Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya, India

Landslide Susceptibility Evaluation and Management Using Different Machine Learning Methods in The Gallicash River Watershed, Iran

(2020) Alireza Arabameri et al.   Remote Sensing

Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment

(2020) Dieu Tien Bui et al.   CATENA

A spatially explicit deep learning neural network model for the prediction of landslide susceptibility

(2020) Dong Van Dao et al.   CATENA

Shallow Landslide Susceptibility Mapping by Random Forest Base Classifier and Its Ensembles in a Semi-Arid Region of Iran

(2020) Viet-Ha Nhu et al.   Forests

Evaluating the Performance of Individual and Novel Ensemble of Machine Learning and Statistical Models for Landslide Susceptibility Assessment at Rudraprayag District of Garhwal Himalaya

(2020) Sunil Saha et al.   Applied Sciences-Basel

Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping

(2020) Zhice Fang et al.   COMPUTERS & GEOSCIENCES

Optimization of Computational Intelligence Models for Landslide Susceptibility Evaluation

(2020) Xia Zhao et al.   Remote Sensing

Performance Evaluation of GIS-Based Artificial Intelligence Approaches for Landslide Susceptibility Modeling and Spatial Patterns Analysis

(2020) Xinxiang Lei et al.   ISPRS International Journal of Geo-Information

Spatial prediction of groundwater potential mapping based on convolutional neural network (CNN) and support vector regression (SVR)

(2020) Mahdi Panahi et al.   JOURNAL OF HYDROLOGY

Assessment of Landslide Susceptibility Combining Deep Learning with Semi-Supervised Learning in Jiaohe County, Jilin Province, China

(2020) Jingyu Yao et al.   Applied Sciences-Basel

GIS-based evaluation of landslide susceptibility using hybrid computational intelligence models

(2020) Wei Chen et al.   CATENA

Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection

(2019) Omid Ghorbanzadeh et al.   Remote Sensing

Hybrid Machine Learning Approaches for Landslide Susceptibility Modeling

(2019) Vu Nguyen et al.   Forests

Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China

(2019) Yi Wang et al.   SCIENCE OF THE TOTAL ENVIRONMENT

Landslide Susceptibility Mapping Based on Random Forest and Boosted Regression Tree Models, and a Comparison of Their Performance

(2019) Soyoung Park et al.   Applied Sciences-Basel

Uncertainties of prediction accuracy in shallow landslide modeling: Sample size and raster resolution

(2019) Ataollah Shirzadi et al.   CATENA

Gully erosion susceptibility assessment and management of hazard-prone areas in India using different machine learning algorithms

(2019) Amiya Gayen et al.   SCIENCE OF THE TOTAL ENVIRONMENT

Deep Learning for Classification of Hyperspectral Data: A Comparative Review

(2019) Nicolas Audebert et al.   IEEE Geoscience and Remote Sensing Magazine

Landslide susceptibility evaluating using artificial intelligence method in the Youfang district (China)

(2019) Haoyuan Hong et al.   Environmental Earth Sciences

Potential Landslide Vulnerability Zonation Using Integrated Analytic Hierarchy Process and GIS Technique of Upper Rangit Catchment Area, West Sikkim, India

(2019) Subodh Chandra Pal et al.   Journal of the Indian Society of Remote Sensing

Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment

(2019) Maher Ibrahim Sameen et al.   CATENA

Flood susceptibility mapping using convolutional neural network frameworks

(2019) Yi Wang et al.   JOURNAL OF HYDROLOGY

Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping

(2019) Yanli Wu et al.   CATENA

Spatial prediction of landslide susceptibility using data mining-based kernel logistic regression, naive Bayes and RBFNetwork models for the Long County area (China)

(2018) Wei Chen et al.   Bulletin of Engineering Geology and the Environment

A novel hybrid intelligent model of support vector machines and the MultiBoost ensemble for landslide susceptibility modeling

(2018) Binh Thai Pham et al.   Bulletin of Engineering Geology and the Environment

Landslide susceptibility mapping using J48 Decision Tree with AdaBoost, Bagging and Rotation Forest ensembles in the Guangchang area (China)

(2018) Haoyuan Hong et al.   CATENA

Landslide susceptibility modeling applying machine learning methods: A case study from Longju in the Three Gorges Reservoir area, China

(2018) Chao Zhou et al.   COMPUTERS & GEOSCIENCES

Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling

(2018) Wei Chen et al.   SCIENCE OF THE TOTAL ENVIRONMENT

Quantifying climatic controls on river network branching structure across scales

(2018) Sevil Ranjbar Moshfeghi et al.   WATER RESOURCES RESEARCH

Landslide susceptibility mapping using maximum entropy and support vector machine models along the highway corridor, Garhwal Himalaya

