Predicting geological interfaces using stacking ensemble learning with multi-scale features

Author:

Wang Ze Zhou1ORCID,Hu Yue1ORCID,Guo Xiangfeng1,He Xiaogang1,Kek Hardy Yide1,Ku Taeseo2ORCID,Goh Siang Huat1,Leung Chun Fai1ORCID

Affiliation:

1. Department of Civil & Environmental Engineering, National University of Singapore, 117 576, Singapore

2. Department of Civil & Environmental Engineering, Konkuk University, Seoul 05 029, South Korea

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 (M3) 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 M3 methodology and a comprehensive borehole dataset in Singapore. A detailed comparative study involving several existing methods is also carried out to rigorously validate the M3 methodology. The results show that the M3 methodology achieves 20% improvement in the model performance compared to existing methods, indicating its promising potential in geotechnical site characterization.

Publisher

Canadian Science Publishing

Subject

Civil and Structural Engineering,Geotechnical Engineering and Engineering Geology

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