Journal of Hydroinformatics Vol 10 No 4 pp 289–300 © IWA Publishing 2008 doi:10.2166/hydro.2008.049
TDNN with logical values for hydrologic modeling in a cold and snowy climate
Yonas B. Dibike and Paulin Coulibaly
National Water Research Institute, Environment Canada, Water & Climate Impact Research Centre, University of Victoria, Victoria, BC V8W 2Y2, Canada
Department of Civil Engineering, School of Geography and Earth Science, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S 4L7, Canada Tel.: +1 905 525 9140 X23354 Fax: +1 905 529 9688 E-mail: couliba@mcmaster.ca
ABSTRACT
Watershed runoff in areas with heavy seasonal snow cover is usually estimated using physically based conceptual hydrologic models. Such simulation models normally require a snowmelt algorithm consisting of a surface energy balance and some accounting of internal snowpack processes to be part of the modeling system. On the other hand, artificial neural networks are flexible mathematical structures that are capable of identifying such complex nonlinear relationships between input and output datasets from historical precipitation, temperature and streamflow records. This paper presents the findings of a study on using a form of time-delayed neural network, namely time-lagged feedforward neural network (TLFN), that implicitly accounts for snow accumulation and snowmelt processes through the use of logical values and tapped delay lines. The logical values (in the form of symbolic inputs) are used to implicitly include seasonal information in the TLFN model. The proposed method has been successfully applied for improved precipitation–runoff modeling of both the Chute-du-Diable reservoir inflows and the Serpent River flows in northeastern Canada where river flows and reservoir inflows are highly influenced by seasonal snowmelt effects. The study demonstrates that the TLFN with logical values is capable of modeling the precipitation–runoff process in a cold and snowy climate by relying on ‘logical input values’ and tapped delay lines to implicitly recognize the temporal input–output patterns in the historical data. The study results also show that, once the appropriate input patterns are identified, the time-lagged neural network based models performed quite well, especially for spring peak flows, and demonstrated comparable performance in simulating the precipitation–runoff processes to that of a physically based hydrological model, namely HBV.
Keywords: cold and snowy climate; logical values; precipitation–runoff modeling; time delay neural networks
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