Random forest and artificial neural networks in landslide susceptibility modeling: a case study of the Fão River Basin, Southern Brazil

Machine Learning for the Geosciences: Challenges and Opportunities

(2019) Anuj Karpatne et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

Landslide inventory of the Basilicata region (Southern Italy)

(2018) Maurizio Lazzari et al.   Journal of Maps

The assessment of landslide susceptibility mapping using random forest and decision tree methods in the Three Gorges Reservoir area, China

(2017) Kaixiang Zhang et al.   Environmental Earth Sciences

Characterization and quantification of path dependency in landslide susceptibility

(2017) Jalal Samia et al.   GEOMORPHOLOGY

Landslide susceptibility mapping using random forest and boosted tree models in Pyeong-Chang, Korea

(2017) Jeong-Cheol Kim et al.   Geocarto International

Machine learning in geosciences and remote sensing

(2016) David J. Lary et al.   Geoscience Frontiers

Evaluating machine learning and statistical prediction techniques for landslide susceptibility modeling

(2015) J.N. Goetz et al.   COMPUTERS & GEOSCIENCES

The topographic signature of a major typhoon

(2015) Chih-Ming Tseng et al.   EARTH SURFACE PROCESSES AND LANDFORMS

Simplifying artificial neural network models of river basin behaviour by an automated procedure for input variable selection

(2015) Guilherme G. Oliveira et al.   ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Spatial prediction models for shallow landslide hazards: a comparative assessment of the efficacy of support vector machines, artificial neural networks, kernel logistic regression, and logistic model tree

(2015) Dieu Tien Bui et al.   Landslides

Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia

(2015) Ahmed Mohamed Youssef et al.   Landslides

Landslide susceptibility, Peloponnese Peninsula in South Greece

(2014) C. Chalkias et al.   Journal of Maps

Comparative analysis of SHALSTAB and SINMAP for landslide susceptibility mapping in the Cunha River basin, southern Brazil

(2014) Gean Paulo Michel et al.   JOURNAL OF SOILS AND SEDIMENTS

Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives

(2014) Marco Scaioni et al.   Remote Sensing

Landslide susceptibility mapping at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms

(2013) Mohammad Zare et al.   Arabian Journal of Geosciences

Recommendations for the quantitative analysis of landslide risk

(2013) J. Corominas et al.   Bulletin of Engineering Geology and the Environment

Evaluation of prediction capability of the artificial neural networks for mapping landslide susceptibility in the Turbolo River catchment (northern Calabria, Italy)

(2013) Massimo Conforti et al.   CATENA

Machine Learning Feature Selection Methods for Landslide Susceptibility Mapping

(2013) Natan Micheletti et al.   Mathematical Geosciences

Automatic landslide detection from remote-sensing imagery using a scene classification method based on BoVW and pLSA

(2012) Gong Cheng et al.   INTERNATIONAL JOURNAL OF REMOTE SENSING

Plausibility test of conceptual soil maps using relief parameters

(2011) Markus Möller et al.   CATENA

Techniques for evaluating the performance of landslide susceptibility models

(2009) Paolo Frattini et al.   ENGINEERING GEOLOGY

Estimating temporal probability for landslide initiation along transportation routes based on rainfall thresholds

(2009) Pankaj Jaiswal et al.   GEOMORPHOLOGY

Artificial neural networks and cluster analysis in landslide susceptibility zonation

(2007) C. Melchiorre et al.   GEOMORPHOLOGY

Modelling the probability of occurrence of shallow landslides and channelized debris flows using GEOtop-FS

(2007) Silvia Simoni et al.   HYDROLOGICAL PROCESSES