14th IFAC Symposium on System Identification, SYSID 2006

SYSID-2006 Paper Abstract


Paper ThA6.5

Young, Peter (Lancaster Univ.)

Data-Based Mechanistic Modelling and River Flow Forecasting

Scheduled for presentation during the Invited Session "Identification of Ecological/Environmental Systems" (ThA6), Thursday, March 30, 2006, 11:50−12:10, Newcastle Room

14th IFAC Symposium on System Identification, March 29 - 31, 2006, Newcastle, Australia

This information is tentative and subject to change. Compiled on September 24, 2018

Keywords Recursive Identification, Other, Nonlinear System Identification


The paper briefly reviews the topic of rainfall-flow modelling and the inductive, Data-Based Mechanistic (DBM) approach to modelling stochastic, dynamic systems. It then uses DBM modelling methods to investigate the nonlinear relationship between daily rainfall and flow in the Leaf River, Mississippi, USA. Initially, recursive State-Dependent Parameter (SDP) estimation is used to identify, in non-parametric (graphical) terms, the location and nature of the `effective rainfall' nonlinearity. Parameterization of this nonlinearity and optimization of a constrained version of the resulting model allow for its interpretation in a hydrologically meaningful State-Dependent Parameter Transfer Function (SDTF) form. Finally, the model its used as the basis for the design of a real-time flow forecasting using an optimized SDP Kalman Filter (SDPKF) forecasting engine that includes a model of the heteroscedastic measurement noise.