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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

Authors :
Mohsin Fayaz
Gowhar Meraj
Sheik Abdul Khader
Majid Farooq
Shruti Kanga
Suraj Kumar Singh
Pankaj Kumar
Netrananda Sahu
Source :
Land, Vol 11, Iss 6, p 884 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

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 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.

Details

Language :
English
ISSN :
2073445X
Volume :
11
Issue :
6
Database :
Directory of Open Access Journals
Journal :
Land
Publication Type :
Academic Journal
Accession number :
edsdoj.1142b81f2aec4845a5d3ccb73cbf951e
Document Type :
article
Full Text :
https://doi.org/10.3390/land11060884