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

Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance DOI
Abdelaziz Merghadi,

Ali P. Yunus,

Jie Dou

et al.

Earth-Science Reviews, Journal Year: 2020, Volume and Issue: 207, P. 103225 - 103225

Published: June 3, 2020

Language: Английский

Citations

825
Life and death of slow-moving landslides DOI
Pascal Lacroix, Alexander L. Handwerger, Grégory Bièvre

et al.

Nature Reviews Earth & Environment, Journal Year: 2020, Volume and Issue: 1(8), P. 404 - 419

Published: July 21, 2020

Language: Английский

Citations

292
Landslide monitoring using seismic ambient noise correlation: challenges and applications DOI
Mathieu Le Breton,

Noélie Bontemps,

Antoine Guillemot

et al.

Earth-Science Reviews, Journal Year: 2021, Volume and Issue: 216, P. 103518 - 103518

Published: Jan. 28, 2021

Language: Английский

Citations

99
Multiscale Study of Physical and Mechanical Properties of Sandstone in Three Gorges Reservoir Region Subjected to Cyclic Wetting–Drying of Yangtze River Water DOI
Wenmin Yao, Changdong Li, Hongbin Zhan

et al.

Rock Mechanics and Rock Engineering, Journal Year: 2020, Volume and Issue: 53(5), P. 2215 - 2231

Published: Jan. 1, 2020

Language: Английский

Citations

96
Recent technological and methodological advances for the investigation of landslide dams DOI

Xuanmei Fan,

Anja Dufresne, Jim Whiteley

et al.

Earth-Science Reviews, Journal Year: 2021, Volume and Issue: 218, P. 103646 - 103646

Published: April 22, 2021

Language: Английский

Citations

78
Review on the Geophysical and UAV-Based Methods Applied to Landslides DOI Creative Commons
Yawar Hussain, Romy Schlögel, Agnese Innocenti

et al.

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(18), P. 4564 - 4564

Published: Sept. 13, 2022

Landslides (LS) represent geomorphological processes that can induce changes over time in the physical, hydrogeological, and mechanical properties of involved materials. For geohazard assessment, variations these might be detected by a wide range non-intrusive techniques, which sometimes confusing due to their significant variation accuracy, suitability, coverage area, logistics, timescale, cost, integration potential; this paper reviews common geophysical methods (GM) categorized as Emitted Seismic Ambient Noise based proposes an integrated approach between them for improving landslide studies; level (among themselves) is important step ahead integrating data with remote sensing data. The aforementioned GMs help construct framework on physical may linked site characterization (e.g., its subsurface channel geometry, recharge pathways, rock fragments, mass flow rate, etc.) dynamics quantification rheology, saturation, fracture process, toe erosion, deformation marks spatiotemporally dependent geogenic pore-water pressure feedback through joint analysis series, displacement hydrometeorological measurements from ground, air space). A review use unmanned aerial vehicles (UAV) photogrammetry investigation landslides was also conducted highlight latest advancement discuss synergy UAV four possible broader areas: (i) survey planning, (ii) LS investigation, (iii) (iv) presentation results GIS environment. Additionally, endogenous source mechanisms lead appearance surface provide ground monitoring early warning systems. Further development area requires UAVs adopt more multispectral other advanced sensors where are one well climatic enable Artificial Intelligent prediction LS.

Language: Английский

Citations

48
Review of Landslide Monitoring Techniques With IoT Integration Opportunities DOI Creative Commons

T. Hemalatha,

Sebastian Uhlemann,

Reshma Reghunadh

et al.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Journal Year: 2022, Volume and Issue: 15, P. 5317 - 5338

