Application of a two-step sampling strategy based on deep neural network for landslide susceptibility mapping
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Application of a two-step sampling strategy based on deep neural network for landslide susceptibility mapping
Authors
Keywords
-
Journal
Bulletin of Engineering Geology and the Environment
Volume 81, Issue 4, Pages -
Publisher
Springer Science and Business Media LLC
Online
2022-03-15
DOI
10.1007/s10064-022-02615-0
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Different sampling strategies for predicting landslide susceptibilities are deemed less consequential with deep learning
- (2020) Jie Dou et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models
- (2020) Zhilu Chang et al. Remote Sensing
- Effectiveness assessment of Keras based deep learning with different robust optimization algorithms for shallow landslide susceptibility mapping at tropical area
- (2020) Viet-Ha Nhu et al. CATENA
- 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
- Learning from positive and unlabeled data: a survey
- (2020) Jessa Bekker et al. MACHINE LEARNING
- Landslide Susceptibility Prediction Modeling Based on Remote Sensing and a Novel Deep Learning Algorithm of a Cascade-Parallel Recurrent Neural Network
- (2020) Li Zhu et al. SENSORS
- Influence of the mapping unit for regional landslide early warning systems: comparison between pixels and polygons in Catalonia (NE Spain)
- (2020) Rosa M. Palau et al. Landslides
- Landslide Susceptibility Mapping Using the Stacking Ensemble Machine Learning Method in Lushui, Southwest China
- (2020) Xudong Hu et al. Applied Sciences-Basel
- iPiDA-sHN: Identification of Piwi-interacting RNA-disease associations by selecting high quality negative samples
- (2020) Hang Wei et al. COMPUTATIONAL BIOLOGY AND CHEMISTRY
- A novel optimized repeatedly random undersampling for selecting negative samples: A case study in an SVM-based forest fire susceptibility assessment
- (2020) Xianzhe Tang et al. JOURNAL OF ENVIRONMENTAL MANAGEMENT
- Improving Spatial Agreement in Machine Learning-Based Landslide Susceptibility Mapping
- (2020) Mohammed Sarfaraz Gani Adnan et al. Remote Sensing
- 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 landslide susceptibility assessment using optimized hybrid machine learning methods
- (2020) Xi Chen et al. CATENA
- Deep learning and process understanding for data-driven Earth system science
- (2019) Markus Reichstein et al. NATURE
- Exploring the effects of the design and quantity of absence data on the performance of random forest-based landslide susceptibility mapping
- (2019) Haoyuan Hong et al. CATENA
- GIS-based logistic regression for rainfall-induced landslide susceptibility mapping under different grid sizes in Yueqing, Southeastern China
- (2019) Yu Zhao et al. ENGINEERING GEOLOGY
- An Improved Oversampling Algorithm Based on the Samples’ Selection Strategy for Classifying Imbalanced Data
- (2019) Wenhao Xie et al. MATHEMATICAL PROBLEMS IN ENGINEERING
- Urban waterlogging susceptibility assessment based on a PSO-SVM method using a novel repeatedly random sampling idea to select negative samples
- (2019) Xianzhe Tang et al. JOURNAL OF HYDROLOGY
- Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles
- (2019) Wei Chen et al. JOURNAL OF HYDROLOGY
- A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction
- (2019) Faming Huang et al. Landslides
- Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan
- (2019) Jie Dou et al. Landslides
- Large-Margin Label-Calibrated Support Vector Machines for Positive and Unlabeled Learning
- (2019) Chen Gong et al. IEEE Transactions on Neural Networks and Learning Systems
- GIS-based landslide susceptibility mapping for a part of the North Anatolian Fault Zone between Reşadiye and Koyulhisar (Turkey)
- (2019) Gökhan Demir CATENA
- Application of convolutional neural networks featuring Bayesian optimization for landslide susceptibility assessment
- (2019) Maher Ibrahim Sameen et al. CATENA
- Machine learning and fractal theory models for landslide susceptibility mapping: Case study from the Jinsha River Basin
