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

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 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.

Assessing Land Use/Land Cover Changes and Urban Heat Island Intensification: A Case Study of Kamrup Metropolitan District, Northeast India (2000–2032)

“…Collectively, these results indicate that the Kamrup Metro Region is on a trajectory towards increased urbanization, heightened temperatures, and amplified UHI effect. The potential impacts of this pathway are vast, encompassing changes in local climate, increased energy consumption, public health risks, and threats to biodiversity, among others [61][62][63].…”

Assessing Sustainable Ecotourism Opportunities in Western Rajasthan, India, through Advanced Geospatial Technologies

“…The analysis demonstrates that a substantial portion of the region, approximately 37.31% of the total area, falls within a significant potential zone, primarily concentrated in the districts of Bikaner and Jaisalmer, which are also highly favoured destinations among tourists. These results emphasize the importance of improving the development and maintenance of ecotourism sites in Western Rajasthan [48][49][50]. By implementing appropriate measures to enhance infrastructure, connectivity, and facilities in these areas, we can unlock immense possibilities for tourism-based activities [51,52].…”

Spatial and Temporal Analysis of Hydrological Modelling in the Beas Basin Using SWAT+ Model

“…However, it displays substantial variation due to the significant range in altitude. The upper reaches of the basin, home to the high-altitude glacial terrain, are subjected to a frigid alpine climate, with heavy snowfall commonly observed during winters [21]. This snow is a vital water source, melting and nourishing the river during the summer months.…”