- author = "Shreenivas Londhe and Shrikant Charhate",
- title = "Comparison of data-driven modelling techniques for river flow forecasting",
- journal = "Hydrological Sciences Journal",
- year = "2010",
- volume = "55",
- number = "7",
- pages = "1163--1174",
- keywords = "genetic algorithms, genetic programming, streamflow, data-driven modelling, artificial neural networks, genetic programming, M5 model trees",
- ISSN = "02626667",
-
DOI = "
doi:10.1080/02626667.2010.512867",
- size = "12 pages",
- abstract = "Accurate forecasting of streamflow is essential for the efficient operation of water resources systems. The streamflow process is complex and highly nonlinear. Therefore, researchers try to devise alternative techniques to forecast streamflow with relative ease and reasonable accuracy, although traditional deterministic and conceptual models are available. The present work uses three data-driven techniques, namely artificial neural networks (ANN), genetic programming (GP) and model trees (MT) to forecast river flow one day in advance at two stations in the Narmada catchment of India, and the results are compared. All the models performed reasonably well as far as accuracy of prediction is concerned. It was found that the ANN and MT techniques performed almost equally well, but GP performed better than both these techniques, although only marginally in terms of prediction accuracy in normal and extreme events.",
-
notes = "Department of Civil Engineering, Vishwakarma Institute
of Information Technology, Survey no. 2/3/4, Kondhwa
(Bk), Pune, MH, 411048, India
Department of Civil Engineering, Datta Meghe College of Engineering, Airoli, Navi Mumbai, MH, 400708, India
p1172 'Rajghat and Mandaleshwar in the Narmada basin in India. The GP models performed better compared to ANN and MT models, though marginally.'
Comparaison de techniques de modelisation conditionnee par les donnees pour la prevision des debits fluviaux",