- (2019) Qiao Hu et al. GEOMORPHOLOGY
- Large-scale urban point cloud labeling and reconstruction
- (2018) Liqiang Zhang et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- SMOTE for Learning from Imbalanced Data: Progress and Challenges, Marking the 15-year Anniversary
- (2018) Alberto Fernandez et al. JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
- A hybrid model using machine learning methods and GIS for potential rockfall source identification from airborne laser scanning data
- (2018) Ali Mutar Fanos et al. Landslides
- Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU
- (2018) Yu-Dong Zhang et al. Journal of Computational Science
- Positive and Unlabeled Learning for User Behavior Analysis based on Mobile Internet Traffic Data
- (2018) Ke Yu et al. IEEE Access
- Landslide Susceptibility Assessment at Mila Basin (Algeria): A Comparative Assessment of Prediction Capability of Advanced Machine Learning Methods
- (2018) Abdelaziz Merghadi et al. ISPRS International Journal of Geo-Information
- Comparison of the presence-only method and presence-absence method in landslide susceptibility mapping
- (2018) A-Xing Zhu et al. CATENA
- Guided Stochastic Gradient Descent Algorithm for inconsistent datasets
- (2018) Anuraganand Sharma APPLIED SOFT COMPUTING
- A shallow slide prediction model combining rainfall threshold warnings and shallow slide susceptibility in Busan, Korea
- (2018) Ananta Man Singh Pradhan et al. Landslides
- A Novel Hybrid Approach of Landslide Susceptibility Modeling Using Rotation Forest Ensemble and Different Base Classifiers
- (2018) Binh Thai Pham et al. Geocarto International
- Convolutional neural networks for hyperspectral image classification
- (2017) Shiqi Yu et al. NEUROCOMPUTING
- Flood susceptibility assessment using GIS-based support vector machine model with different kernel types
- (2015) Mahyat Shafapour Tehrany et al. CATENA
- 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
- Detecting positive and negative deceptive opinions using PU-learning
- (2015) Donato Hernández Fusilier et al. INFORMATION PROCESSING & MANAGEMENT
- 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
- Deep learning
- (2015) Yann LeCun et al. NATURE
- Deep learning in neural networks: An overview
- (2015) Jürgen Schmidhuber NEURAL NETWORKS
- Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia
- (2014) Zahrul Umar et al. CATENA
- A Remote Sensing-Based Approach for Debris-Flow Susceptibility Assessment Using Artificial Neural Networks and Logistic Regression Modeling
- (2014) Racha Elkadiri et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Extraction of Built-up Areas From Fully Polarimetric SAR Imagery Via PU Learning
- (2014) Wen Yang et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Flood susceptibility mapping using a novel ensemble weights-of-evidence and support vector machine models in GIS
- (2014) Mahyat Shafapour Tehrany et al. JOURNAL OF HYDROLOGY
- Assessment of rainfall-generated shallow landslide/debris-flow susceptibility and runout using a GIS-based approach: application to western Southern Alps of New Zealand
- (2014) Theodosios Kritikos et al. Landslides
- The influences of geological and land use settings on shallow landslides triggered by an intense rainfall event in a coastal terraced environment
- (2013) Andrea Cevasco 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
- Positive-unlabeled learning for disease gene identification
- (2012) Peng Yang et al. BIOINFORMATICS
- A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS
- (2012) Biswajeet Pradhan COMPUTERS & GEOSCIENCES
- Landslide inventory maps: New tools for an old problem
- (2012) Fausto Guzzetti et al. EARTH-SCIENCE REVIEWS
- GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China
- (2012) Chong Xu et al. GEOMORPHOLOGY
- 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
- Physical vulnerability assessment for alpine hazards: state of the art and future needs
- (2010) M. Papathoma-Köhle et al. NATURAL HAZARDS
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreFind the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
Search