A Novel GIS-Based Random Forest Machine Algorithm for the Spatial Prediction of Shallow Landslide Susceptibility
出版年份 2020 全文链接
标题
A Novel GIS-Based Random Forest Machine Algorithm for the Spatial Prediction of Shallow Landslide Susceptibility
作者
关键词
-
出版物
Forests
Volume 11, Issue 1, Pages 118
出版商
MDPI AG
发表日期
2020-01-20
DOI
10.3390/f11010118
参考文献
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注意:仅列出部分参考文献,下载原文获取全部文献信息。- Assessment of advanced random forest and decision tree algorithms for modeling rainfall-induced landslide susceptibility in the Izu-Oshima Volcanic Island, Japan
- (2019) Jie Dou et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Landslide susceptibility mapping: a practitioner’s view
- (2019) G. J. Hearn et al. Bulletin of Engineering Geology and the Environment
- Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility
- (2019) Abolfazl Jaafari et al. CATENA
- Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China
- (2019) Yi Wang et al. SCIENCE OF THE TOTAL ENVIRONMENT
- A new intelligence approach based on GIS-based Multivariate Adaptive Regression Splines and metaheuristic optimization for predicting flash flood susceptible areas at high-frequency tropical typhoon area
- (2019) Dieu Tien Bui et al. JOURNAL OF HYDROLOGY
- A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area
- (2019) Dieu Tien Bui et al. SCIENCE OF THE TOTAL ENVIRONMENT
- The human cost of global warming: Deadly landslides and their triggers (1995–2014)
- (2019) Ubydul Haque et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Multi-Hazard Exposure Mapping Using Machine Learning Techniques: A Case Study from Iran
- (2019) Omid Rahmati et al. Remote Sensing
- Spatial prediction of shallow landslide using Bat algorithm optimized machine learning approach: A case study in Lang Son Province, Vietnam
- (2019) Dieu Tien Bui et al. ADVANCED ENGINEERING INFORMATICS
- Cloud detection algorithm using SVM with SWIR2 and tasseled cap applied to Landsat 8
- (2019) Pratik P. Joshi et al. International Journal of Applied Earth Observation and Geoinformation
- Snow avalanche hazard prediction using machine learning methods
- (2019) Bahram Choubin et al. JOURNAL OF HYDROLOGY
- Predicting uncertainty of machine learning models for modelling nitrate pollution of groundwater using quantile regression and UNEEC methods
- (2019) Omid Rahmati et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Novel ensembles of COPRAS multi-criteria decision-making with logistic regression, boosted regression tree, and random forest for spatial prediction of gully erosion susceptibility
- (2019) Alireza Arabameri et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Artificial neural network ensembles applied to the mapping of landslide susceptibility
- (2019) L. Bragagnolo et al. CATENA
- A similarity-based approach to sampling absence data for landslide susceptibility mapping using data-driven methods
- (2019) A-Xing Zhu et al. CATENA
- Earth fissure hazard prediction using machine learning models
- (2019) Bahram Choubin et al. ENVIRONMENTAL RESEARCH
- Spatial hazard assessment of the PM10 using machine learning models in Barcelona, Spain
- (2019) Bahram Choubin et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions
- (2019) Omid Rahmati et al. Remote Sensing
- Enhancing the accuracy of rainfall-induced landslide prediction along mountain roads with a GIS-based random forest classifier
- (2018) Viet-Hung Dang 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
- GIS-based landslide susceptibility evaluation using a novel hybrid integration approach of bivariate statistical based random forest method
- (2018) Wei Chen et al. CATENA
- Review on landslide susceptibility mapping using support vector machines
- (2018) Yu Huang et al. CATENA
- 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
- A review of statistically-based landslide susceptibility models
- (2018) Paola Reichenbach et al. EARTH-SCIENCE REVIEWS
- Comparison of GIS-based landslide susceptibility models using frequency ratio, logistic regression, and artificial neural network in a tertiary region of Ambon, Indonesia
- (2018) Aril Aditian et al. GEOMORPHOLOGY
- Image Processing–Based Classification of Asphalt Pavement Cracks Using Support Vector Machine Optimized by Artificial Bee Colony
- (2018) Nhat-Duc Hoang et al. JOURNAL OF COMPUTING IN CIVIL ENGINEERING
