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Predicting geological interfaces using stacking ensemble learning with multi-scale features.
- Source :
- Canadian Geotechnical Journal; Jul2023, Vol. 60 Issue 7, p1036-1054, 19p
- Publication Year :
- 2023
-
Abstract
- Understanding the variation of geological interfaces plays a crucial role in the analysis and design of infrastructure systems. Generally, there are two classes of techniques for predicting geological interfaces, for example, interpolation/regression-based techniques and machine-learning-based techniques. In this paper, a Multi-scale Meta-learning Model (M<superscript>3</superscript>) methodology is proposed. The new methodology improves the current state-of-the-art techniques by fusing two levels of information: (i) generic characteristics of the sampling locations, for example, coordinates, and (ii) location-specific characteristics, for example, local-scale predictions. The implementation starts from using an array of classic interpolation/regression-based techniques as base learners to provide first-level predictions at a local scale. These predictions are then combined with generic characteristics to train a meta-learner following the stacking ensemble learning framework. In this manner, the location-specific information from the base learners can be simultaneously considered with the generic information in the training process. The variation of rockhead elevation is predicted using the M<superscript>3</superscript> methodology and a comprehensive borehole dataset in Singapore. A detailed comparative study involving several existing methods is also carried out to rigorously validate the M<superscript>3</superscript> methodology. The results show that the M<superscript>3</superscript> methodology achieves 20% improvement in the model performance compared to existing methods, indicating its promising potential in geotechnical site characterization. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00083674
- Volume :
- 60
- Issue :
- 7
- Database :
- Complementary Index
- Journal :
- Canadian Geotechnical Journal
- Publication Type :
- Academic Journal
- Accession number :
- 164662722
- Full Text :
- https://doi.org/10.1139/cgj-2022-0365