A new integrated approach for landslide data balancing and spatial prediction based on generative adversarial networks (GAN)
Landslide susceptibility mapping has significantly progressed with improvements in
machine learning techniques. However, the inventory/data imbalance (DI) problem remains …
machine learning techniques. However, the inventory/data imbalance (DI) problem remains …
Landslide susceptibility investigation for Idukki district of Kerala using regression analysis and machine learning
S Jones, AK Kasthurba, A Bhagyanathan… - Arabian Journal of …, 2021 - Springer
Kerala is the third most densely populated state in India, with 860 persons per square
kilometer. The uniqueness and diversity of the state's topology make it highly vulnerable to …
kilometer. The uniqueness and diversity of the state's topology make it highly vulnerable to …
SkyWords: An automatic keyword extraction system based on the skyline operator and semantic similarity
This study presents a hybrid keyword extraction method called SkyWords. It implements a
novel supervised step based on the skyline operator and the majority voting principle for …
novel supervised step based on the skyline operator and the majority voting principle for …
Assessing landslide susceptibility using improved machine learning methods and considering spatial heterogeneity for the Three Gorges Reservoir Area, China
When conducting susceptibility evaluation for study areas of special significance, especially
those with spatial heterogeneity of landslide development, it is easy to ignore the potential …
those with spatial heterogeneity of landslide development, it is easy to ignore the potential …
Exploring class imbalance with under-sampling, over-sampling, and hybrid sampling based on Mahalanobis distance for landslide susceptibility assessment: a case …
K Nam, J Kim, BG Chae - Geosciences Journal, 2024 - Springer
This study focuses on evaluating the performance of the resampling approach using under-
sampling, over-sampling, and hybrid sampling techniques in the random forest (RF) model …
sampling, over-sampling, and hybrid sampling techniques in the random forest (RF) model …
[图书][B] A novel approach by integrating physically and Machine leaning-based models for landslide susceptibility assessment
HAH Al-Najjar - 2023 - search.proquest.com
One of the most destructive natural hazards is landslide, and Landslide Susceptibility
Mapping (LSM) estimates the probability of the hazard in a region. Physical and Machine …
Mapping (LSM) estimates the probability of the hazard in a region. Physical and Machine …
Seismic Performance of Vertically Prestressed Semicircular Anti-Slide Piles Based on Deep Machine-learning Algorithms
Y Peng, T Wang, X Wang, L Kang - … of the First International Conference on …, 2024 - eudl.eu
Deep machine learning algorithm is a method involving technology, data analysis, statistics,
monitoring and so on. Its main goal is to extract, transfer, process and manage information …
monitoring and so on. Its main goal is to extract, transfer, process and manage information …
[PDF][PDF] H.; Pradhan, B.; Sarkar, R.; Beydoun, G.; Alamri
HA Al-Najjar - A. A New Integrated Approach for Landslide Data …, 2021 - researchgate.net
Landslide susceptibility mapping has significantly progressed with improvements in
machine learning techniques. However, the inventory/data imbalance (DI) problem remains …
machine learning techniques. However, the inventory/data imbalance (DI) problem remains …