(Landslide susceptibility prediction using the coupled Mahalanobis distance and machine learning models (case study: Owghan watershed, Golestan province

Introduction
Landslide susceptibility maps are considered a backbone for decision-makers to suggest solitary or
combined technical and regulatory measures. Such maps are also considered an invaluable tool for
engineers, earth scientists, planners, and decision-makers to select the most suitable areas for agriculture,
building, and other development activities. Hence, thanks to landslide susceptibility maps, addressing
highly susceptible areas are feasible, so that over the course of further detailed studies on the imminent
landslide occurrences in the future, landslide potential risk is mitigated.
Materials and Methods
In this study, two robust data mining models, namely random forest and maximum entropy were used
to map landslide susceptibility across the Owghan Watershed in Golestan province. After preparing the
landslide inventory map via extensive field surveys, interpreting Google Earth images, and the archived
data acquired from different organizations, landslide points were split into two sets of training (70%)
and validation (30%) by using the Mahalanobis distance technique. Further, drawing on the extensive
literature review, fifteen factors including climatic, geological, tectonic, topo-hydrological, and
anthropogenic drivers, as landslide-controlling factors were selected and sieved through the variance
inflation factor test. Ultimately, by implementing the above-mentioned data mining techniques, the
most important factors in the modeling process, as well as the highly susceptible locations in the study
area, were introduced.
Results and Discussion
Evaluating the learning capability, both the random forest and maximum entropy models with the
respective area under the receiver operating characteristic curve (AUROC) values of 0.923 and 0.91,
showed almost identical fitting abilities. However, getting to the validation stage, it was found that the
random forest with the AUROC value of 0.9 clearly outperforms maximum entropy (AUROC= 0.85)
in terms of prediction power and generalization capacity.
Hence, the random forest was suggested as a better-performing model for landslide susceptibility
mapping in the Owghan watershed, compared to its counterpart. About 10% of the study area falls into
high and very high landslide susceptibility zones. Furthermore, five landslide-controlling factors
including rainfall, normalized difference vegetation index, height above the nearest drainage,
lithological formation, and proximity to roads have been found to be the most significant factors
contributing to landslide occurrence in the study area. Additionally, the results attest that announcing
the Safiabad village as a landslide-prone area by the authorities is technically sound and evacuating the
residents to a new place has been a right decision; however, some parts of the newly inhabited area
shows landslide predisposing patterns which can lead to a higher susceptibility of the area to landslide
occurrence in the future.
Conclusion
Scrutinizing the results of random forest model revealed that a combination of natural factors (intense
rainfall, bare lands, susceptible lithological formations, and topo-hydrological mechanisms) and
anthropogenic interferences (tillage parallel to slope length/perpendicular to contour lines and
unprincipled road construction) are synergistically responsible for landslide occurrence in the Owghan
Watershed. On the other hand, announcing the Safiabad village as a critical landslide-prone area seems
to be a wise decision, although the newly inhabited place seems to be selected merely based on having
a suitable slope steepness (i.e., almost flat) and being accessible through several connecting routes,
while the enhanced conservation tillage methods have not been applied to the selected site and adjacent
areas. The latter, according to our inferences, can trigger a crisis in a larger extent. Moreover, owing to
the presence of other landslide predisposing factors in the new residential site, safe areas should be
pointed out and announced by adopting a holistic view on the entire influential and predisposing
conditions for landslide occurrence.