Using Support Vector Machines to Characterize Runoff-triggering in Small Watersheds

Runoff is one of the most complex hydrological phenomena to comprehend due to the tremendous spatial variability of catchment characteristics and precipitation patterns. However, the determination of runoff is critical for flood protection works, effective water storage and release, and protection of agricultural lands. The quantity of runoff depends on parameters such as rainfall intensity, duration, initial soil moisture, land use, and catchment geomorphology or relief. One common approach to estimate runoff is to develop physical models validated with measured data that relate the variables (input - output relationship) in the system. Conversely, this extraction of knowledge from the data requires large datasets, sophisticated modeling techniques as well as human intuition and experience. Additionally, the exact conditions that trigger runoff are difficult to predict because of their dependency on a combination of rainfall intensity, antecedent soil moisture conditions, and physical soil properties. Currently, pattern-learning algorithms based on artificial intelligence have shown promise in developing non-parametric models involving complex processes using few input parameters due to their ability to learn and recognize trends in the data. In this study, we explore the applicability of a sparse pattern-learning algorithm called Support Vector Machines (SVM) for modeling runoff from small watersheds. Results indicate that these methods can be an effective alternative to physical models for identifying runoff generation characteristics. Once identified, characteristics that trigger runoff from catchments, such as rainfall intensity and antecedent soil moisture, may be successfully used for large scale monitoring of watersheds using remote methods such as satellite sensors.