This paper presents a novel machine learning approach backed by ensembling machine
learning algorithms to build landslide susceptibility maps. The results reveal that this …

The concept of leveraging the predictive capacity of predisposing factors for landslide
susceptibility (LS) modeling has been continuously improved in recent work focusing on …

Ensemble machine learning methods have been widely used for modeling landslide
susceptibility, but there has been no uniform ensemble method for this problem. The main …

Landslides pose significant impact on human life and society such as loss of livelihood,
destruction of infrastructure, and damage to natural resources around the world. Due to …

We prepared a landslide susceptibility map for the Sarkhoon watershed, Chaharmahal-w-
bakhtiari, Iran, using novel ensemble artificial intelligence approaches. A classifier of …

Landslide susceptibility assessment (LSA) based on machine learning methods has been
widely used in landslide geological hazard management and research. However, the …

Landslides are one of the most devastating natural hazards causing huge loss of life and
damage to properties and infrastructures and adversely affecting the socioeconomy of the …

Landslides frequently occur because of natural or human factors. Landslides cause huge
losses to the economy as well as human beings every year around the globe. Landslide …

Landslide susceptibility mapping is still an ongoing requirement for variety of applications
such as land use management plans. The central objective of the present research was to …

Landslide susceptibility maps are vital tools used by decision-makers to adopt mitigation
strategies for future calamities. In this context, research on landslide susceptibility modelling …