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

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 3 ) 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 3 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 3 methodology. The results show that the M 3 methodology achieves 20% improvement in the model performance compared to existing methods, indicating its promising potential in geotechnical site characterization.