| Issue |
E3S Web Conf.
Volume 583, 2024
Innovative Technologies for Environmental Science and Energetics (ITESE-2024)
|
|
|---|---|---|
| Article Number | 01009 | |
| Number of page(s) | 7 | |
| Section | Geoinformatics, Mining Geology and Mineral Resources | |
| DOI | https://doi.org/10.1051/e3sconf/202458301009 | |
| Published online | 25 October 2024 | |
Machine learning estimation of rock masses displacement
1 Reshetnev Siberian State University of Science and Technology, Krasnoyarsk, Russia
2 Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, Moscow, Russia
3 Krasnoyarsk State Agrarian University, Krasnoyarsk state agrarian university, 660049, Krasnoyarsk, Russia
* Corresponding author: ilya-kleshko@mail.ru
This paper presents a comprehensive analysis of the factors affecting landslide occurrence in Iran based on a dataset containing information on more than 4000 landslide cases. Both natural (slope, height, rainfall, distance to rivers and faults) and anthropogenic (type of land use) factors were studied. A random forest model was used to predict landslide risk and assess the significance of various factors. The results show that the most significant factors are terrain slope, elevation and distance to water bodies and tectonic faults. These findings can be used to develop preventive measures and improve landslide risk management strategies in the region.
© The Authors, published by EDP Sciences, 2024
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