The study integrated Geographic Information Systems, remote sensing and the Analytic Hierarchy Process (AHP) to generate a groundwater-potential map. Six thematic layers—rainfall, drainage density, slope, soil type, geology, and land use/land cover—were selected based on their reported hydrogeological influence and the availability of data. After standardization and assignment of normalized weights, the researchers used weighted-overlay analysis in ArcGIS Pro 3.1.0 to combine the layers. The resulting map divided the study area into five categories: very low, low, medium, high and very high groundwater potential.

Kitgum’s mapped area was described as predominantly having very low to low potential, with the abstract identifying higher drainage density and steeper slopes as constraints. Pader showed a contrasting modeled pattern: 85.5% of its area was placed in the high or very high categories, which the researchers associated with gentler slopes and higher rainfall that they said enhanced infiltration capacity. These are characteristics identified by the model and its AHP weighting framework, rather than direct measurements of groundwater volume at every mapped location.

For validation, the researchers compared the model with yield data from 18 existing boreholes. They reported a 75.0% alignment between model classifications and actual well performance and described this as confirming the model’s reliability at an acceptable level. The study is presented as a preliminary GIS-based assessment for data-scarce environments, and its map is intended as a scientific basis for more targeted groundwater exploration. The available evidence comes from the abstract of an open-access, peer-reviewed article shared in an early version subject to further editing; it does not provide the full model inputs, weighting values, detailed borehole results or an uncertainty analysis. The validation sample of 18 boreholes and the preliminary nature of the assessment also limit the precision with which the mapped zones can be interpreted.

Subject of Research: Preliminary GIS and AHP assessment of groundwater potential in Kitgum and Pader districts, Uganda

Article Title: Mapping groundwater potential zones using GIS, remote sensing and analytical hierarchy process (AHP) in Pader and Kitgum Districts, Uganda

Article References: Dirie, A. A., Mohamed, A. A., & Kannapiran, A. (2026). Mapping groundwater potential zones using GIS, remote sensing and analytical hierarchy process (AHP) in Pader and Kitgum Districts, Uganda. Discover Sustainability. https://doi.org/10.1007/s43621-026-04943-7

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04943-7

Keywords: groundwater potential, Kitgum District, Pader District, Uganda, geographic information systems, remote sensing, analytical hierarchy process, weighted overlay analysis, borehole validation, water-resource mapping, Mapping, groundwater

The study, published as an open-access case study in the journal Discover Geoscience, focuses on Pozhuthana Grama Panchayath, a 70.29-square-kilometer patch of the Western Ghats in Vythiri Taluk whose very name is said to derive from the phrase Puzha Tanna, meaning the place that rivers gave. The terrain rises to nearly 2,100 meters, and the elevation within the panchayath itself ranges from 624 to 1,505 meters above mean sea level. That dramatic relief is precisely what makes the area treacherous: water that falls on the high ridges converges rapidly into the valleys, and the low-lying valley fills and gently undulating piedmont zones act as temporary reservoirs during heavy rainfall. The researchers chose the site deliberately, because it sits at a scale that most flood studies in India ignore. While susceptibility mapping has been carried out for entire river basins and states, micro-level assessments at the panchayat scale — the level at which Kerala’s Panchayati Raj institutions actually plan and respond — have been conspicuously missing.

To fill that gap, the team built what hydrologists call a multi-criteria decision analysis, executed entirely within the ArcGIS 10.8.2 environment. Eight thematic layers were assembled: elevation and slope extracted from a Shuttle Radar Topography Mission digital elevation model, rainfall interpolated from station data, drainage density derived from the stream network, and geology, geomorphology, soil and land use/land cover compiled from satellite imagery, the Bhukosh portal and field-verified geological maps. Each layer was reclassified onto a common scale of one to five, where five marks the strongest contribution to flood susceptibility. Low elevations, gentle slopes, dense drainage networks, clay-rich soils and built-up or agricultural land all scored high, because each of these conditions either slows the escape of water or accelerates its arrival.

The rainfall surface deserves particular attention, because rainfall is the engine of every flood in the region. The team used inverse distance weighted interpolation, a technique that estimates values at unsampled locations by giving progressively less influence to measurements taken farther away. A power value of two was applied to sharpen that distance decay, and a fixed search radius ensured that every interpolated cell drew on a consistent neighborhood of observations. Data from the Pinangode and Vythiri stations, roughly seven kilometers apart, were blended with field-monitored rainfall measurements collected on site, and the resulting raster surface was validated against the observed moisture variability across the panchayath. The approach is mathematically humble compared with sophisticated geostatistics, but its minimal assumptions make it well suited to data-sparse mountain terrain and to the fast-moving realities of monsoon flooding.

