Towards a generic neural network model for the prediction of daily streamflow in ungauged boreal plain watersheds. (1st August 2008)

• Record Type: Journal Article
• Authors: Prepas, E E; Smith, Daniel W
• Is Part Of: Journal of environmental engineering and science. Volume 7(2008)Supplement 1
• Page Start: 79
• Page End: 93
• Publication Date: 2008-08-01
• Subjects: neural networks; -- model transferability; -- spectral analysis; -- Boreal Plain; -- modelling; -- hydrologic similarity; -- inverse distance weighted interpolation; -- degree-day; -- streamflow; -- hystereses; Environmental engineering -- Periodicals; Sanitary engineering -- Periodicals; Environmental engineering -- Canada -- Periodicals; Environnement, Technique de l' -- Périodiques; Technique sanitaire -- Périodiques; Environnement, Technique de l' -- Canada -- Périodiques; Environmental engineering; Sanitary engineering; Canada; Electronic journals; Computer network resources; Periodicals; 628.05
• DOI: 10.1139/S08-046
• Languages: English
• ISSNs: 1496-2551

Abstract

Abstract : Most of the currently available streamflow neural network models are either recurrent neural networks or feed-forward multi-layer perceptron (FF-MLP) requiring past flow values for lead time prediction. These models cannot be used for modelling ungauged watersheds because past flow values are not available in such cases. This study proposes a FF-MLP algorithm that relies only on low-cost, readily available meteorological data and careful time series manipulation prior to model building, and thus, is suitable for modelling streamflow in ungauged watersheds. The proposed approach was tested on four watersheds (5 to 130 km 2 ) in the Canadian Boreal forest and was found to provide an efficient modelling alternative for daily streamflow predictions. To assess the possibility of successful model transferability from a gauged watershed to a hydrologically similar ungauged watershed, a new remotely sensed hydrologic similarity measure - SWMIR_SI - was proposed and was found to provide a successful indicator of basin similarity. The square of Pearson's correlation coefficient, r 2, was evaluated to exceed 0.71 when SWMIR_SI was regressed to models' "goodness-of-fit" statistics reflecting the usefulness of the approach.