 0.04 was even better than the crisp version’s.

After weighted overlay of the eight reclassified layers, both models painted Baramati as a region of predominantly moderate groundwater potential, but with instructive differences. The AHP map assigned 67.72 percent of the area to the moderate class, 19.35 percent to high potential and 12.76 percent to low potential, with very high and very low zones occupying only marginal slivers of land. The F-AHP map was more conservative: the moderate class expanded to 78.40 percent, while high and low zones shrank to 14.42 percent and 7.12 percent respectively, and no area at all fell into the very low category. The fuzzy approach, by smoothing out abrupt transitions between suitability classes, produced a more cautious and arguably more realistic delineation.

To test how well the maps performed, the team used Receiver Operating Characteristic analysis, comparing predicted groundwater potential values at the 38 well locations against background values across the study area. The AHP model achieved an Area Under the Curve of 0.772, while the F-AHP model reached 0.835, a clear comparative edge. The authors are careful to note an important caveat: because the same 38 wells used for validation also informed the vadose-zone input layer, and because the ROC analysis was a presence/background comparison without independent true-negative data, these AUC values should be read as internal consistency measures rather than fully independent predictive validation. This kind of transparency about circularity is refreshing in a field where validation statistics are sometimes presented with more confidence than they deserve.

The hydrogeological interpretation behind the maps is grounded in the realities of basaltic aquifers. Favorable zones correspond to flat terrain, fractured rock, permeable clayey soils that still allow some infiltration, lower drainage density and agricultural land use that supports recharge. Unfavorable zones coincide with residual hills, exposed bedrock and steep slopes in the rugged north of the tehsil, where runoff dominates and water has little chance to percolate. The combined influence of geomorphology and lineament density, the two heaviest-weighted factors across both models, explains most of the spatial pattern of moderate-to-high potential.

For the people of Baramati, the practical implications are direct. The maps can guide where to prioritize well drilling and recharge interventions, and where abstraction should be restrained in favor of water conservation. The authors emphasize that the outputs are decision-support tools, not absolute portraits of the subsurface, since expert weighting, static rainfall surfaces and limited well data all introduce uncertainty. Still, the study offers a compelling demonstration that embracing uncertainty, rather than suppressing it, can make environmental models both more honest and more accurate. In a semi-arid world where every drop counts, that lesson travels far beyond one tehsil in Maharashtra.

Subject of Research: Geospatial mapping of groundwater potential zones in a semi-arid hard-rock region of India using AHP and fuzzy AHP methods

Article Title: Geospatial modelling of groundwater potential zones in Baramati tehsil, India using AHP and F-AHP approaches

Article References: Zagade, N. D., Pardeshi, S. S., Umrikar, B. N., & Badhe, Y. P. (2026). Geospatial modelling of groundwater potential zones in Baramati tehsil, India using AHP and F-AHP approaches. Discover Geoscience, 4(1), Article 330. https://doi.org/10.1007/s44288-026-00701-4

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00701-4

Keywords: groundwater, geospatial modelling, AHP, fuzzy AHP, GIS, remote sensing, Deccan Traps, Maharashtra, hydrogeology, water resources, multi-criteria decision making, ROC analysis