With the eight layers ranked, the researchers turned to the Analytic Hierarchy Process, the pairwise comparison framework devised by Thomas Saaty, to decide how much each factor should count. Using Saaty’s one-to-nine fundamental scale, they judged rainfall to be the dominant driver, assigning it a weight of 25 percent, followed by elevation at 20 percent. Slope, geomorphology, soil, drainage density and land use/land cover each received 10 percent, while geology — which influences flooding indirectly through the low permeability of the region’s charnockite and gneissic rocks — received 5 percent. The pairwise comparison matrix was then normalized by column summation and row averaging to extract the priority weights. The consistency arithmetic that followed was, by the standards of the method, remarkable: the principal eigenvalue came out at exactly 8.0, equal to the number of parameters, yielding a Consistency Index of zero and a Consistency Ratio of 0.00 against a random index of 1.41. In plain terms, the expert judgments embedded in the matrix were perfectly transitive, with no logical contradictions, which is rare in real-world applications where a ratio of 0.10 is usually considered acceptable.

The weighted overlay of the eight reclassified layers produced the study’s centerpiece: a Flood Hazard Zonation map dividing the panchayath into five risk classes. The spatial pattern is strikingly coherent. Very high risk zones, covering 7.47 percent of the area, cluster in the central to north-central basin around Achooranam and Pozhuthana, with high risk zones — 28.86 percent of the land — extending toward the northeast near Pinangode. Low and very low risk zones, together accounting for 38.64 percent, dominate the western, southwestern and southern sectors near Sugandhagiri and Kolichal, where the terrain is higher, slopes are steeper and gravelly soils drain more freely. The moderate class occupies 25.05 percent. Combined, the high and very high categories reveal that just over 36 percent of the panchayath faces meaningful flood exposure, and those hotspots coincide with low-lying ground, clay and loam soils that swell and seal when wet, dense drainage networks that concentrate runoff quickly, and settlements whose impervious surfaces shed rainfall rather than absorbing it.

What elevates this study above many GIS-based hazard maps is the ground truthing. The researchers surveyed 1,120 households across the panchayath, using purposive sampling that deliberately targeted riverbank settlements, low-lying areas and historically vulnerable zones. Of those households, 118 — 10.5 percent — reported being directly flooded during the 2018 disaster, while the remaining 1,002 occupied relatively elevated ground. When the affected houses were plotted against the modeled hazard zones, the correspondence was absolute: every one of the 118 flooded households fell within the very high risk zone. GPS measurements confirmed that flood impacts concentrated at the lowest elevations while higher ground remained largely untouched, exactly as the weighted model predicted.

The statistical validation was equally emphatic. A Receiver Operating Characteristic analysis, the standard test of a classification model’s ability to discriminate between positive and negative cases, produced an Area Under the Curve of 0.97 — a value indicating exceptional discriminatory power, close to the theoretical maximum of 1.0. A confusion matrix built from the household survey showed that the model correctly identified all positive cases, the flooded houses, and all negative cases, the unaffected ones. In practical terms, the combination of rainfall and elevation weights, reinforced by the six supporting layers, captured the hydrological reality of Pozhuthana with near-perfect fidelity, isolating a small, specific high-risk portion of the landscape that contains virtually all of the observed damage.

The implications reach well beyond one village. The 2018 deluge was not an anomaly in Kerala’s history — major floods struck the region in 1341, 1907, 1924, 1961, 1974, 1992, 2003, 2013, 2018 and 2019 — and climate change studies suggest that short-duration, high-intensity monsoon rainfall events are becoming more frequent. Yet most hazard modeling in India operates at basin or district scales, too coarse to guide the decisions of a panchayat council deciding where to permit construction, where to reinforce a bridge, or where to stage evacuation routes. By demonstrating that a weighted overlay model, calibrated with nothing more exotic than a digital elevation model, satellite imagery, station rainfall and a household survey, can achieve near-perfect agreement with observed flood impacts, the study offers a replicable template for other vulnerable rural communities in hilly terrain.

