Related references
Note: Only part of the references are listed.Performance evaluation of machine learning and statistical techniques for modelling landslide susceptibility with limited field data
Ashokan Laila Achu et al.
EARTH SCIENCE INFORMATICS (2023)
Landslide susceptibility modelling using hybrid bivariate statistical-based machine-learning method in a highland segment of Southern Western Ghats, India
A. L. Achu et al.
ENVIRONMENTAL EARTH SCIENCES (2022)
Global Landslide Forecasting System for Hazard Assessment and Situational Awareness
Sana Khan et al.
FRONTIERS IN EARTH SCIENCE (2022)
AReGeoDatHa: Apulian Regional GeoDatabase for geo-hydrological Hazards
Carmela Vennari et al.
JOURNAL OF ENVIRONMENTAL MANAGEMENT (2022)
A national landslide inventory for Denmark
Gregor Luetzenburg et al.
EARTH SYSTEM SCIENCE DATA (2022)
Preliminary analysis of a catastrophic landslide event on 6 August 2020 at Pettimudi, Kerala State, India
A. L. Achu et al.
LANDSLIDES (2021)
Auto-Pytorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL
Lucas Zimmer et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2021)
Data-Driven Landslide Nowcasting at the Global Scale
Thomas A. Stanley et al.
FRONTIERS IN EARTH SCIENCE (2021)
A comparison among fuzzy multi-criteria decision making, bivariate, multivariate and machine learning models in landslide susceptibility mapping
Quoc Bao Pham et al.
GEOMATICS NATURAL HAZARDS & RISK (2021)
The GEM Global Active Faults Database
Richard Styron et al.
EARTHQUAKE SPECTRA (2020)
Landslides susceptibility assessment by analytical network process: a case study for Kuttiyadi river basin (Western Ghats, southern India)
T. V. Swetha et al.
SN APPLIED SCIENCES (2020)
Using citizen science to expand the global map of landslides: Introducing the Cooperative Open Online Landslide Repository (COOLR)
Caroline S. Juang et al.
PLOS ONE (2019)
Landslide susceptibility hazard map in southwest Sweden using artificial neural network
Abbas Abbaszadeh Shahri et al.
CATENA (2019)
Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan
Jie Dou et al.
SCIENCE OF THE TOTAL ENVIRONMENT (2019)
Automated deep learning design for medical image classification by health-care professionals with no coding experience: a feasibility study
Livia Faes et al.
LANCET DIGITAL HEALTH (2019)
NAS-Unet: Neural Architecture Search for Medical Image Segmentation
Yu Weng et al.
IEEE ACCESS (2019)
Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia
Aril Aditian et al.
GEOMORPHOLOGY (2018)
Mapping landslide susceptibility and types using Random Forest
Khaled Taalab et al.
BIG EARTH DATA (2018)
Global fatal landslide occurrence from 2004 to 2016
Melanie J. Froude et al.
NATURAL HAZARDS AND EARTH SYSTEM SCIENCES (2018)
WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas
Stephen E. Fick et al.
INTERNATIONAL JOURNAL OF CLIMATOLOGY (2017)
New Zealand's National Landslide Database
Brenda Rosser et al.
LANDSLIDES (2017)
A heuristic approach to global landslide susceptibility mapping
Thomas Stanley et al.
NATURAL HAZARDS (2017)
Mapping landslide susceptibility using data-driven methods
J. L. Zezere et al.
SCIENCE OF THE TOTAL ENVIRONMENT (2017)
A novel hybrid artificial intelligence approach based on the rotation forest ensemble and naive Bayes tree classifiers for a landslide susceptibility assessment in Langao County, China
Wei Chen et al.
GEOMATICS NATURAL HAZARDS & RISK (2017)
Landslide susceptibility mapping on a global scale using the method of logistic regression
Le Lin et al.
NATURAL HAZARDS AND EARTH SYSTEM SCIENCES (2017)
Landslide susceptibility mapping using analytic hierarchy process and information value methods along a highway road section in Constantine, Algeria
Yacine Achour et al.
ARABIAN JOURNAL OF GEOSCIENCES (2017)
Comparison of a logistic regression and Naive Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size
Paraskevas Tsangaratos et al.
CATENA (2016)
A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM)
Arzu Erener et al.
ENGINEERING GEOLOGY (2016)
Landslide susceptibility mapping at Gongliu county, China using artificial neural network and weight of evidence models
Qiqing Wang et al.
GEOSCIENCES JOURNAL (2016)
Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset
I. Harris et al.
INTERNATIONAL JOURNAL OF CLIMATOLOGY (2014)
Landslide susceptibility mapping using GIS-based multi-criteria decision analysis, support vector machines, and logistic regression
Taskin Kavzoglu et al.
LANDSLIDES (2014)
Prediction of Classification of Rock Burst Risk Based on Genetic Algorithms with SVM
Peng Yahui et al.
MATERIALS SCIENCE, MECHANICAL ENGINEERING AND APPLIED RESEARCH (2014)
The new global lithological map database GLiM: A representation of rock properties at the Earth surface
Jens Hartmann et al.
GEOCHEMISTRY GEOPHYSICS GEOSYSTEMS (2012)
Landslide hazard zoning along Himalayan Kaghan Valley of Pakistan-by integration of GPS, GIS, and remote sensing technology
Tahir Ali Akbar et al.
LANDSLIDES (2011)
Landslide susceptibility assessment and factor effect analysis: backpropagation artificial neural networks and their comparison with frequency ratio and bivariate logistic regression modelling
Biswajeet Pradhan et al.
ENVIRONMENTAL MODELLING & SOFTWARE (2010)
Use of satellite remote sensing data in the mapping of global landslide susceptibility
Yang Hong et al.
NATURAL HAZARDS (2007)
Is prediction of future landslides possible with a GIS?
AG Fabbri et al.
NATURAL HAZARDS (2003)