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
Mohsin Fayaz,
Gowhar Meraj,
Sheik Abdul Khader,
Majid Farooq,
Shruti Kanga,
Suraj Kumar Singh,
Pankaj Kumar and
Netrananda Sahu
Additional contact information
Mohsin Fayaz: Department of Computer Applications, B. S. Abdur Rahman Crescent Institute of Science and Technology, Chennai 600048, India
Gowhar Meraj: Department of Ecology, Environment & Remote Sensing, Government of Jammu & Kashmir, SDA Colony Bemina, Srinagar 190018, India
Sheik Abdul Khader: Department of Computer Applications, B. S. Abdur Rahman Crescent Institute of Science and Technology, Chennai 600048, India
Majid Farooq: Department of Ecology, Environment & Remote Sensing, Government of Jammu & Kashmir, SDA Colony Bemina, Srinagar 190018, India
Shruti Kanga: Centre for Climate Change & Water Research (C3WR), Suresh Gyan Vihar University, Jaipur 302017, India
Suraj Kumar Singh: Centre for Sustainable Development, Suresh Gyan Vihar University, Jaipur 302017, India
Pankaj Kumar: Institute for Global Environmental Strategies, Hayama 240-0115, Japan
Netrananda Sahu: Department of Geography, Delhi School of Economics, University of Delhi, Delhi 110007, India
Land, 2022, vol. 11, issue 6, 1-27
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.
Keywords: hazards; early warning system; LST; urban–rural fringes; machine learning; ANFIS; random forest; decision tree (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
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