The authors are careful to note that flood simulation and risk assessment cannot prevent floods; they can only reduce the damage they inflict. But the Pozhuthana map turns an abstraction into a planning instrument. It tells the village’s local government, which the study highlights as a critical actor in the disaster preparedness and recovery chain, exactly where the 36 percent of land that matters most lies. It identifies Pozhuthana and Achooranam as the most critical zones, flags the dense drainage convergence areas of the central basin as hydrological flashpoints, and confirms that the communities living on the valley floors — the same households that watched the water rise in 2018 — are the ones the next flood will find first. In an era when a single monsoon season can undo decades of development, a map that knows where the water will go may be the cheapest insurance a mountain village can buy.

Subject of Research: GIS-based flood hazard zonation using the Analytic Hierarchy Process in a Kerala panchayat

Article Title: Flood hazard zonation in Pozhuthana Grama Panchayath of Kerala using geoinformatics and analytic hierarchy process

Article References: Shirin, P. N., Sanjayan, M. S., Brijesh, V. K., & Swetha, R. (2026). Flood hazard zonation in Pozhuthana Grama Panchayath of Kerala using geoinformatics and analytic hierarchy process. Discover Geoscience, 4(1), Article 320. https://doi.org/10.1007/s44288-026-00664-6

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00664-6

Keywords: flood hazard zonation, geoinformatics, Analytic Hierarchy Process, GIS, remote sensing, Kerala floods 2018, Wayanad, weighted overlay analysis, digital elevation model, drainage density, land use land cover, disaster preparedness

The study, led by Oloche Robert Ekwule with colleagues Gariel Delian Akpen and Matthew Igbalumun Aho, set out to address a persistent gap in Nigerian flood research: earlier assessments of Makurdi had relied on relatively small sets of environmental factors and, critically, had rarely validated their predictions against independent records of where floods actually occurred. The new work integrated ten distinct flood-conditioning factors, drawing on freely available satellite products that have transformed what is possible in data-scarce regions. A 30-meter Digital Elevation Model from NASA’s ASTER instrument supplied the terrain backbone, yielding elevation, slope, drainage density, distance to streams, topographic wetness index, curvature, and geomorphological classes. Landsat 8 imagery provided vegetation density through the Normalized Difference Vegetation Index, while a 10-meter land-cover product derived from Sentinel-2 data captured the patchwork of built-up areas, cropland, trees, rangeland, bare ground, and water bodies across the city.

Rainfall, the climatic engine of flooding, was handled with particular care. The researchers obtained long-term mean annual rainfall data covering 1991 to 2021 from a global climate database, then adjusted those values for elevation using a century-old empirical relationship known as the Schreiber method, which assumes precipitation increases systematically with altitude—roughly 54 millimeters per 100 meters of elevation gain in this formulation. Five hundred randomly placed sampling points across the metropolis received these elevation-corrected values, which were then interpolated into a continuous rainfall surface using the Inverse Distance Weighted technique. The resulting map showed rainfall ranging from about 958 to 1033 millimeters across the study area, a modest spatial gradient but one that nonetheless contributes to differential runoff generation across the city’s low-relief landscape.

The heart of the method lies in the Analytical Hierarchy Process, a multi-criteria decision-making framework developed by mathematician Thomas Saaty. Rather than letting a statistical algorithm discover weights from data, AHP asks experts to compare every pair of factors and judge their relative importance on a scale from one to nine, where one means equal importance and nine means extreme dominance. For ten factors, that means forty-five pairwise judgments, which are assembled into a matrix and normalized by dividing each element by its column sum and averaging across rows. The resulting priority vector assigned elevation the largest share of influence at 19.43 percent, followed closely by slope at 18.21 percent, distance to streams at 14.22 percent, and drainage density at 13.43 percent. Precipitation received 8.73 percent, land use and land cover 8.25 percent, the topographic wetness index 6.98 percent, vegetation index 4.89 percent, geomorphology 3.34 percent, and curvature just 2.52 percent.

Because expert judgments can be internally contradictory, AHP includes a built-in consistency check. The researchers computed the principal eigenvalue of their comparison matrix at 10.71, derived a Consistency Index of 0.0785, and divided it by Saaty’s Random Index of 1.49 for ten criteria, yielding a Consistency Ratio of 0.0527. Since values below 0.10 are considered acceptably consistent, the weighting scheme passed the test, meaning the experts’ pairwise comparisons were logically coherent enough to trust. The weighted factors were then combined through a GIS-based weighted overlay, producing a composite Flood Susceptibility Index for every 30-meter pixel in the metropolis, which was classified into four categories from low to very high susceptibility.

