Multi-hazard susceptibility mapping of landslides and earthquakes in Bhagirathi Valley region of Uttarakhand Himalaya, India DOI
Neha Gupta, Debi Prasanna Kanungo, Josodhir Das

et al.

Journal of Spatial Science, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 26

Published: Oct. 1, 2024

Language: Английский

Assessing classification system for landslide susceptibility using frequency ratio, analytical hierarchical process and geospatial technology mapping in Aizawl district, NE India DOI
Jonmenjoy Barman, Jayanta Das

Advances in Space Research, Journal Year: 2024, Volume and Issue: 74(3), P. 1197 - 1224

Published: May 10, 2024

Language: Английский

Citations

12
Comparing the effectiveness of landslide susceptibility mapping by using the Frequency ratio and hybrid MCDM models DOI Creative Commons
Jonmenjoy Barman, Syed Sadath Ali,

Teachersunday Nongrem

et al.

Results in Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 103205 - 103205

Published: Oct. 1, 2024

Language: Английский

Citations

5
Geospatial Assessment and Mapping Landslide Susceptibility for the Garo Hills Division, Meghalaya, India DOI Open Access
Naveen Badavath, Smrutirekha Sahoo

Geological Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 19, 2025

ABSTRACT Creating accurate and effective Landslide Susceptibility (LS) maps can aid disaster prevention mitigation efforts provide sufficient public safety. The primary aim of this study is to develop an LS map for the Garo Hills region in Meghalaya, India, using weight evidence (WoE), frequency ratio (FR), Shannon entropy (SE) methods. A comprehensive landslide inventory catalogued 98 events from 2000 2023 analysis, nine key geographical environmental parameters were prepared. Conducted multicollinearity correlation analysis identify mitigate collinearity issues between factors. model's performance was analysed through area under curve (AUC) value receiver operating characteristic (ROC) curves three recent landslides. results showed that FR method achieved highest accuracy, with successive rate (SRC) AUC predictive (PRC) values 0.860 0.940, respectively, classified susceptibility at sites as high, moderate, low. WoE effectively identified landslides site high very zones, achieving SRC PRC 0.844 0.915, respectively. SE robust predicting landslide‐prone areas, comparable other methods (0.913), though its (0.771) lower. Developed revealed zones account approximately 10% 3% area, predominantly near roads, steep slopes, higher elevations. information valuable civilians government authorities involved hazard monitoring management.

Language: Английский

Citations

0
Landslide susceptibility assessment in Tongguan District Anhui China using information value and certainty factor models DOI Creative Commons
Dan Ding, Yuting Wu,

Wu Tianzhen

et al.

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 10, 2025

The study of susceptibility to geological hazards is crucial not only for local risk assessment but also understanding global patterns in disaster-prone regions. Geological such as landslides and subsidence are a common threat worldwide, affecting millions people causing significant economic losses annually. Landslides major hazard the Tongling City, Tongguan District, Anhui, China, posing risks infrastructure human activity. This assesses using seven influencing factors, including elevation, slope, aspect, distance faults. Both information value certainty factor (CF) models were applied evaluate region's landslide susceptibility, resulting classification area into five levels. While primary focus, ground collapses observed, though much lesser extent. focuses on interest District. found that: (1) predominantly concentrated within 300 m faults along cut slopes adjacent mountain roads buildings. distribution indicates that both construction activities factors contributing frequent occurrence region; (2) proportion classified high-prone City significant, indicating need focused mitigation efforts. (3) CF can effectively region. under curve (AUC) receiver operating characteristic (ROC) curves used models' performance, with model demonstrating superior evaluation accuracy. emphasizes areas District susceptible landslides, offering critical insights strategies decision-making prevention, treatment, emergency response regions similar conditions.

Language: Английский

Citations

0
Landslide vulnerability mapping using GIS-based statistical model for sustainable ecosystem management in the Himalayan region of Teesta River basin, India DOI Creative Commons
Subodh Chandra Pal, Tanmoy Biswas,

S.K. Ghorai

et al.

Environmental Sciences Europe, Journal Year: 2025, Volume and Issue: 37(1)

Published: April 22, 2025

Language: Английский

Citations

0
Least cost path analysis for alternative road network assessment of landslide-prone NH-2, Mizoram, NE India DOI Creative Commons
Jonmenjoy Barman, Brototi Biswas, Jayanta Das

et al.

Geocarto International, Journal Year: 2025, Volume and Issue: 40(1)

Published: April 25, 2025

Language: Английский

Citations

0
The TOPSIS Method: Figuring the landslide susceptibility using Excel and GIS DOI Creative Commons
Jonmenjoy Barman, Brototi Biswas, Syed Sadath Ali

et al.

MethodsX, Journal Year: 2024, Volume and Issue: 13, P. 103005 - 103005

Published: Oct. 17, 2024

Language: Английский

Citations

2
Multi-hazard susceptibility mapping of landslides and earthquakes in Bhagirathi Valley region of Uttarakhand Himalaya, India DOI
Neha Gupta, Debi Prasanna Kanungo, Josodhir Das

et al.

Journal of Spatial Science, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 26

Published: Oct. 1, 2024

Language: Английский

Citations

1