Machine Learning Outperforms Expert Weighting in Mapping Landslide Risk in India’s Western Ghats

A new study of the landslide-prone hills around Idukki township in Kerala, India, shows that a machine learning model can outperform a widely used expert-based method when it comes to predicting where slopes are most likely to fail. The research, published in Discover Geoscience, compared a Random Forest classifier against the Analytic Hierarchy Process (AHP), a decision-making technique that relies on expert judgement, using an unusually rich set of inputs that includes both terrain characteristics and the engineering properties of the soil itself.

Landslides are a persistent threat in the Western Ghats, the mountain chain running along India’s western coast. Roughly fourteen percent of India’s land area is considered landslide-prone, and the country loses an estimated 500 lives and around four billion rupees each year to these events. In Kerala, the west-facing scarps of the Ghats cover about 1,400 square kilometres, and debris flows are the most frequent type of failure in the highlands. Nearly all of these mass movements occur during the monsoon, when prolonged and intense rainfall raises pore-water pressures in the soil and undermines slope stability.

The study area covers roughly 70 square kilometres around Idukki township, including Painav, Cheruthoni, Karimban, Thadiyampad, Maniyarankudy, Chelachuvadu, Keerithod and Kanjikuzhy. The terrain is rugged, with altitudes ranging from 500 to 2,600 metres, and the district receives 3,000 to 4,000 millimetres of rain annually from the southwest and northeast monsoons. The underlying geology is dominated by Precambrian crystalline basement rocks, chiefly biotite gneiss and hornblende-biotite gneiss, with no major lineaments identified in the mapped region.

What distinguishes this work from most landslide susceptibility studies is its integration of geotechnical parameters alongside the usual geomorphological factors. The researchers compiled fifteen conditioning factors in total: elevation, slope angle, slope aspect, profile and planiform curvature, topographic wetness index, geomorphology, land use, distance from roads and distance from drainage, plus five soil properties measured in the laboratory: cohesion, angle of internal friction, liquid limit, plastic limit and plasticity index. Soil samples were collected from locations with different slope-forming materials, and the point measurements were interpolated into continuous surfaces using kriging.

The foundation of any susceptibility model is a reliable inventory of past events. The team assembled a catalogue of 502 landslide locations by combining government reports with interpretation of pre- and post-event high-resolution satellite imagery, cross-checked against Geological Survey of India datasets. Most of the mapped landslides were triggered during the extreme rainfall of August 2018, though significant activity continued through 2019 and 2020. Slides accounted for 54 percent of the inventory and flows for 45 percent. For modelling, a balanced dataset of 503 landslide and 533 non-landslide points was split 70/30 into training and testing subsets.

The AHP method works by having experts compare factors pairwise on Saaty’s nine-point scale, producing weights that reflect each factor’s relative importance. The resulting comparison matrix passed the standard consistency check, with a consistency ratio below 0.10. The Random Forest model, by contrast, learned relationships directly from the data. Implemented in Python with the scikit-learn library, it used an ensemble of 100 decision trees, each trained on random subsets of the features, with predictions combined by majority vote. This ensemble approach reduces overfitting while capturing nonlinear interactions among factors that expert weighting cannot easily express.

The two models produced noticeably different maps. Under AHP, 28.43 percent of recorded landslides fell in high susceptibility zones and 37.78 percent in very high zones, with the highest-risk areas concentrated in the central and western parts of the study area, including Maniyarankudy, Thadiyampad and Karimban. The Random Forest model distributed landslides more evenly across classes, assigned the largest share of area to the medium susceptibility class (24.60 percent, or 17.18 square kilometres), and flagged additional high-risk zones around Kanjikuzhy in the northwest and eastern Karimban, while classifying Keerithodu and Painavu as very low susceptibility.

Validation told a clear story. The Random Forest model achieved a receiver operating characteristic area under the curve of 0.99, placing it in the excellent category, compared with 0.85 for AHP, which falls in the very good range. Accuracy was 0.947 for Random Forest against 0.774 for AHP, precision was 0.922 versus 0.777, recall was 0.947 versus 0.753, and the F1-score was 0.945 versus 0.765. The authors attribute the gap to AHP’s reliance on subjective expert judgement, which can introduce inconsistencies, while Random Forest learns directly from historical landslide data.

Despite the machine learning model’s superior numbers, the researchers are careful not to dismiss the expert-based approach. AHP remains transparent, easy to interpret, and valuable in regions where comprehensive landslide inventories do not exist, since it functions independently of historical event data. Its main drawbacks are the subjectivity of weight assignment and the assumption that each factor’s importance is uniform across the landscape. The authors argue that the two methods should be seen as complementary rather than competing, and that combining them can strengthen susceptibility assessments.

The study also acknowledges its limitations: the analysis rests on a finite historical inventory and a limited number of geotechnical sampling locations, and the resulting maps represent present-day conditions without explicitly accounting for changing rainfall patterns, groundwater conditions or land-use practices. Future work, the team suggests, could incorporate time-dependent factors such as rainfall intensity and groundwater fluctuations, along with higher-resolution topographic data and more advanced hybrid machine learning techniques. As extreme rainfall events intensify in a warming climate, tools like these are becoming central to land-use planning, early warning systems and disaster risk reduction in the world’s mountainous regions.

Subject of Research: Landslide susceptibility mapping in the Western Ghats using Random Forest and AHP with integrated geomorphological and geotechnical parameters

Article Title: Landslide susceptibility assessment in the Western Ghats adjoining Idukki township, Kerala, India, using integrated geomorphological and geotechnical parameters with AHP and machine learning

Article References: P, A. S., S, A., S, A. S., & P, J. K. (2026). Landslide susceptibility assessment in the Western Ghats adjoining Idukki township, Kerala, India, using integrated geomorphological and geotechnical parameters with AHP and machine learning. Discover Geoscience, 4(1), Article 406. https://doi.org/10.1007/s44288-026-00778-x

Image Credits: AI Generated

DOI: 10.1007/s44288-026-00778-x

Keywords: landslide susceptibility, Western Ghats, Idukki, Random Forest, Analytic Hierarchy Process, machine learning, geotechnical parameters, geomorphology, Kerala, disaster risk reduction, ROC-AUC, slope stability