Future of machine learning in geotechnics
KK Phoon, W Zhang - … : Assessment and Management of Risk for …, 2023 - Taylor & Francis
Machine learning (ML) is widely used in many industries, resulting in recent interests to
explore ML in geotechnical engineering. Past review papers focus mainly on ML algorithms …
explore ML in geotechnical engineering. Past review papers focus mainly on ML algorithms …
Iterative integration of deep learning in hybrid Earth surface system modelling
Earth system modelling (ESM) is essential for understanding past, present and future Earth
processes. Deep learning (DL), with the data-driven strength of neural networks, has …
processes. Deep learning (DL), with the data-driven strength of neural networks, has …
A survey of machine learning and deep learning in remote sensing of geological environment: Challenges, advances, and opportunities
Due to limited resources and environmental pollution, monitoring the geological
environment has become essential for many countries' sustainable development. As various …
environment has become essential for many countries' sustainable development. As various …
[HTML][HTML] A comprehensive comparison among metaheuristics (MHs) for geohazard modeling using machine learning: Insights from a case study of landslide …
Abstract Machine learning (ML) has been extensively applied to model geohazards, yielding
tremendous success. However, researchers and practitioners still face challenges in …
tremendous success. However, researchers and practitioners still face challenges in …
Metaheuristic-based support vector regression for landslide displacement prediction: A comparative study
Recently, integrated machine learning (ML) metaheuristic algorithms, such as the artificial
bee colony (ABC) algorithm, genetic algorithm (GA), gray wolf optimization (GWO) algorithm …
bee colony (ABC) algorithm, genetic algorithm (GA), gray wolf optimization (GWO) algorithm …
Deep learning methods for time-dependent reliability analysis of reservoir slopes in spatially variable soils
Abstract The Three Gorges Reservoir Area (TGRA) is one of the most important landslide-
prone regions in China, and rational stability evaluation of reservoir slopes in it is of great …
prone regions in China, and rational stability evaluation of reservoir slopes in it is of great …
[HTML][HTML] A systematic review of trustworthy artificial intelligence applications in natural disasters
AS Albahri, YL Khaleel, MA Habeeb, RD Ismael… - Computers and …, 2024 - Elsevier
Artificial intelligence (AI) holds significant promise for advancing natural disaster
management through the use of predictive models that analyze extensive datasets, identify …
management through the use of predictive models that analyze extensive datasets, identify …
[HTML][HTML] 基于优化负样本采样策略的梯度提升决策树与随机森林的汶川同震滑坡易发性评价
郭衍昊， 窦杰， 向子林， 马豪， 董傲男， 罗万祺 - 地质科技通报, 2024 - dzkjqb.cug.edu.cn
强震诱发的滑坡具有数量多, 分布广, 规模大等特点, 严重威胁人民生命财产安全.
滑坡易发性评价能够快速预测灾害空间分布, 对于减轻震后灾害的危险性具有重要意义 …
滑坡易发性评价能够快速预测灾害空间分布, 对于减轻震后灾害的危险性具有重要意义 …
A review of recent earthquake-induced landslides on the Tibetan Plateau
Earthquake-induced landslides, also called seismic landslides (SLs), are some of the most
catastrophic natural hazards on the Tibetan Plateau (TP). They have frequently caused …
catastrophic natural hazards on the Tibetan Plateau (TP). They have frequently caused …
Modelling landslide susceptibility prediction: a review and construction of semi-supervised imbalanced theory
Fully supervised machine learning models are widely applied for landslide susceptibility
prediction (LSP), mainly using landslide and non-landslide samples as output variables and …
prediction (LSP), mainly using landslide and non-landslide samples as output variables and …