Landslides in the Himalayas: Causes, Evolution, and Mitigation—A Case Study of National Highway 44, India

Fayaz, Mohsin; Khader, Sheik Abdul; Rafiq, Mohammd

doi:10.1007/978-3-030-89308-8_4

Cited by 3 publications

(1 citation statement)

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“…In contrast, others are covered with sedimentary rocks and sandstone granules, making these sites highly prone to rainfallinduced land failure [68]. All these factors make the region highly susceptible and prone to landslides [68,89,[100][101][102].…”

Section: Landslide Early Warning System (Lews) and Land System Proces...mentioning

confidence: 99%

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

Fayaz

Meraj²,

Khader

et al. 2022

Land

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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.

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Section: Landslide Early Warning System (Lews) and Land System Proces...mentioning

confidence: 99%