AI SearchEvaluation of factors influencing soil-water infiltration behavior using machine learning approach
Napattarapong Kaenpuek
Evaluation of factors influencing soil-water infiltration behavior using machine learning approach - Pathum Thani, Thailand : Asian Institute of Technology, 2025 - 164 leaves : ill.+ 1 online resource - Thesis ; no. GE-24-01 . - Asian Institute of Technology. Thesis ; no. GE-24-01 .
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Geotechnical and Earth Resources Engineering
Thesis (M. Eng.) - Asian Institute of Technology, 2025
Landslides pose significant threats to lives, infrastructure, and the environment, often triggered by rainfall-induced soil infiltration that reduces slope stability. Accurate prediction of soil-water infiltration under varying rainfall and slopeconditions is crucial for landslide risk mitigation. Traditional deterministic models, while physically rigorous, require detailed input parameters that are difficult to obtain in practice, whereas purely data-driven approaches may lack physical consistency. This study develops and evaluates a Physics-Informed Neural Network (PINN) framework that integrates the governing Richards equation with observational data to predict soil moisture profiles in unsaturated slopes subjected to rainfall. Laboratory experiments were conducted using a physical slope model filled with red clayey sand and white sandy clay under controlled rainfall intensities (10, 50, and 90mm/h) and slope angles (0°, 15°, and 30°). Soil properties, including soil-water characteristic curves, were determined experimentally, and Finite Element Method (FEM) simulations were calibrated against observed data. The PINN was first trained using observational data only and then extended to a hybrid strategy combining FEM and observations. Results show that the observation-only PINN captured the general infiltration trend but produced smoother, less accurate wetting fronts, particularly at greater depths. In contrast, the hybrid-trained PINN improved accuracy, capturing infiltration dynamics and wetting front propagation more effectively, with lower error metrics and better alignment with observations and FEM results. These findings indicate that, with denser sensor networks in the future, PINNs trained on richer observational data could further enhance prediction performance, supporting real-time landslide hazard assessment.
Landslide hazard analysis--Data processing
Soil science
Machine learning
Evaluation of factors influencing soil-water infiltration behavior using machine learning approach - Pathum Thani, Thailand : Asian Institute of Technology, 2025 - 164 leaves : ill.+ 1 online resource - Thesis ; no. GE-24-01 . - Asian Institute of Technology. Thesis ; no. GE-24-01 .
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Geotechnical and Earth Resources Engineering
Thesis (M. Eng.) - Asian Institute of Technology, 2025
Landslides pose significant threats to lives, infrastructure, and the environment, often triggered by rainfall-induced soil infiltration that reduces slope stability. Accurate prediction of soil-water infiltration under varying rainfall and slopeconditions is crucial for landslide risk mitigation. Traditional deterministic models, while physically rigorous, require detailed input parameters that are difficult to obtain in practice, whereas purely data-driven approaches may lack physical consistency. This study develops and evaluates a Physics-Informed Neural Network (PINN) framework that integrates the governing Richards equation with observational data to predict soil moisture profiles in unsaturated slopes subjected to rainfall. Laboratory experiments were conducted using a physical slope model filled with red clayey sand and white sandy clay under controlled rainfall intensities (10, 50, and 90mm/h) and slope angles (0°, 15°, and 30°). Soil properties, including soil-water characteristic curves, were determined experimentally, and Finite Element Method (FEM) simulations were calibrated against observed data. The PINN was first trained using observational data only and then extended to a hybrid strategy combining FEM and observations. Results show that the observation-only PINN captured the general infiltration trend but produced smoother, less accurate wetting fronts, particularly at greater depths. In contrast, the hybrid-trained PINN improved accuracy, capturing infiltration dynamics and wetting front propagation more effectively, with lower error metrics and better alignment with observations and FEM results. These findings indicate that, with denser sensor networks in the future, PINNs trained on richer observational data could further enhance prediction performance, supporting real-time landslide hazard assessment.
Landslide hazard analysis--Data processing
Soil science
Machine learning