In recent years, numerous landslides on hillsides in urban and rural areas have underscored that understanding and predicting these phenomena is more than an academic curiosity—it is a human necessity. When unstable slopes give way after intense rainfall, the consequences can be devastating, with both human and material losses. These recurring tragedies led us to a simple yet powerful question: Can we build landslide susceptibility maps that are more objective, transparent, and useful for local authorities and residents?
The answer led us to compare two susceptibility analysis methods: the traditional Analytical Hierarchy Process (AHP) and its statistical version, the Gaussian AHP. After months of multidisciplinary work, we found that the Gaussian AHP, which relies on data rather than subjective judgments, better identifies critical areas in a more balanced manner and is consistent with the landslide patterns observed in the field. We share here our journey and the lessons we learned. Our findings are published in Scientific Reports.
Traditional AHP is a decision-support technique widely used in geosciences and urban planning. It relies on pairwise comparisons of factors such as slope, soil type, and distance to rivers or roads to assign relative weights based on expert opinion. One advantage is that it allows the incorporation of accumulated experience; a disadvantage is the subjectivity and the effort required when many factors are involved. In our case, we worked with 16 physical and environmental variables that influence slope instability—from terrain morphometry to land cover and proximity to rivers.
