Development and Assessment of GIS-Based Landslide Susceptibility Mapping Models Using ANN, Fuzzy-AHP, and MCDA in Darjeeling Himalayas, West Bengal, India DOI Creative Commons
Abhik Saha, Vasanta Govind Kumar Villuri, Ashutosh Bhardwaj

et al.

Land, Journal Year: 2022, Volume and Issue: 11(10), P. 1711 - 1711

Published: Oct. 2, 2022

Landslides, a natural hazard, can endanger human lives and gravely affect the environment. A landslide susceptibility map is required for managing, planning, mitigating landslides to reduce damage. Various approaches are used susceptibility, with varying degrees of efficacy depending on methodology utilized in research. An analytical hierarchy process (AHP), fuzzy-AHP, an artificial neural network (ANN) current study construct maps part Darjeeling Kurseong West Bengal, India. On inventory map, 114 sites were randomly split into training testing 70:30 ratio. Slope, aspect, profile curvature, drainage density, lineament geomorphology, soil texture, land use cover, lithology, rainfall as model inputs. The area under curve (AUC) was examine models. When tested validation, ANN prediction performed best, AUC 88.1%. values fuzzy-AHP AHP 86.1% 85.4%, respectively. According statistics, northeast eastern portions most vulnerable. This might help development by preventing economic losses.

Language: Английский

Rapid characterisation of landslide heterogeneity using unsupervised classification of electrical resistivity and seismic refraction surveys DOI Creative Commons
Jim Whiteley, Arnaud Watlet, Sebastian Uhlemann

et al.

Engineering Geology, Journal Year: 2021, Volume and Issue: 290, P. 106189 - 106189

Published: May 12, 2021

The characterisation of the subsurface a landslide is critical step in developing ground models that inform planned mitigation measures, remediation works or future early-warning instability. When failure may be imminent, time pressures on producing such great. Geoelectrical and seismic geophysical surveys are able to rapidly acquire volumetric data across large areas at slope-scale. However, analysis individual model derived from each survey typically undertaken isolation, robust, accurate interpretation highly dependent experience skills operator. We demonstrate machine learning process for constructing rapid reconnaissance model, by integrating several sources single objective manner. Firstly, we use topographic acquired UAV co-locate three Hollin Hill Landslide Observatory UK. inverted using joint 2D mesh, resulting set co-located resistivity, P-wave velocity S-wave velocity. Secondly, analyse relationships trends present between variables point mesh (resistivity, velocity, depth) identify correlations. Thirdly, Gaussian Mixture Model (GMM), form unsupervised learning, classify into cluster groups with similar ranges measurements. created probabilistically assigning group characterises heterogeneity materials based their properties, identifying major discontinuities site. Finally, compare results intrusive borehole data, which show good agreement spatial variations lithology. applicability integrated coupled simple time-critical situations minimal prior knowledge about subsurface.

Language: Английский

Citations

51
Advancing hydrological process understanding from long‐term resistivity monitoring systems DOI
Lee Slater, Andrew Binley

Wiley Interdisciplinary Reviews Water, Journal Year: 2021, Volume and Issue: 8(3)

Published: Feb. 8, 2021

Abstract Monitoring subsurface flow and transport processes over a wide range of spatiotemporal scales remains one the greatest challenges in hydrology. Electrical geophysical techniques have been implemented to noninvasively investigate broad hydrological processes. Recent advances instrumentation interpretational tools highlight emerging opportunities adopt long‐term resistivity monitoring (LTRM) improve understanding operating monthly decadal timescales that are not adequately captured short‐term data sets temporally aliased constructed from occasional reoccupation study site. The emergence LTRM as robust tool hydrology represents paradigm shift acquisition analysis, with now evolving into decision support technology. We describe theoretical basis for adopting noninvasive state variables multiple spatial higher temporal resolution than achieved periodic field Instrumentation developments facilitating autonomous at off grid sites discussed, along processing enhance information content inherent sets. Case studies diverse subdisciplines largely untapped potential provide beyond reach established tools. Future relating more widespread adoption LTRM, including addressing uncertainty interpretation, upscaling, computational, modeling needs critically discussed. This article is categorized under: Science Water (WCAA)

Language: Английский

Citations

45
Freeze-thaw induced landslides on grasslands in cold regions DOI
Jiahui Yang, Gao‐Lin Wu, Juying Jiao

et al.

CATENA, Journal Year: 2022, Volume and Issue: 219, P. 106650 - 106650

Published: Sept. 21, 2022

Language: Английский

Citations

31
A new method to detect changes in displacement rates of slow-moving landslides using InSAR time series DOI Creative Commons
Alexandra Urgilez Vinueza, Alexander L. Handwerger, Mark Bakker

et al.

Landslides, Journal Year: 2022, Volume and Issue: 19(9), P. 2233 - 2247

Published: June 14, 2022

Abstract Slow-moving landslides move downslope at velocities that range from mm year −1 to m . Such deformations can be measured using satellite-based synthetic aperture radar interferometry (InSAR). We developed a new method systematically detect and quantify accelerations decelerations of slowly deforming areas InSAR displacement time series. The series are filtered an outlier detector subsequently piecewise linear functions fitted identify changes in the rate (i.e., or decelerations). Grouped inventoried as indicators potential unstable areas. tested refined our high-quality dataset Mud Creek landslide, CA, USA. Our detects coincide with those previously detected by manual examination. Second, we region around Mazar dam reservoir Southeast Ecuador, where data were considerably lower quality. occurring during entire study period near upslope reservoir. Application results wealth information on dynamics surface hillslopes provides objective way rates. rates, their spatial variation, timing used physical behavior slow-moving slope for regional hazard assessment linking rates landslide causal triggering factors.

Language: Английский

Citations

30
Development and Assessment of GIS-Based Landslide Susceptibility Mapping Models Using ANN, Fuzzy-AHP, and MCDA in Darjeeling Himalayas, West Bengal, India DOI Creative Commons
Abhik Saha, Vasanta Govind Kumar Villuri, Ashutosh Bhardwaj

et al.

Land, Journal Year: 2022, Volume and Issue: 11(10), P. 1711 - 1711

Published: Oct. 2, 2022

Landslides, a natural hazard, can endanger human lives and gravely affect the environment. A landslide susceptibility map is required for managing, planning, mitigating landslides to reduce damage. Various approaches are used susceptibility, with varying degrees of efficacy depending on methodology utilized in research. An analytical hierarchy process (AHP), fuzzy-AHP, an artificial neural network (ANN) current study construct maps part Darjeeling Kurseong West Bengal, India. On inventory map, 114 sites were randomly split into training testing 70:30 ratio. Slope, aspect, profile curvature, drainage density, lineament geomorphology, soil texture, land use cover, lithology, rainfall as model inputs. The area under curve (AUC) was examine models. When tested validation, ANN prediction performed best, AUC 88.1%. values fuzzy-AHP AHP 86.1% 85.4%, respectively. According statistics, northeast eastern portions most vulnerable. This might help development by preventing economic losses.

Language: Английский

Citations

30