Abstract
We apply deep learning to a synthetic near-surface hydrological response
dataset of 4.4 million infiltration scenarios to determine conditions
for the onset of positive pore-water pressures. This provides a rapid
assessment of hydrologic conditions of potentially hazardous hillslopes
where mass wasting is prevalent, and sidesteps the computationally
expensive process of solving complex, highly non-linear equations. Each
scenario considers antecedent soil moisture and storm depth with varying
soil properties based on those measured at a USGS site in the East Bay
Hills, CA, USA. Our model combines antecedent soil wetness and storm
conditions with soil-hydraulic properties and predicts a binary output
of whether or not positive pore pressures were generated. After
parameterization, pore-water pressure conditions can be returned for any
combination of antecedent soil moisture content and storm depth values.
Similar to previous work, a deep learning model reduces computational
cost: processing time is decreased by more than an order of magnitude
for 1D simulated infiltration scenarios while maintaining high levels of
accuracy. While the physical relevance and utility behind process-based
numerical modeling cannot be replaced, the comparatively reduced
computational cost of deep learning allows for rapid modeling of
pore-water pressure conditions where solving complex, highly non-linear
equations would otherwise be required. Furthermore, comparing the
solution of a deep learning model with a hydrological model exemplifies
how similar results can be produced through highly divergent
mathematical relationships. This provides a unique opportunity to
understand which variables are most relevant for the prediction of
positive pore-water pressures on hillslopes, and can represent
landslide-relevant hydrologic conditions for hillslopes where rapid
analysis is imperative for informing potential hazard mitigation
efforts. Ultimately, a calibrated deep learning model may reduce the
need for computationally expensive physics-based modeling, which are
often time and resource intensive, while providing critical statistical
insight for the onset of hazardous conditions in landslide-prone areas.