| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
A coupled deep learning approach for shield moving performance prediction of underground tunnel construction
|
Lin, Song-Shun
|
|
|
123 |
C |
p. 125-139
|
artikel
|
| 2 |
A new method to promptly evaluate spatial earthquake probability mapping using an explainable artificial intelligence (XAI) model
|
Jena, Ratiranjan
|
|
|
123 |
C |
p. 54-67
|
artikel
|
| 3 |
A novel method using explainable artificial intelligence (XAI)-based Shapley Additive Explanations for spatial landslide prediction using Time-Series SAR dataset
|
Al-Najjar, Husam A.H.
|
|
|
123 |
C |
p. 107-124
|
artikel
|
| 4 |
Assessment of landslide susceptibility along mountain highways based on different machine learning algorithms and mapping units by hybrid factors screening and sample optimization
|
Sun, Deliang
|
|
|
123 |
C |
p. 89-106
|
artikel
|
| 5 |
Assessment of liquefaction-induced lateral spread using soft computing approaches
|
Chen, Zhixiong
|
|
|
123 |
C |
p. 265-279
|
artikel
|
| 6 |
BayLUP: A Bayesian framework for conditional random field simulation of the liquefaction-induced settlement considering statistical uncertainty and model error
|
Miao, Cong
|
|
|
123 |
C |
p. 140-163
|
artikel
|
| 7 |
Comparison of trend models for geotechnical spatial variability: Sparse Bayesian Learning vs. Gaussian Process Regression
|
Ching, Jianye
|
|
|
123 |
C |
p. 174-183
|
artikel
|
| 8 |
Data driven models: Introduction
|
Zhang, Wengang
|
|
|
123 |
C |
p. 1-2
|
artikel
|
| 9 |
Deep learning for magnitude prediction in earthquake early warning
|
Wang, Yanwei
|
|
|
123 |
C |
p. 164-173
|
artikel
|
| 10 |
Editorial Board (IFC)
|
|
|
|
123 |
C |
p. IFC
|
artikel
|
| 11 |
Efficient time-variant reliability analysis of Bazimen landslide in the Three Gorges Reservoir Area using XGBoost and LightGBM algorithms
|
Zhang, Wengang
|
|
|
123 |
C |
p. 41-53
|
artikel
|
| 12 |
Machine learning-based estimation of soil’s true air-entry value from GSD curves
|
Es-haghi, Mohammad Sadegh
|
|
|
123 |
C |
p. 280-292
|
artikel
|
| 13 |
Machine learning-based landslide susceptibility assessment with optimized ratio of landslide to non-landslide samples
|
Yang, Can
|
|
|
123 |
C |
p. 198-216
|
artikel
|
| 14 |
Machine learning powered high-resolution co-seismic landslide detection
|
Wang, Haojie
|
|
|
123 |
C |
p. 217-237
|
artikel
|
| 15 |
Prediction of wall deflection induced by braced excavation in spatially variable soils via convolutional neural network
|
Wu, Chongzhi
|
|
|
123 |
C |
p. 184-197
|
artikel
|
| 16 |
Quantification of model uncertainty and variability for landslide displacement prediction based on Monte Carlo simulation
|
Wang, Luqi
|
|
|
123 |
C |
p. 27-40
|
artikel
|
| 17 |
Safety analysis of a deep foundation ditch using deep learning methods
|
Hong, Chengyu
|
|
|
123 |
C |
p. 16-26
|
artikel
|
| 18 |
Sensitivity of the land surface hydrological cycle to human activities in China
|
Luo, Kaisheng
|
|
|
123 |
C |
p. 255-264
|
artikel
|
| 19 |
Support vector regression with heuristic optimization algorithms for predicting the ground surface displacement induced by EPB shield tunneling
|
Lu, Dechun
|
|
|
123 |
C |
p. 3-15
|
artikel
|
| 20 |
Transfer learning improves landslide susceptibility assessment
|
Wang, Haojie
|
|
|
123 |
C |
p. 238-254
|
artikel
|
| 21 |
Vulnerability assessment of drought in India: Insights from meteorological, hydrological, agricultural and socio-economic perspectives
|
Saha, Asish
|
|
|
123 |
C |
p. 68-88
|
artikel
|
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