Predicting Erosion Channel First Passage with Machine Learning

We investigate statistical and machine learning approaches to predicting erosive headward growth in rivers with bimodal source distributions. Porosity maps generated by multiscale simulation methods calibrated with experiments [1] are used to generate large data sets amenable to statistical analysis. We focus on complementary methods to analyze the statistical features of the spatio-temporal porosity maps and perform image analysis with convolution neural networks (CNN). The accuracy of each method in predicting channel passage from a single sink point to two source points located across a rectangular domain as a function time and distance to source will be discussed. CNN methods are found to consistently provide the earliest prediction with greatest confidence. We will further discuss the robustness of the predictions depending on the degree of domain disorder, data size, and network architecture.

[1] "Headward growth and branching in subterranean channels," Arshad Kudrolli, Nikolay Ionkin, and Andreea Panaitescu, Phys. Rev. E 96, 052904 (2017).