Clustering and Random Forest Analysis for the Identification of Hydrological Controls of Slope Response to Rainfall

The assessment of the response of slopes to precipitations is important for various applications, from water resources management to hazard assessment due to extreme rainfall events. It is well known that the underground conditions prior to the initiation of rainfall events control the hydrological processes that occur in slopes, affecting the water exchange through their boundaries. The present study aims at identifying hydrological variables to be monitored and modelled, suitable to improve the prediction of slope response to precipitations, for the case of a slope covered with loose pyroclastic coarse-grained soil overlaying a karstic bedrock, typical of southern Apennines (Italy). Field monitoring has been carried out for three years at the slope, including stream level recordings, meteorological recordings, and soil water content and suction measurements, which allowed setting up a physically based hydrological model of the slope, coupling the unsaturated flow in the soil cover with a perched aquifer developing in the fractured bedrock. To enlarge the field dataset, a synthetic dataset has been generated, linking a previously calibrated stochastic rainfall generator to the hydrological model. In this way, a synthetic dataset of 1000 years has been obtained, containing information on rainfall, aquifer water level and soil volumetric water content at different depths. Machine Learning techniques have been used to unwrap the relationships linking the studied variables, typically non-linear. The Random Forest technique has been used to assess the importance of each variable on the slope response, and the k-means clustering technique has been used to explore the geometrical disposition of data, so to identify seasonally recurrent different conditions controlling the slope response. The results indicate that the slope response, in terms of the fraction of rainwater remaining stored in the soil cover at the end of each rainfall event, can be predicted from the underground conditions prior to the rainfall initiation, weighting the role, on one hand, of the soil moisture excess above field capacity, controlling the ease of the water to flow in and out of the soil cover and, on the other hand, of the perched aquifer water level, that gives evidence of the activation of effective slope drainage.