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<p>This study introduces an innovative application of a hybrid empirical-data-driven neural network (HEDDNN) for regression-based prediction of the air-entry value (AEV) in unsaturated soils, offering a novel approach to predicting soil hydraulic properties crucial for understanding water movement, rainfall infiltration, and groundwater recharge in hydrology and engineering geology. AEV, a critical parameter in unsaturated soil water migration, significantly influences processes such as soil erosion, surface runoff, landslide initiation, and groundwater recharge. A comprehensive database of 214 representative soil samples from diverse geological origins and soil types was constructed, ensuring broad model applicability. The HEDDNN model achieved remarkable predictive performance on the test dataset, with an <i>R</i><sup>2</sup> value of 0.980 and root mean square error (RMSE) of 2.294&#xa0;kPa, outperforming traditional machine learning models like LightGBM [light gradient-boosting machine] and support vector regression (SVR). Feature importance analysis identified fines content, initial water content, and plasticity index as key AEV predictors. These findings underscore AEV’s importance in modeling soil hydraulic behavior across diverse depositional environments and its impact on geological phenomena like slope stability and subsurface water flow. This dual-driven HEDDNN framework not only enhances AEV prediction accuracy but also bridges the gap between physics-based and data-driven modeling, offering a scalable and reliable solution for hydrological applications such as rainfall infiltration, groundwater recharge, and slope stability assessment.</p> Graphical Abstract <p></p>

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Hybrid Empirical-Data-Driven Neural Network for Predicting Air-Entry Value in Unsaturated Soils

  • 期刊论文

  • 发表日期：

    2025年9月13日

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  • Tianxing Ma,
  • Liangxu Shen,
  • Mingzhi Zhang,
  • Kun Luo,
  • Xiangqi Hu,
  • Zheyuan Jiang,
  • Zhixing Yang,
  • Yun Lin,
  • Kang Peng

摘要

This study introduces an innovative application of a hybrid empirical-data-driven neural network (HEDDNN) for regression-based prediction of the air-entry value (AEV) in unsaturated soils, offering a novel approach to predicting soil hydraulic properties crucial for understanding water movement, rainfall infiltration, and groundwater recharge in hydrology and engineering geology. AEV, a critical parameter in unsaturated soil water migration, significantly influences processes such as soil erosion, surface runoff, landslide initiation, and groundwater recharge. A comprehensive database of 214 representative soil samples from diverse geological origins and soil types was constructed, ensuring broad model applicability. The HEDDNN model achieved remarkable predictive performance on the test dataset, with an R2 value of 0.980 and root mean square error (RMSE) of 2.294 kPa, outperforming traditional machine learning models like LightGBM [light gradient-boosting machine] and support vector regression (SVR). Feature importance analysis identified fines content, initial water content, and plasticity index as key AEV predictors. These findings underscore AEV’s importance in modeling soil hydraulic behavior across diverse depositional environments and its impact on geological phenomena like slope stability and subsurface water flow. This dual-driven HEDDNN framework not only enhances AEV prediction accuracy but also bridges the gap between physics-based and data-driven modeling, offering a scalable and reliable solution for hydrological applications such as rainfall infiltration, groundwater recharge, and slope stability assessment.

Graphical Abstract