Related references
Note: Only part of the references are listed.Comparisons of Convolutional Neural Network and Other Machine Learning Methods in Landslide Susceptibility Assessment: A Case Study in Pingwu
Ziyu Jiang et al.
REMOTE SENSING (2023)
Assessing the effectiveness of alternative landslide partitioning in machine learning methods for landslide prediction in the complex Himalayan terrain
Muhammad Tayyib Riaz et al.
PROGRESS IN PHYSICAL GEOGRAPHY-EARTH AND ENVIRONMENT (2023)
Insights into geospatial heterogeneity of landslide susceptibility based on the SHAP-XGBoost model
Junyi Zhang et al.
JOURNAL OF ENVIRONMENTAL MANAGEMENT (2023)
A LightGBM-based landslide susceptibility model considering the uncertainty of non-landslide samples
Deliang Sun et al.
GEOMATICS NATURAL HAZARDS & RISK (2023)
Assessment of a New Solar Radiation Nowcasting Method Based on FY-4A Satellite Imagery, the McClear Model and SHapley Additive exPlanations (SHAP)
Dongyu Jia et al.
REMOTE SENSING (2023)
Pine wilt disease detection in high-resolution UAV images using object-oriented classification
Zhao Sun et al.
JOURNAL OF FORESTRY RESEARCH (2022)
Advanced hyperparameter optimization for improved spatial prediction of shallow landslides using extreme gradient boosting (XGBoost)
Taskin Kavzoglu et al.
BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT (2022)
Convolutional neural network (CNN) with metaheuristic optimization algorithms for landslide susceptibility mapping in Icheon, South Korea
Wahyu Luqmanul Hakim et al.
JOURNAL OF ENVIRONMENTAL MANAGEMENT (2022)
Landslide Susceptibility Mapping Using Machine Learning Algorithm Validated by Persistent Scatterer In-SAR Technique
Muhammad Afaq Hussain et al.
SENSORS (2022)
Landslide Extraction Using Mask R-CNN with Background-Enhancement Method
Ruilin Yang et al.
REMOTE SENSING (2022)
Evaluation of neural network models for landslide susceptibility assessment
Yaning Yi et al.
INTERNATIONAL JOURNAL OF DIGITAL EARTH (2022)
Earthquake-Induced Landslide Susceptibility Assessment Using a Novel Model Based on Gradient Boosting Machine Learning and Class Balancing Methods
Shuhao Zhang et al.
REMOTE SENSING (2022)
Metaheuristic-based support vector regression for landslide displacement prediction: a comparative study
Junwei Ma et al.
LANDSLIDES (2022)
Rapid prediction of landslide dam stability considering the missing data using XGBoost algorithm
Ning Shi et al.
LANDSLIDES (2022)
Automatic Remote Sensing Identification of Co-Seismic Landslides Using Deep Learning Methods
Dongdong Pang et al.
FORESTS (2022)
Exploring aspects affecting the predicted capacity of landslide susceptibility based on machine learning technology
Qiang Liu et al.
GEOCARTO INTERNATIONAL (2022)
Explainable step-wise binary classification for the susceptibility assessment of geo-hydrological hazards
Omer Ekmekcioglu et al.
CATENA (2022)
Deep Residual Learning for Image Recognition: A Survey
Muhammad Shafiq et al.
APPLIED SCIENCES-BASEL (2022)
Predictive Performances of Ensemble Machine Learning Algorithms in Landslide Susceptibility Mapping Using Random Forest, Extreme Gradient Boosting (XGBoost) and Natural Gradient Boosting (NGBoost)
Taskin Kavzoglu et al.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING (2022)
Landslide identification using machine learning
Haojie Wang et al.
GEOSCIENCE FRONTIERS (2021)
A comparative analysis of gradient boosting algorithms
Candice Bentejac et al.
ARTIFICIAL INTELLIGENCE REVIEW (2021)
Hybrids of Support Vector Regression with Grey Wolf Optimizer and Firefly Algorithm for Spatial Prediction of Landslide Susceptibility
Ru Liu et al.
