Machine-Learning Models and Global Sensitivity Analyses to Explicitly Estimate Groundwater Presence Validated by Observed Dataset at K-NET in Japan

This study incorporates the comprehensively observed proxies of in situ geotechnical, geophysical, petrophysical, and lithological datasets to estimate groundwater presence. Two machine-learning approaches, random forest regression (RFR) and deep neural network (DNN), are applied. The constructed RFR and DNN models are validated using observed depths of groundwater levels at 772 K-NET sites in Japan. The RFR model exhibited effectiveness and robust performance compared to the poor-fitting performance of the DNN model and previous groundwater detection physical-based approaches. The RFR and DNN models yielded a remarkable 1:1 agreement between the observed and predicted groundwater levels at 733 and 470 K-NET sites, respectively. During the RFR training process, all datasets at the 772 K-NET sites were split into training, validating, and unseen testing datasets with the ratio set at 1:1:11. This k-fold cross-validation strategy demonstrates better-fitting performance for the RFR model. The contributions and interactions among the in situ observed proxies utilizing the variance-based global sensitivity analyses can be understood. The P-wave velocity and the standard penetration test values have exhibited prominent contributions among other proxies at groundwater depths. To apply the RFR model at any given site, reliable and detailed P- and S-wave velocity structures are crucial to building the needed source datasets.