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035			_a.b1247504x
099		9	_aAIT Thesis no.GE-24-01
100	1		_aNapattarapong Kaenpuek
245	1	0	_aEvaluation of factors influencing soil-water infiltration behavior using machine learning approach
260			_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2025
300			_a164 leaves : _bill.+ _e1 online resource
490	1		_aThesis ; _vno. GE-24-01
500			_aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Geotechnical and Earth Resources Engineering
502			_aThesis (M. Eng.) - Asian Institute of Technology, 2025
520			_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.
650		0	_aLandslide hazard analysis _xData processing
650		0	_aSoil science
650		0	_aMachine learning
700	1		_aChao, Kuo Chieh, _eChairperson
700	0		_aAvirut Puttiwongrak, _eExamination Committee
710	2		_aRoyal Thai Government Fellowship, _eScholarship Donor
810	2		_aAsian Institute of Technology. _tThesis ; _vno. GE-24-01
856	4	0	_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B23569
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902			_a260219
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909			_aBarcode : - _bCREATED : 2026-09-02 _cRECORD # : i13571539 _dLPATRON : 0 _eLCHKIN : - _f# RENEWALS : 0 _g# OVERDUE : 0 _hIUSE3 : 0 _iTOT CHKOUT : 0 _jTOT RENEW : 0
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