Roering, JoshuaOrland, Elijah2020-09-242020-09-24https://hdl.handle.net/1794/25701Empirical thresholds for landslide warning systems have benefitted from the incorporation of soil-hydrologic monitoring data, but the mechanistic basis for their predictive capabilities is limited. Although physics-based hydrologic models can accurately simulate changes in soil moisture and pore pressure that promote landslides, their utility is restricted by high computational costs and non-unique parameterization issues. We construct a Deep Learning model using soil-moisture, pore-pressure, and rainfall monitoring data acquired from landslide-prone hillslopes in Oregon, USA, to predict the timing and magnitude of hydrologic response dynamics at multiple soil depths for 36-hour intervals. We find that observation records as short as six months are sufficient for accurate predictions, and our model captures hydrologic response for high-intensity rainfall events even when those storm types are excluded from model training. We conclude that machine learning can provide an accurate, and computationally efficient alternative to empirical methods or physics-based hydrologic modeling for landslide hazard warning.en-USAll Rights Reserved.Deep LearningLandslidesLSTMMachine LearningNatural HazardsElectronic Thesis or Dissertation