03449nas a2200361 a 4500 20260818112726.0 260209s20259999th u ms t 000 eng d .b1247504x AIT Thesis no.GE-24-01 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 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{u2019} 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 90{u202F}mm/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 Chao, Kuo Chieh, Chairperson Avirut Puttiwongrak, Examination Committee Royal Thai Government Fellowship, Scholarship Donor Asian Institute of Technology. Thesis ; no. GE-24-01 Full-Text http://203.159.5.9/ait-thesis/detail.php?q=B23569 .b1247504x mnarc a 260219 0 260218 m h a 0 mnarc 61 Barcode : - CREATED : 2026-09-02 RECORD # : i13571539 LPATRON : 0 LCHKIN : - # RENEWALS : 0 # OVERDUE : 0 IUSE3 : 0 TOT CHKOUT : 0 TOT RENEW : 0 40963 40963 0 0 0 0 MAIN MAIN mnarc 2026-08-18 0 AIT Thesis no.GE-24-01 2026-08-18 11:27:26 1 2026-08-18 61