The research team focused on the upper Ziz area, a catchment whose elevations sweep from about 1,023 meters to 3,687 meters above sea level and whose slopes range from nearly flat ground to cliffs pitched at more than 66 degrees. The region sits within thick Jurassic sequences of calcareous, marly-calcareous, and marly rocks folded by ancient tectonic forces, producing the deep gorges, escarpments, and jagged summits that define the High Atlas. A semi-arid climate governs the area, with mean annual precipitation between 119 and 377 millimeters and temperatures averaging between 10.2 and 19.2 degrees Celsius. These conditions have carved a landscape of canyons and cliff bands that already attract climbers, particularly near well-known destinations such as the Tinghir gorges and the cliffs of Azilal, yet much of the terrain remains undocumented from a climbing perspective.

Traditional approaches to evaluating geomorphosites, the landforms valued for their geological and recreational significance, typically depend on quantitative scoring systems, semi-quantitative checklists, or hybrid qualitative-quantitative frameworks. While these methods have produced valuable inventories across Morocco and beyond, they are labor-intensive, demand extensive field campaigns, and can be impractical in remote mountain environments where access is limited to dirt tracks. The researchers argued that geographic information systems combined with machine learning algorithms offer a faster and cheaper alternative, capable of ranking terrain across an entire watershed without exhaustive surveying. Their study therefore set out to test whether two widely used classifiers, logistic regression and support vector machines, could reliably predict where climbing sites are likely to occur based purely on topographic evidence.

Building the training data required a careful inventory campaign. The team compiled a database of 120 mountain climbing sites and 120 non-climbing sites, drawing on field visits, Google Earth satellite imagery, consultations with local communities, and previous studies of the region. Each location was characterized using three topographic parameters derived from a 30-meter-resolution digital elevation model obtained from the United States Geological Survey: altitude, slope angle, and slope length. Before modeling, all spatial layers were reprojected to a common coordinate system, resampled to a uniform resolution, reclassified into ordered suitability categories, and normalized to a common scale between 0.1 and 0.9 so that no single variable would dominate the algorithms simply because of its units. Spatial processing was performed in ArcGIS 10.5, statistical analysis in IBM SPSS Statistics 26, and machine learning modeling in Weka 3.8.5.

The choice of only three conditioning factors was deliberate rather than an oversight. Lithology, though clearly relevant to climbing, was excluded because detailed lithological data at the required spatial resolution were unavailable for the study area. Land use and land cover were omitted because the region is dominated by degraded forest and pasture with only narrow agricultural strips along the Oued Ziz, a near-homogeneous cover that offers almost no discriminating power between candidate sites. Accessibility was not fed into the models either, but the researchers overlaid a one-kilometer buffer around the road network onto their final suitability maps to highlight which promising zones are actually reachable. The road network, they noted, consists mainly of a single national road linking Midelt to Errachidia and a regional road connecting Er-Rich to Imilchil, with everything else reduced to unpaved tracks, a significant logistical constraint for any future climbing development.

Statistical evaluation of the three factors revealed a clear hierarchy of influence. Using Weka’s CorrelationAttributeEval, the team found that slope angle contributed the most predictive power at 58 percent, followed by elevation at 50 percent, while slope length lagged at 21 percent. The frequency ratio analysis sharpened this picture: more than 80 percent of the inventoried climbing sites fell within the 2,300-to-2,900-meter altitude band, with the 2,300-to-2,600-meter class showing the strongest association at a frequency ratio of 7.60. For slope, sites steeper than 60 degrees carried the highest ratio of 9.58, and over 90 percent of all recorded climbing locations occurred on slopes exceeding 30 degrees. Pearson’s correlation testing confirmed that the three factors were sufficiently independent to be used together, satisfying a key prerequisite for both classifiers.

The two algorithms then processed the data in a structured three-stage protocol. The 240-site database was randomly split into a 70 percent training set and a 30 percent test set before any model fitting. Ten-fold cross-validation on the training data tuned the models and estimated their performance, after which the finalized models were applied to the held-out test set to measure generalizability. The support vector machine, which works by finding an optimal hyperplane that separates climbing from non-climbing locations while maximizing the margin between them, achieved a cross-validation area under the curve of 0.82, settling at 0.754 on independent validation. Logistic regression, a generalized linear model that estimates the probability of site occurrence from the topographic predictors, posted a training AUC of 0.801 that declined to 0.711 during validation. The support vector machine’s edge, the authors suggested, stems from its capacity to capture non-linear relationships among terrain variables, an advantage in the structurally complex Atlas terrain where linear models are fundamentally constrained.