- Comparison and evaluation of landslide susceptibility maps obtained from weight of evidence, logistic regression, and artificial neural network models
- (2018) Christos Polykretis et al. NATURAL HAZARDS
- A Novel Hybrid Approach of Bayesian Logistic Regression and Its Ensembles for Landslide Susceptibility Assessment
- (2018) Mousa Abedini et al. Geocarto International
- Presenting logistic regression-based landslide susceptibility results
- (2018) Luigi Lombardo et al. ENGINEERING GEOLOGY
- Multistep Wind Speed and Wind Power Prediction Based on a Predictive Deep Belief Network and an Optimized Random Forest
- (2018) Zexian Sun et al. MATHEMATICAL PROBLEMS IN ENGINEERING
- Application of Ensemble-Based Machine Learning Models to Landslide Susceptibility Mapping
- (2018) Prima Kadavi et al. Remote Sensing
- A Novel Integrated Approach of Relevance Vector Machine Optimized by Imperialist Competitive Algorithm for Spatial Modeling of Shallow Landslides
- (2018) Dieu Tien Bui et al. Remote Sensing
- Landslide Susceptibility Mapping and Comparison Using Decision Tree Models: A Case Study of Jumunjin Area, Korea
- (2018) Sung-Jae Park et al. Remote Sensing
- An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines
- (2018) Bahram Choubin et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Comparative analysis of statistical methods for landslide susceptibility mapping in the Bostanlik District, Uzbekistan
- (2018) Mukhiddin Juliev et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Comparison of machine-learning techniques for landslide susceptibility mapping using two-level random sampling (2LRS) in Alakir catchment area, Antalya, Turkey
- (2017) Metehan Ada et al. NATURAL HAZARDS
- 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
- A Novel Hybrid Approach Based on Instance Based Learning Classifier and Rotation Forest Ensemble for Spatial Prediction of Rainfall-Induced Shallow Landslides using GIS
- (2017) Quang-Khanh Nguyen et al. Sustainability
- Predicting earthquake-induced soil liquefaction based on a hybridization of kernel Fisher discriminant analysis and a least squares support vector machine: a multi-dataset study
- (2016) Nhat-Duc Hoang et al. Bulletin of Engineering Geology and the Environment
- Parallelization of the TRIGRS model for rainfall-induced landslides using the message passing interface
- (2016) M. Alvioli et al. ENVIRONMENTAL MODELLING & SOFTWARE
- 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
- Slope Collapse Prediction Using Bayesian Framework with K-Nearest Neighbor Density Estimation: Case Study in Taiwan
- (2016) Min-Yuan Cheng et al. JOURNAL OF COMPUTING IN CIVIL ENGINEERING
- Flood susceptibility assessment using GIS-based support vector machine model with different kernel types
- (2015) Mahyat Shafapour Tehrany et al. CATENA
- Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy)
- (2015) Alessandro Trigila et al. GEOMORPHOLOGY
- A comparative study of landslide susceptibility maps using logistic regression, frequency ratio, decision tree, weights of evidence and artificial neural network
- (2015) Liang-Jie Wang et al. GEOSCIENCES JOURNAL
- Landslide susceptibility mapping by combining the analytical hierarchy process and weighted linear combination methods: a case study in the upper Lo River catchment (Vietnam)
- (2015) Le Quoc Hung et al. Landslides
- 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 assesssment in the Uttarakhand area (India) using GIS: a comparison study of prediction capability of naïve bayes, multilayer perceptron neural networks, and functional trees methods
- (2015) Binh Thai Pham et al. THEORETICAL AND APPLIED CLIMATOLOGY
- An advanced process-based distributed model for the investigation of rainfall-induced landslides: The effect of process representation and boundary conditions
- (2015) Grigorios G. Anagnostopoulos et al. WATER RESOURCES RESEARCH
- Modeling and Testing Landslide Hazard Using Decision Tree
- (2014) Mutasem Sh. Alkhasawneh et al. Journal of Applied Mathematics
- 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
- A fuzzy random forest
- (2010) Piero Bonissone et al. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING
- Landslide susceptibility mapping based on Support Vector Machine: A case study on natural slopes of Hong Kong, China
- (2008) X. Yao et al. GEOMORPHOLOGY
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