Comparison of prediction error methods and subspace identification methods for rivers

Data based modelling is an important tool in the operation and management of rivers. In this paper, we compare Prediction Error Methods (PEM) and Subspace Identification Methods (SIM) for system identification of rivers. PEM can incorporate the available prior information and can accommodate the non-linearities in the model structure with ease. The models obtained by SIM are linear, and it is generally difficult to incorporate prior information, but SIM is well suited to MIMO systems such as rivers. In this paper, we simulate a few typical river scenarios and use the simulated data to obtain PEM and SIM based models. The models are compared using several measures and for the scenarios considered it is found that PEM has an edge over SIM.