ArticleScientific reports2025
Deciphering of groundwater potential zones in hard rock terrain using GIS technology with AHP statistical methods: A case study of Nilgiri, Tamil Nadu, India.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This research aims to define the potential of using natural networks for groundwater mapping. While neural networks have proven effective for various perceptual tasks, the difficulty in identifying data points below the surface remains a key challenge. The area under study encompasses a mountainous region in the Western Ghats. The most efficient, practical, along sensible methods for defining the GWPZ (Groundwater Potential Zones) in the Nilgiri's hard rock terrain are Geographic Information Systems (GIS) as well as analytic hierarchy process (AHP) of multicriteria decision making. To create various thematic layers, we utilized Indian topographical maps, satellite imagery, and field observations. We collected data on ten factors influencing groundwater(GW), including LULC(Land Use Land Cover), elevation, slope, soil type, geomorphology(GM), rainfall(RF), geology(GL), LD(lineament density), as well as DD(drainage density). Based on the weight assignment, all the thematic maps influencing GW events were assessed and compiled using GIS analysis. The weighted index overlay (WIO) approach and PCM (pairwise comparison matrix) within the AHP were used for a hierarchical ranking to identify the possible GW zones. The outcome revealed that the sample region could be divided into 5 separate groundwater potential (GWP) areas, i.e., very good (10%), good (32%), moderate (21%), low (26%), as well as very low (11%) potentials. Well and spring data were used to validate the model, and the ROC (Receiver Operating Characteristic) curve method was applied. The results showed a good accuracy of 70.03%. chance of correctly distinguishing a randomly chosen true positive from a false positive. This research is useful for improved preparation and control of GW supplies and offers swift guidelines for the discovery of GW in the hard rock aquifer region.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.