Multifidelity-based Gaussian process for quasi-site-specific probabilistic prediction of soil properties

Abstract

Conventional empirical equations for soil properties prediction tend to be site-specific, exhibiting poor reliability and accuracy. Meanwhile, alternative data-driven methods require large datasets for training. To address these issues, this study proposed a novel multifidelity residual neural-network-based Gaussian process (MR-NNGP) modelling framework. A soil property low-fidelity (LF) prediction model is first trained using abundant LF data collected from worldwide sites for generating preliminary estimation. A high-fidelity (HF) model is subsequently trained on sparse HF data from the specific site of interest for calibrating the LF model to make quasi-site-specific predictions. An infinitely wide NN-inspired NNGP is adopted as the baseline algorithm for training LF and HF models. The compression index of clays is selected as an example to examine the capability of the proposed MR-NNGP. The results indicate that the compression index of clays can be well captured by MR-NNGP, exhibiting superior accuracy and reliability compared with one-shot training without using MR modelling and other baseline algorithms such as GP. The MR-NNGP framework alleviates data dependency and improves model performance through hierarchical modelling on relatively simple correlations using a superior algorithm. Unified LF data and efficient hyper-parameter optimization indicate the flexibility for broader applications in various sites worldwide.