Published: Jan. 1, 2022

With the advancements of technology in era big data and artificial intelligence, IoT (Internet Things) has a major role for purpose monitoring natural disasters like landslides. Landslides are catastrophic disaster worldwide that alter from terrain to terrain. In pursuit saving communities endangered by landslides, many techniques practiced. This paper is survey landslide adapted different parts world monitor unstable slopes. It provides glance into challenges opportunities integrating techniques, which explained briefly with emphasis on real-world case studies. Each technique presented regarding kind parameters, type landslides it can monitor, investigating phases, advantages, disadvantages, possibility integrate each techniques. also aims provide an overview general non-specialist field. The classified based (fall, topple, slide, spread, flow, slope deformation), velocity (slow, moderate, rapid), parameters (meteorological, geological, hydro-geological, physical, geophysical), phases (spatial, temporal) early-warning systems classification will serve as guideline (but not replacement expert advice) selecting appropriate classifications expressed through visual representations.

Language: Английский

Citations

46
A Review on Applications of Time-Lapse Electrical Resistivity Tomography Over the Last 30 Years : Perspectives for Mining Waste Monitoring DOI Creative Commons
Adrien Dimech, Lizhen Cheng, Michel Chouteau

et al.

Surveys in Geophysics, Journal Year: 2022, Volume and Issue: 43(6), P. 1699 - 1759

Published: Aug. 12, 2022

Abstract Mining operations generate large amounts of wastes which are usually stored into large-scale storage facilities pose major environmental concerns and must be properly monitored to manage the risk catastrophic failures also control generation contaminated mine drainage. In this context, non-invasive monitoring techniques such as time-lapse electrical resistivity tomography (TL-ERT) promising since they provide subsurface information that complements surface observations (walkover, aerial photogrammetry or remote sensing) traditional tools, often sample a tiny proportion mining waste facilities. The purposes review follows: (i) understand current state research on TL-ERT for various applications; (ii) create reference library future geoelectrical waste; (iii) identify areas development needs issue according our experience. This describes theoretical basis provides an overview applications developments over last 30 years from database 650 case studies, not limited (e.g., landslide, permafrost). particular, focuses ERT characterization 150 studies is used long-term autonomous geotechnical geochemical stability wastes. Potential challenges could emerge broader adoption discussed. considers recent advances in instrumentation, data acquisition, processing interpretation draws perspectives avenues help improve design accuracy geoelectric programs

Language: Английский

Citations

42
SBAS-InSAR based validated landslide susceptibility mapping along the Karakoram Highway: a case study of Gilgit-Baltistan, Pakistan DOI Creative Commons
Isma Kulsoom, Weihua Hua, Sadaqat Hussain

et al.

Scientific Reports, Journal Year: 2023, Volume and Issue: 13(1)

Published: Feb. 27, 2023

Geological settings of the Karakoram Highway (KKH) increase risk natural disasters, threatening its regular operations. Predicting landslides along KKH is challenging due to limitations in techniques, a environment, and data availability issues. This study uses machine learning (ML) models landslide inventory evaluate relationship between events their causative factors. For this, Extreme Gradient Boosting (XGBoost), Random Forest (RF), Artificial Neural Network (ANN), Naive Bayes (NB), K Nearest Neighbor (KNN) were used. A total 303 points used create an inventory, with 70% for training 30% testing. Susceptibility mapping Fourteen The area under curve (AUC) receiver operating characteristic (ROC) employed compare accuracy models. deformation generated susceptible regions was evaluated using SBAS-InSAR (Small-Baseline subset-Interferometric Synthetic Aperture Radar) technique. sensitive showed elevated line-of-sight (LOS) velocity. XGBoost technique produces superior Landslide map (LSM) region integration findings. improved LSM offers predictive modeling disaster mitigation gives theoretical direction management KKH.

Language: Английский

Citations

42
Geographically weighted neural network considering spatial heterogeneity for landslide susceptibility mapping: A case study of Yichang City, China DOI Open Access

Zhongguo Zhao,

Zhangyan Xu, Chuli Hu

et al.

CATENA, Journal Year: 2023, Volume and Issue: 234, P. 107590 - 107590

Published: Oct. 16, 2023

Language: Английский

Citations

23