Forecasting methods of the landslides conditions and occurrence time in the village of Gush - Kert of the Shatoy district of the Chechen Republic

Introduction. The structure of the adopted methodology defines the object or its attributes that will need to be quantified. Cartographic, photographic and field data very rarely exist independently of each other, since most maps are created based on field research and remote sensing images, and the basic data for images are usually refined in the field. Materials and methods. The most effective way to study a landslide is its visual ground inspection and regular sampling of geo - materials, especially when it is necessary to take geo - material for subsequent physical - mechanical analysis. Results. As a result of the implementation of all these approaches, it becomes possible to develop a map of the Chechen Republic in its various aspects, compiled by combining various information values obtained for various categories of fundamental factors for the occurrence, jointly characterizing the prediction of conditions and time of occurrence of landslides in the territory of the Shatoysky district of the Chechen Republic. Resume. Based on the methodology of the landslide survey, it is initially necessary to carry out preliminary studies, which are conducted in order to identify the contours of the spread of landslide sites in the study area.

Authors

Publication Details

Journal
Sustainable Development of Mountain Territories
Published
2024-09-30
DOI
https://doi.org/10.21177/1998-4502-2024-16-3-1278-1290
Primary Topic
Marine and environmental studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Forecasting methods of the landslides conditions and occurrence time in the village of Gush - Kert of the Shatoy district of the Chechen Republic

Maret Madaeva, Kirill Vorobyov, Aleksandеr VOROBYOV, Anna Dzhamalueva
Sustainable Development of Mountain Territories
Marine and environmental studies
article

Forecasting methods of the landslides conditions and occurrence time in the village of Gush - Kert of the Shatoy district of the Chechen Republic

Maret Madaeva, Kirill Vorobyov, Aleksandеr VOROBYOV, Anna Dzhamalueva
article en

Abstract

Introduction. The structure of the adopted methodology defines the object or its attributes that will need to be quantified. Cartographic, photographic and field data very rarely exist independently of each other, since most maps are created based on field research and remote sensing images, and the basic data for images are usually refined in the field. Materials and methods. The most effective way to study a landslide is its visual ground inspection and regular sampling of geo - materials, especially when it is necessary to take geo - material for subsequent physical - mechanical analysis. Results. As a result of the implementation of all these approaches, it becomes possible to develop a map of the Chechen Republic in its various aspects, compiled by combining various information values obtained for various categories of fundamental factors for the occurrence, jointly characterizing the prediction of conditions and time of occurrence of landslides in the territory of the Shatoysky district of the Chechen Republic. Resume. Based on the methodology of the landslide survey, it is initially necessary to carry out preliminary studies, which are conducted in order to identify the contours of the spread of landslide sites in the study area.

Sustainable Development of Mountain TerritoriesVol. 16(3)
Openalex Percentile: Top 75%
Marine and environmental studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.