The spatial pattern that emerged is dominated by geography. Moderate susceptibility covers the largest share of the city at 66.13 percent, or 546 square kilometers, reflecting the transitional landscapes where terrain and hydrology are intermediate. High susceptibility zones account for 27.81 percent, while very high susceptibility—though covering only 5.96 square kilometers, or 0.72 percent of the area—is concentrated precisely where it matters most: along the River Benue floodplain and the adjacent low-lying communities that sit on terrain between 62 and 89 meters above sea level. Only 44 square kilometers, about 5 percent of the city, qualified as low susceptibility, and those areas lie in the elevated, well-drained peripheral uplands. The physical logic is straightforward: Makurdi’s terrain is predominantly gentle, with slopes of zero to six degrees near the river corridors, which promotes slow runoff, prolonged water accumulation, and overbank flooding when the Benue and its tributaries overflow.

Several of the thematic layers tell a story about how human activity reshapes flood risk. The land-cover analysis showed that built-up areas and cropland exhibit relatively high susceptibility because impervious surfaces and disturbed soils reduce infiltration and accelerate runoff, while areas dominated by trees and dense vegetation received lower ratings because canopy interception and root systems slow water movement and enhance soil absorption. The Normalized Difference Vegetation Index, calculated from the near-infrared and red bands of Landsat 8 and ranging from minus 0.166 to 0.549 across the city, confirmed this inverse relationship: sparser vegetation correlated with greater vulnerability. Meanwhile, the topographic wetness index, which combines upstream contributing area with local slope in a logarithmic formula, ranged from 3.88 to 24.47, with the highest values clustering in the central low-lying portions of the city where runoff converges and drainage is naturally poor.

Perhaps the most important methodological advance is the validation. The researchers assembled an independent binary dataset of 30 reference points—17 documented flood occurrences recorded between 2012 and 2022, obtained from the Benue State Emergency Management Agency, and 13 randomly selected non-flood locations. Crucially, none of these points were used in the weighting, reclassification, or overlay stages; they were reserved exclusively to test the finished map. Overlaying the points on the susceptibility zones and performing Receiver Operating Characteristic analysis produced an Area Under the Curve of 0.94, a score above the 0.90 threshold generally interpreted as excellent discrimination between flood-prone and non-flood-prone ground. The authors are candid about the limitation, however: with only 30 validation points, the result demonstrates strong discriminatory capability for the available data, but larger independent inventories and complementary uncertainty analyses would provide additional confidence in the model’s robustness.

The broader significance of the study extends well beyond Makurdi. In an era when machine learning methods such as Random Forest, Support Vector Machines, and XGBoost dominate the flood-susceptibility literature, the researchers argue that the GIS–AHP approach remains highly suitable for urban flood studies in regions where reliable hydrological records are limited and expert-based spatial evaluation is essential. The method is transparent—every weight can be traced to an explicit judgment and checked for consistency—and it runs on freely available satellite data, making it replicable in other flood-prone cities across sub-Saharan Africa where rapid, unplanned urbanization, blocked drainage channels, and encroachment into natural floodplains are steadily amplifying flood losses. Nigeria has endured devastating national flood events in 2012, 2020, 2022, and 2024, and climate-driven extreme rainfall is intensifying the hazard.

For Makurdi’s planners, the map is a practical decision-support tool. It identifies, at 30-meter resolution, which neighborhoods warrant drainage investment, which developments should be restricted, and where early-warning and evacuation resources should be pre-positioned before the next wet season peaks. The finding that very high susceptibility hugs the River Benue floodplain is hardly surprising to residents of Wadata or Wurukum, but quantifying it—and validating the quantification against a decade of recorded floods—converts lived experience into evidence that can guide policy. As the authors note, the framework can be adapted to other riverine cities with similar environmental characteristics, offering a template for turning open satellite data and structured expert judgment into actionable flood-risk intelligence for the places that need it most.

Subject of Research: GIS and Analytical Hierarchy Process modelling of flood susceptibility in Makurdi Metropolis, Nigeria

Article Title: Integrated GIS–AHP modelling for flood susceptibility assessment in Makurdi Metropolis, Nigeria

Article References: Ekwule, O. R., Akpen, G. D., & Aho, M. I. (2026). Integrated GIS–AHP modelling for flood susceptibility assessment in Makurdi Metropolis, Nigeria. Discover Geoscience, 4(1), Article 338. https://doi.org/10.1007/s44288-026-00714-z

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00714-z

Keywords: flood susceptibility, GIS, Analytical Hierarchy Process, Makurdi, River Benue, Nigeria, remote sensing, digital elevation model, ROC-AUC validation, urban flooding, land use land cover, weighted overlay analysis