Uncertainty-aware Estimation of California Bearing Ratio for Tropical Subgrade Soils Using Conformal Prediction

Machine Learning Forecasting of Pavement Temperature Across Multiple Horizons and Depths Benchmarked Against Persistence in a Continental Climate

Uncertainty Estimation in Predicting River Discharge Using Probabilistic Machine Learning and Conformal Prediction

Reliable streamflow forecasting with quantified uncertainty is essential for water resource management, flood mitigation, and climate adaptation in semi-arid regions. This research introduces a framework that combines three conformal prediction techniques, including Split Conformal Prediction (SplitCP), Cross Validatio...

An Uncertainty-Aware Hybrid Mathematical-Machine-Learning Model for Smart Irrigation Decision Support

Agriculture accounts for roughly 70% of global freshwater withdrawals, yet irrigation is still commonly scheduled reactively, with no forecast of where soil moisture is heading and no statement of confidence in that forecast. Data-driven models are accurate but opaque and point-valued; water-balance models are transpar...

A novel data - driven prediction method for frozen soil unconfined compressive strength considering uncertainty: Partial Bayesian Neural Network

Sensitivity-Driven Evolutionary Polynomial Regression for Tropical Subgrade Permanent Deformation

This study developed an interpretable Evolutionary Polynomial Regression framework to predict permanent deformation of tropical soil subgrades in semi-rigid pavement structures. The database combined repeated-load triaxial test parameters reported in Brazilian studies with controlled mechanistic-empirical simulations r...

Missingness-Aware Heterogeneous Ensemble Learning for Compression Index Prediction Across Predefined Incomplete-Input Scenarios and Unseen Marine-Clay Sites

Reliable estimation of the compression index (Cc) is essential for settlement assessment, yet geotechnical databases often contain incomplete soil-index measurements. This study developed a missingness-aware heterogeneous ensemble that combined masked variables with binary availability indicators to predict Cc across e...