Characterization of Hillslope Hydrologic Events Using Machine Learning Algorithms

Abstract. Time series of soil moisture were measured at 30 points for 396 rainfall events on a steep, forested hillslope between 2007 and 2016. We then analyzed the dataset using an unsupervised machine learning algorithm to cluster the hydrologic events based on the dissimilarity distances between weighting components of a self-organizing map (SOM). Generation patterns of two primary hillslope hydrological processes, namely, vertical flow and lateral flow, at the upslope and downslope areas were responsible for the distinction of the hydrologic events. Two-dimensional spatial weighting patterns in the SOM provided explanations for the relationships between rainfall characteristics and hydrological processes at different locations and depths. High reliability in hydrologic classification was achieved for both the driest and wettest events; as assessed through k-fold cross validation using 10 years of data. Representative soil moisture monitoring points were found through temporal stability analysis of the event structure delineated from the machine learning classification. Application of a supervised machine learning algorithm provided a scheme using soil moisture for the cluster identification of hydrologic event even without rainfall data which is useful to configure hillslope hydrologic process with the least cost in data acquisition.