Translated into maps, the models painted a consistent picture of where the region’s climbing potential concentrates. The logistic regression model identified highly suitable terrain covering 15.18 percent of the study area, while the support vector machine flagged 19.21 percent, with the index values ranging up to nearly 1.0 on the normalized scale. In both cases, the most promising zones clustered in the western portion of the study area, characterized by rugged topography, short and steep slopes, and high altitudes. The predicted suitable locations overlapped substantially with geomorphosite areas identified in earlier studies of the same watershed, particularly in the west, an encouraging sign that the algorithms were capturing real geological structure rather than statistical noise. When the road buffer was applied, however, it became clear that many of the highest-rated zones lie far from paved access, underscoring the gap between physical suitability and practical developability.

The authors were candid about the limits of their approach. The 30-meter digital elevation model, while freely available and widely used, smooths away fine-scale features such as narrow ridges, small rock faces, and micro-topographic variations that can make or break a climbing route; boulders tens of meters high, ideal for bouldering, simply cannot be resolved at this scale. Rock stability, a decisive factor for climber safety, could not be included because no suitable data exist for the area. The team also acknowledged potential spatial autocorrelation in the validation process and cautioned that their findings should not be generalized to other geomorphological settings, such as the Himalayas or coastal Thailand, without comparable local studies using sub-meter-resolution elevation data. Field verification, they stressed, remains essential before any mapped zone is opened to climbers.

Even with those caveats, the study signals a shift in how adventure tourism might be planned in data-poor mountain regions. Interest in climbing among Moroccan nationals has been rising visibly, with cliffs near Azilal and the gorges of Tinghir drawing crowds thanks to their proximity to Marrakech and Ouarzazate. A validated, algorithm-generated suitability map gives decision-makers a starting point for prioritizing sites, directing infrastructure investment, and balancing tourism promotion against conservation of fragile geoheritage. The researchers suggest that future work should incorporate additional geo-environmental variables such as geology, land cover, and access, apply spatially explicit validation strategies, and test ensemble or hybrid algorithms to push accuracy further. For now, the message from the High Atlas is clear: some of Morocco’s next great climbing walls may already be hiding in the data, waiting for a machine to point the way.

Subject of Research: Machine learning-based geospatial modeling of potential mountain climbing sites in the semi-arid Eastern High Atlas of Morocco

Article Title: Geospatial modeling of potential climbing sites in the semi-arid mountains of Morocco using machine learning

Article References: Manaouch, M., Rhoujjati, N., Pham, Q. B., & Rhoujjati, A. (2026). Geospatial modeling of potential climbing sites in the semi-arid mountains of Morocco using machine learning. Discover Geoscience, 4(1), Article 294. https://doi.org/10.1007/s44288-026-00676-2

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00676-2

Keywords: machine learning, geotourism, geodiversity, High Atlas, Morocco, GIS, support vector machine, logistic regression, digital elevation model, geomorphosites, mountain climbing, spatial modeling

The tool at the heart of the study is the Modified Geosite Assessment Model, or M-GAM, a quantitative method that evaluates geosites on two complementary dimensions. The Main Value (MV) captures intrinsic scientific and educational qualities: how representative a landform is of regional geomorphic processes, how rare it is, how well it records geological history, and how intact it remains. The Additional Value (AV) covers the practical and experiential attributes that determine whether a site can actually attract and serve visitors: accessibility, infrastructure, scenery, safety, and the presence of interpretive facilities. By separating what a site means from what a site offers, the model prevents spectacular but remote landforms from being judged by the same criteria as accessible viewpoints, and vice versa.

What distinguishes this application from earlier uses of the model is its dual-audience design. Experts scored each geosite on 27 standardized sub-indicators, but the researchers then modified those expert judgments with importance factors (Im) derived from visitors themselves. A structured survey collected opinions from 202 respondents, whose ratings of how much weight each attribute deserved were folded into the expert assessment. This is a meaningful methodological refinement: geotourism planning often fails because scientists and tourists value different things, and a ranking that ignores visitor priorities risks protecting sites nobody visits while promoting sites with little scientific substance. The visitor survey instrument showed acceptable internal consistency, with Cronbach’s alpha of 0.79 for the Main Value subscale and 0.84 for the Additional Value subscale, indicating that respondents answered in a coherent and reliable pattern.

The results reveal striking variability across the state’s inventory. Scores differed widely on all five attribute clusters the authors examined: scientific and educational value, scenic and aesthetic appeal, protection status, functional capacity, and touristic potential. Perhaps the most telling headline number is the comparison of the two value dimensions. The mean Additional Value score across the 42 geosites was 11.90, notably higher than the mean Main Value score of 8.53. In plain terms, the sites of West Bengal, on average, offer better functional and tourism-supporting conditions than their intrinsic scientific scores alone would suggest, and those practical attributes pull substantial weight in the final relative rankings. For planners, this means that infrastructure and access, not just geological significance, drive which sites rise to the top.

