Short lead-time streamflow forecasting by machine learning methods, with climate variability incorporated ...
Streamflow fluctuates as a result of different atmospheric, hydrologic, and morphologic mechanisms governing a river watershed. Variability of meteorological variables such as rainfall, temperature, wind, sea level pressure, humidity, and heating, as well as large scale climate indices like the Arct...
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| Format: | Text |
| Language: | English |
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University of British Columbia
2010
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| Online Access: | https://dx.doi.org/10.14288/1.0052642 https://doi.library.ubc.ca/10.14288/1.0052642 |
| Summary: | Streamflow fluctuates as a result of different atmospheric, hydrologic, and morphologic mechanisms governing a river watershed. Variability of meteorological variables such as rainfall, temperature, wind, sea level pressure, humidity, and heating, as well as large scale climate indices like the Arctic Oscillation, Pacific/North American Pattern, North Atlantic Oscillation, and El Niño-Southern Oscillation play a role on the availability of water in a given basin. In this study, outputs of the NOAA Global Forecasting System (GFS) model, climate fluctuations, and observed data from meteohydrologic stations are used to forecast daily streamflows. Three machine learning methods are used for this purpose: support vector regression (SVR), Gaussian process (GP), and Bayesian neural network (BNN) models, and the results are compared with the multiple linear regression (MLR) model. Lead-time for forecasting varies from 1 to 7 days. This study has been applied to a small coastal watershed in British Columbia, Canada. ... |
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