03050nas a2200253 a 450000500170000000800410001703500150005810000270007324501030010026000670020330000430027049000270031350001540034050200590049452018950055365000470244865000170249565000210251270000340253370000480256771000570261581000590267285600650273120260818112726.0260209s20259999th u  ms t    000   eng d  a.b1247504x1 aNapattarapong Kaenpuek10aEvaluation of factors influencing soil-water infiltration behavior using machine learning approach  aPathum Thani, Thailand :bAsian Institute of Technology,c2025  a164 leaves :bill.+e1 online resource1 aThesis ;vno. GE-24-01  aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Geotechnical and Earth Resources Engineering  aThesis (M. Eng.) - Asian Institute of Technology, 2025  aLandslides 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. 0aLandslide hazard analysisxData processing 0aSoil science 0aMachine learning1 aChao, Kuo Chieh,eChairperson0 aAvirut Puttiwongrak,eExamination Committee2 aRoyal Thai Government Fellowship,eScholarship Donor2 aAsian Institute of Technology.tThesis ;vno. GE-24-01403Full-Textuhttp://203.159.5.9/ait-thesis/detail.php?q=B23569