Using quartile-based thresholds within the study inventory, the team sorted the 42 geosites into three priority classes: 13 sites of relatively high priority, 18 of moderate priority, and 11 of low priority. This relative classification is deliberately comparative rather than absolute; a site’s rank depends on how it performs against the other sites in the same dataset, which makes the method well suited to allocating limited conservation and promotion budgets across a state-wide portfolio. The high-priority group represents the landscapes where scientific value, visitor experience and management readiness converge, and where investment in geotourism is most likely to pay both educational and economic dividends.

Correlation analysis of the model’s components added quantitative texture to these patterns. Accessibility correlated strongly with visitor numbers (r = 0.74), a relationship that will surprise no one who has watched a well-paved viewpoint outdraw a more remarkable but unreachable landform. The correlation between the touristic component and Additional Value was even stronger (r = 0.81), but the authors treat this figure with appropriate caution, because the touristic component is itself part of the Additional Value construct, making the association partly circular. This kind of statistical self-awareness is a strength of the paper: rather than presenting every correlation as a discovery, the authors flag which relationships are independent evidence and which are artifacts of how the model is built.

One of the most interesting analytical moves in the study is the direct comparison of expert and visitor assessments, which identifies sites where geomorphological significance and perceived importance either converge or diverge. Convergence suggests a site where scientific merit and public appeal reinforce each other, the ideal profile for flagship geotourism destinations. Divergence cuts both ways: some sites of genuine scientific importance score poorly with visitors, often because they lack the scenery, access or interpretation that makes geomorphology legible to non-specialists, while other visually spectacular sites may attract crowds without carrying exceptional scientific weight. Mapping these convergences and divergences gives managers a practical triage tool, showing where investment in trails, signage and interpretation could close the gap between what a landform is and what visitors think it is.

The geographic sweep of the inventory is itself a contribution. Geosite assessments have often focused on single provinces, a mountain range or a coastline, evaluated in isolation. By sampling across the Himalaya, the Sub-Himalayan piedmont, the plateau, the fluvial plains, the delta and the coast within one consistent framework, the study makes it possible to ask, for the first time in this region, which landscape types deliver the highest combined value. That matters for a state whose geomorphic identity is defined by this extraordinary gradient, and it matters for geotourism strategies that could link sites into itineraries tracing the full descent from high mountains to the sea.

The authors are candid about the uncertainties inherent in the approach. Visitor sampling may not represent the full population of potential tourists; expert scoring involves judgment calls; the weighting and aggregation of sub-indicators embed assumptions; and the quartile-based classification is relative, meaning a different inventory could reshuffle the ranks. None of these caveats undermines the core contribution, which is to demonstrate M-GAM as a comparative decision-support framework. The study was approved by the Institutional Ethics Committee of the Department of Geography at Syamaprasad College, conducted in accordance with the Declaration of Helsinki, and carried out with informed consent from all survey participants. The authors declare no competing interests, and the work received no external funding.

Published as an open-access article on 30 September 2026, the paper arrives at a moment when geotourism is gaining traction as a sustainable alternative to mass tourism in ecologically sensitive regions. For West Bengal, the practical implications are concrete: the 13 high-priority geosites form a ready-made shortlist for infrastructure investment, interpretation programs and heritage designation, while the divergent sites identified through the expert-visitor comparison point to where low-cost interventions such as signage and access improvements could unlock hidden value. More broadly, the study offers a template that other geomorphologically diverse regions, from Andean valleys to Rift Valley escarpments, could adapt. By putting numbers on the sometimes vague language of geoheritage, and by insisting that visitors have a formal voice in how landforms are valued, the research moves geosite assessment from description toward decision, and gives the landscapes of West Bengal a quantified case for the protection and promotion they have long deserved.

Subject of Research: Quantitative assessment and prioritization of geomorphosites for geotourism and geoheritage conservation in West Bengal, India

Article Title: Geomorphosite assessment and prioritization across diverse landscapes of West Bengal India using expert and visitor modified geosite assessment model

Article References: Bhandari, U., Mondal, S., Basak, I., Roy, M., Koley, B., Das, S., & Nath, A. (2026). Geomorphosite assessment and prioritization across diverse landscapes of West Bengal India using expert and visitor modified geosite assessment model. Discover Sustainability. https://doi.org/10.1007/s43621-026-04880-5

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04880-5

Keywords: geomorphosites, geosites, geoheritage, geodiversity, geotourism, M-GAM, West Bengal, Eastern Himalaya, Ganges Delta, sustainable tourism, site assessment, geomorphology