Landslide Susceptibility Prediction Based on Positive Unlabeled Learning Coupled With Adaptive Sampling
Many studies consider landslide susceptibility prediction as a binary classification problem when using machine learning methods, which requires both landslide and nonlandslide samples for modeling. Nevertheless, there are only landslide and unlabeled areas in the real world, and directly considerin...
Enregistré dans:
Auteurs principaux: | Zhice Fang, Yi Wang, Ruiqing Niu, Ling Peng |
---|---|
Format: | article |
Langue: | EN |
Publié: |
IEEE
2021
|
Sujets: | |
Accès en ligne: | https://doaj.org/article/64d4a45d731d4ee68fb6e1b2e3b9327b |
Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires
-
Landslide Susceptibility Mapping Using Ant Colony Optimization Strategy and Deep Belief Network in Jiuzhaigou Region
par: Yibing Xiong, et autres
Publié: (2021) -
Landslide Detection Mapping Employing CNN, ResNet, and DenseNet in the Three Gorges Reservoir, China
par: Tong Liu, et autres
Publié: (2021) -
A Simulation Experiment on In-Situ Observation of Short-Wavelength Scale Dynamic Processes and Potential Applications to Wide-Swath Interferometric Altimetry Validation
par: Chen Wang, et autres
Publié: (2021) -
Cost-Sensitive Self-Paced Learning With Adaptive Regularization for Classification of Image Time Series
par: Hao Li, et autres
Publié: (2021) -
Soil Moisture Active/Passive (SMAP) L-Band Microwave Radiometer Post-Launch Calibration Revisit: Approach and Performance
par: Jinzheng Peng, et autres
Publié: (2021)