REMOTE SENSING (2021)
A Meta-Learning Approach of Optimisation for Spatial Prediction of Landslides
Biswajeet Pradhan et al.
REMOTE SENSING (2021)
Comparison of multi-criteria and artificial intelligence models for land-subsidence susceptibility zonation
Alireza Arabameri et al.
JOURNAL OF ENVIRONMENTAL MANAGEMENT (2021)
Landslide Detection Mapping Employing CNN, ResNet, and DenseNet in the Three Gorges Reservoir, China
Tong Liu et al.
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING (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)
Application of alternating decision tree with AdaBoost and bagging ensembles for landslide susceptibility mapping
Yanli Wu et al.
CATENA (2020)
Parallel Regional Segmentation Method of High-Resolution Remote Sensing Image Based on Minimum Spanning Tree
Wenjie Lin et al.
REMOTE SENSING (2020)
Landslides Information Extraction Using Object-Oriented Image Analysis Paradigm Based on Deep Learning and Transfer Learning
Heng Lu et al.
REMOTE SENSING (2020)
Predicting Hard Rock Pillar Stability Using GBDT, XGBoost, and LightGBM Algorithms
Weizhang Liang et al.
MATHEMATICS (2020)
Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping
Zhice Fang et al.
COMPUTERS & GEOSCIENCES (2020)
Landslide Susceptibility Mapping Using Machine Learning Algorithms and Remote Sensing Data in a Tropical Environment
Viet-Ha Nhu et al.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH (2020)
Novel Credal Decision Tree-Based Ensemble Approaches for Predicting the Landslide Susceptibility
Alireza Arabameri et al.
REMOTE SENSING (2020)
Rainfall Induced Landslide Susceptibility Mapping Based on Bayesian Optimized Random Forest and Gradient Boosting Decision Tree Models-A Case Study of Shuicheng County, China
Guangzhi Rong et al.
WATER (2020)
Landslides Susceptibility Assessment Based on GIS Statistical Bivariate Analysis in the Hills Surrounding a Metropolitan Area
Paul Sestras et al.
SUSTAINABILITY (2019)
Detecting and monitoring long-term landslides in urbanized areas with nighttime light data and multi-seasonal Landsat imagery across Taiwan from 1998 to 2017
Tzu-Hsin Karen Chen et al.
REMOTE SENSING OF ENVIRONMENT (2019)
Debris Flow Susceptibility Mapping Using Machine-Learning Techniques in Shigatse Area, China
Yonghong Zhang et al.
REMOTE SENSING (2019)
The new landslide inventory of Tuscany (Italy) updated with PS-InSAR: geomorphological features and landslide distribution
A. Rosi et al.
LANDSLIDES (2018)
Review on landslide susceptibility mapping using support vector machines
Yu Huang et al.
CATENA (2018)
An Efficient Parallel Multi-Scale Segmentation Method for Remote Sensing Imagery
Haiyan Gu et al.
REMOTE SENSING (2018)
Remote Sensing of LandslidesA Review
Chaoying Zhao et al.
REMOTE SENSING (2018)
Efficient paddy field mapping using Landsat-8 imagery and object-based image analysis based on advanced fractel net evolution approach
Tengfei Su
GISCIENCE & REMOTE SENSING (2017)
A comparative assessment of GIS-based data mining models and a novel ensemble model in groundwater well potential mapping
Seyed Amir Naghibi et al.
JOURNAL OF HYDROLOGY (2017)
Landslide dam formation susceptibility analysis based on geomorphic features
Chien-Yuan Chen et al.
LANDSLIDES (2016)
GIS-based logistic regression for landslide susceptibility mapping of the Zhongxian segment in the Three Gorges area, China
Shi-Biao Bai et al.
GEOMORPHOLOGY (2010)
ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data
Lucian Dragut et al.
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE (2010)
Greedy function approximation: A gradient boosting machine
JH Friedman
ANNALS OF STATISTICS (2001)