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TitleSupport vector machine for the prediction of future trend of Athabasca River (Alberta) flow rate
AuthorLiu, Y
SourceGeological Survey of Canada, Open File 6864, 2017, 31 pages,
PublisherNatural Resources Canada
Documentopen file
Mediaon-line; digital
RelatedThis publication is related to Chen, G; Chen, Z; (2017). Extreme river flow prediction for river water supply to oil sands mining sites, Athabasca River near Fort McMurray, Alberta,, Geological Survey of Canada, Open File 6863
File formatpdf
NTS73L; 73M; 74D; 82N; 82O; 83B; 83C; 83D; 83E; 83F; 83G; 83H; 83I; 83J; 83K; 83N; 83O; 83P; 84A; 84B; 84C
AreaAthabasca River; Fort McMurray; Clear Water River; Edson Creek; Slave Lake; Whitecourt
Lat/Long WENS-120.0000 -110.0000 57.0000 51.5000
Subjectshydrogeology; mathematical and computational geology; modelling; climate; climatic fluctuations; temperature; precipitation; hydrologic properties; hydrologic environment; surface waters; rivers; climate change; hydrology; river flow rates; support vector machine (SVM); water supply
Illustrationstables; location maps; time series
ProgramGeoscience for New Energy Supply (GNES) - Program Corrdination, Geoscience for New Energy Supply (GNES)
Released2017 03 07
River flow process usually was affected by a wide variety of factors and it is very difficult to make the trend prediction. Various mathematical techniques have been developed to tackle the prediction, but these techniques are less accurate compared with physically-based models. In this paper I presented the recurrent support vector machine methods to study a series of climate variables, such as temperature and precipitation, and then predict the future trend of flow river rate based on these climate variables. The Athabasca River data have been tested and the flow river rates in 100 years have been predicted.
Summary(Plain Language Summary, not published)
Many factors contribute to the fluctuation of river flow rate through time. Complicated mathematical models based on physical principles are difficult to apply in many cases due to limitation of data and constraints of resources. This open file reports a method that is based on the support vector machine to predict river flow rate using series of relevant climate variables, such as temperature and precipitation only, as model inputs. The historical climate data from Athabasca River were used to demonstrate the application of the proposed method, as an example, for future river flow prediction.