Management of Landslides in a Rural–Urban Transition Zone Using Machine Learning Algorithms—A Case Study of a National Highway (NH-44), India, in the Rugged Himalayan Terrains

In Land
Peer-reviewed Article
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Landslides are critical natural disasters characterized by a downward movement of land
masses. As one of the deadliest types of disasters worldwide, they have a high death toll every year
and cause a large amount of economic damage. The transition between urban and rural areas is
characterized by highways, which, in rugged Himalayan terrain, have to be constructed by cutting
into the mountains, thereby destabilizing them and making them prone to landslides. This study was
conducted in one most landslide-prone regions of the entire Himalayan belt, i.e., National Highway
NH-44 (the Jammu–Srinagar stretch). The main objectives of this study are to understand the causes
behind the regular recurrence of the landslides in this region and propose a landslide early warning
system (LEWS) based on the most suitable machine learning algorithms among the four selected,
i.e., multiple linear regression, adaptive neuro-fuzzy inference system (ANFIS), random forest, and
decision tree. It was found that ANFIS and random forest outperformed the other proposed methods
with a substantial increase in overall accuracy. The LEWS model was developed using the land
system parameters that govern landslide occurrence, such as rainfall, soil moisture, distance to the
road and river, slope, land surface temperature (LST), and the built-up area (BUA) near the landslide
site. The developed LEWS was validated using various statistical error assessment tools such as the
root mean square error (RMSE), mean square error (MSE), confusion matrix, out-of-bag (OOB) error
estimation, and area under the receiver operating characteristic (ROC) curve (AUC). The outcomes of
this study can help to manage landslide hazards in the Himalayan urban–rural transition zones and
serve as a sample study for similar mountainous regions of the world.

Sheik Abdul
Suraj Kumar