(2018) Vijendra Kumar Pandey et al.   Geocarto International

Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping

(2018) Prima Kadavi et al.   Remote Sensing

Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping

(2018) Ataollah Shirzadi et al.   SENSORS

A Novel Hybrid Approach of Landslide Susceptibility Modeling Using Rotation Forest Ensemble and Different Base Classifiers

(2018) Binh Thai Pham et al.   Geocarto International

Mapping earthquake-triggered landslide susceptibility by use of artificial neural network (ANN) models: an example of the 2013 Minxian (China) Mw 5.9 event

(2018) Yingying Tian et al.   Geomatics Natural Hazards & Risk

Landslide Susceptibility Modeling Based on GIS and Novel Bagging-Based Kernel Logistic Regression

(2018) Wei Chen et al.   Applied Sciences-Basel

Landslide susceptibility modeling using Reduced Error Pruning Trees and different ensemble techniques: Hybrid machine learning approaches

(2018) Binh Thai Pham et al.   CATENA

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

Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification

(2017) Justin Salamon et al.   IEEE SIGNAL PROCESSING LETTERS

Deep Recurrent Neural Networks for Hyperspectral Image Classification

(2017) Lichao Mou et al.   IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING

A new deep convolutional neural network for fast hyperspectral image classification

(2017) M.E. Paoletti et al.   ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

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

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

Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and artificial neural networks (ANN)

(2017) Bahareh Kalantar et al.   Geomatics Natural Hazards & Risk

GIS-based landslide susceptibility modelling: a comparative assessment of kernel logistic regression, Naïve-Bayes tree, and alternating decision tree models

(2017) Wei Chen et al.   Geomatics Natural Hazards & Risk

GIS-based landslide spatial modeling in Ganzhou City, China

(2016) Haoyuan Hong et al.   Arabian Journal of Geosciences

A comparative study for landslide susceptibility mapping using GIS-based multi-criteria decision analysis (MCDA), logistic regression (LR) and association rule mining (ARM)

(2016) Arzu Erener et al.   ENGINEERING GEOLOGY

A comparative study of different machine learning methods for landslide susceptibility assessment: A case study of Uttarakhand area (India)

(2016) Binh Thai Pham et al.   ENVIRONMENTAL MODELLING & SOFTWARE

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

(2016) Hoo-Chang Shin et al.   IEEE TRANSACTIONS ON MEDICAL IMAGING

Spatial prediction of rainfall-induced shallow landslides using hybrid integration approach of Least-Squares Support Vector Machines and differential evolution optimization: a case study in Central Vietnam

(2016) Dieu Tien Bui et al.   International Journal of Digital Earth

Landslide susceptibility mapping by comparing weight of evidence, fuzzy logic, and frequency ratio methods

(2016) V. Vakhshoori et al.   Geomatics Natural Hazards & Risk

Modification of seed cell sampling strategy for landslide susceptibility mapping: an application from the Eastern part of the Gallipoli Peninsula (Canakkale, Turkey)

(2015) Gulseren Dagdelenler et al.   Bulletin of Engineering Geology and the Environment

Spatial prediction of landslide hazard at the Yihuang area (China) using two-class kernel logistic regression, alternating decision tree and support vector machines

(2015) Haoyuan Hong et al.   CATENA

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 impacts in Germany: A historical and socioeconomic perspective

(2015) Martin Klose 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

An integrated artificial neural network model for the landslide susceptibility assessment of Osado Island, Japan

(2015) Jie Dou et al.   NATURAL HAZARDS

Deep learning

(2015) Yann LeCun et al.   NATURE

Deep Learning-Based Classification of Hyperspectral Data

(2014) Yushi Chen et al.   IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Rule-based semi-automated approach for the detection of landslides induced by 18 September 2011 Sikkim, Himalaya, earthquake using IRS LISS3 satellite images

(2014) Sajad Siyahghalati et al.   Geomatics Natural Hazards & Risk

Rainfall event-based landslide susceptibility zonation mapping

(2013) Netra Prakash Bhandary et al.   NATURAL HAZARDS

A novel hybrid evidential belief function-based fuzzy logic model in spatial prediction of rainfall-induced shallow landslides in the Lang Son city area (Vietnam)

(2013) Dieu Tien Bui et al.   Geomatics Natural Hazards & Risk

Global patterns of loss of life from landslides

(2012) David Petley   GEOLOGY

Deep Neural Networks for Acoustic Modeling in Speech Recognition: The Shared Views of Four Research Groups

(2012) Geoffrey Hinton et al.   IEEE SIGNAL PROCESSING MAGAZINE

Application of frequency ratio and likelihood ratio model for geo-spatial modelling of landslide hazard vulnerability assessment and zonation: a case study from the Sikkim Himalayas in India

(2012) L.P. Sharma et al.   Geocarto International

Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression

(2011) Dieu Tien Bui et al.   NATURAL HAZARDS

Implementation of reconstructed geomorphologic units in landslide susceptibility mapping: the Melen Gorge (NW Turkey)

(2008) T. Gorum et al.   NATURAL HAZARDS