Evaluating landslide hazard, vulnerability, and risk using machine learning; A case study from the Alaknanda Valley, NW Himalaya DOI
Yaspal Sundriyal, Sandeep Kumar,

Sameeksha Kaushik

et al.

Environment Development and Sustainability, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 5, 2024

Language: Английский

Groundwater Salinization in Coastal Regions and the Control Mechanisms: Insights for Sustainable Groundwater Development and Management DOI
Johnson C. Agbasi, Mahamuda Abu,

Chaitanya B. Pande

et al.

Springer hydrogeology, Journal Year: 2025, Volume and Issue: unknown, P. 165 - 191

Published: Jan. 1, 2025

Language: Английский

Citations

1
Riverbank Erosion and vulnerability – A study on the char dwellers of Assam, India DOI Creative Commons
Mrinal Saikia, Ratul Mahanta

Natural Hazards Research, Journal Year: 2023, Volume and Issue: 4(2), P. 274 - 287

Published: Nov. 11, 2023

The paper tries to analyze the impacts of erosion on livelihood and vulnerability statusof char dwellers Assam, India. study employs both quantitative qualitative methodologies, choosing one district from each Assam's agro-climatic zones across Brahmaputra valley as a representative state's regions. As tool, uses participatory rural appraisal (PRA) technique tool Vulnerability Uninsured Exposure Risk (VER) econometric model.394 households were surveyed through semi-structured schedule. For village selected for study, combined social-resource map was created using PRA method in order determine severity issue VER model is used empirically examine relationship between land well-being inhabitants. reveals that has serious, detrimental economic residents thereby make vulnerable. makes recommendations structural non-structural adaptation practices minimize effects livelihood.

Language: Английский

Citations

18
An integrated approach of machine learning and remote sensing for evaluating landslide hazards and risk hotspots, NW Himalaya DOI
Yaspal Sundriyal, Sandeep Kumar, Neha Chauhan

et al.

Remote Sensing Applications Society and Environment, Journal Year: 2024, Volume and Issue: 33, P. 101140 - 101140

Published: Jan. 1, 2024

Language: Английский

Citations

8
Urban Sprawl in the context of proximity factors using Shannon’s Entropy Index and fractal dimensions: a case of Lucknow DOI
Gaurav Kumar Mishra, Amit M. Deshmukh

Journal of Spatial Science, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 27

Published: April 9, 2024

This study employs Geomatics techniques, including Remote Sensing (RS) for satellite dataset acquisition and Geographic Information System (GIS) analysing Land-use Land-cover (LULC) patterns, to assess the nature of urban sprawl. Central this analysis are proximity Business District (CBD) major roads. The research utilises low- high-density measures distinguish varying characteristics sprawl, allowing prioritisation sustainable objectives across different city zones. applies Shannon's Entropy Index (SEI) Fractal Dimensions (FD) years 1991 2021, focusing on CBD roads proximity.

Language: Английский

Citations

6
Investigation of the Historical Trends and Variability of Rainfall Patterns during the March–May Season in Rwanda DOI Creative Commons
Constance Uwizewe, Jianping Li, Théogène Habumugisha

et al.

Atmosphere, Journal Year: 2024, Volume and Issue: 15(5), P. 609 - 609

Published: May 17, 2024

This study explores the spatiotemporal variability and determinants of rainfall patterns during March to May (MAM) season in Rwanda, incorporating an analysis teleconnections with oceanic–atmospheric indices over period 1983–2021. Utilizing Climate Hazards Group Infrared Precipitation Stations (CHIRPS) dataset, employs a set statistical tools including standardized anomalies, empirical orthogonal functions (EOF), Pearson correlation, Mann–Kendall (MK) trend test, Sen’s slope estimator dissect intricacies variability, trends, their association large-scale climatic drivers. The findings reveal distinct southwest northwest gradient across MK test signaling decline annual precipitation, particularly southwest. for MAM reveals general downtrend rainfall, attributed part Indian Ocean Sea surface temperatures (SSTs). Notably, leading EOF mode demonstrates unimodal pattern, explaining significant 51.19% total variance, underscoring pivotal role atmospheric dynamics moisture conveyance shaping seasonal rainfall. spatial correlation suggests modest linkage between Dipole, indicating that negative (positive) phases are likely result anomalously wet (dry) conditions Rwanda. comprehensive assessment highlights intricate interplay local global phenomena, offering valuable insights into meteorological underpinnings Rwanda’s critical season.

Language: Английский

Citations

5
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
Assessing landslide susceptibility based on the random forest model and multi-source heterogeneous data DOI Creative Commons

Mengxia Li,

Haiying Wang, Jinlong Chen

et al.

Ecological Indicators, Journal Year: 2024, Volume and Issue: 158, P. 111600 - 111600

Published: Jan. 1, 2024

Landslides pose significant threats to human lives and property. The usefulness of mapping susceptibility in predicting these events, by providing early warning implementing preventive measures, cannot be overstated. This study relies on the well-established grid management mechanism Dengfeng utilizes historical data collected from field surveys. Combining multi-source heterogeneous obtained aerospace technology, using random forest model feature selection, a landslide sensitivity assessment system based space-ground collaboration was constructed. categorized areas according their landslides probabilistic forecasts. Specifically, regions were classified into four categories: very low (landslide probabilities below 0.26), (0.26 0.38), moderate (0.38 0.50), high (above 0.50). resulted comprehensive map that rigorously analyzed assessed. findings indicate that: (1) high, moderate, low, for are 127.36, 527.49, 385.67, 184.03 km2, respectively, accounting 10.40 %, 43.08 31.49 15.03 % total area. (2) spatial distribution exhibits “multi-core clustering-radiate distribution” pattern, peripheral center. (3) is primarily influenced factors such as distance faults, elevation, rivers. has constructed can provide replicable research methods, scientific support disaster reduction prevention work, reduce casualties property losses caused landslides.

Language: Английский

Citations

4
Assessment of the effects of characterization methods selection on the landslide susceptibility: a comparison between logistic regression (LR), naive bayes (NB) and radial basis function network (RBF Network) DOI
Hui Shang,

Lixiang Su,

Yang Liu

et al.

Bulletin of Engineering Geology and the Environment, Journal Year: 2025, Volume and Issue: 84(3)

Published: Feb. 15, 2025

Language: Английский

Citations

0
Landslide Risk Assessment in a Century-Old Tea Plantation Range Following Monsoonal Extremes in the Western Ghats of Kerala, India DOI
K. Amal George, P. S. Sunil,

A. U. Anish

et al.

Anthropocene Science, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 21, 2025

Language: Английский

Citations

0
Pixel-Wise Feature Fusion in Gully Susceptibility: A Comparison of Feed-Forward Neural Networks and Ensemble (Voting, Stacking) Models DOI Creative Commons
Vincent E. Nwazelibe, Johnson C. Agbasi, Daniel A. Ayejoto

et al.

Journal of African Earth Sciences, Journal Year: 2025, Volume and Issue: unknown, P. 105633 - 105633

Published: March 1, 2025

Language: Английский

Citations

0