Description
The applications of statistical learning theory (SLT) in Hydrology have been either in the form of Support Vector Machines and other complexity regularized machine learning algorithms that learn and predict input-output patterns such as rainfall-runoff time series or of identifying optimal complexity of low order models such as k nearest neighbor models to predict hydrological time series such as streamflow. The regularization of model complexity offers a way to identify minimal complexity of a model to accurately predict a time series of interest. However such applications often assume that the modelled residual are independent of each other. This limits its application to conceptual hydrological models where residuals are often auto-correlated. This paper applies recent results of risk bounds for time series forecasting and SLT approaches to dynamical system identification to conceptual hydrological models, offering a means to identify optimal complexity of conceptual models and complexity regularized streamflow predictions based on it.| Period | 14 Dec 2022 |
|---|---|
| Event title | AGU Fall Meeting 2022 |
| Event type | Conference |
| Location | Chicago, United States |
| Degree of Recognition | International |