Abstract
Predicting future river flow is a difficult problem. Firstly, models are (by definition) crudely simplified versions of reality. Secondly, historical streamflow data is limited and noisy. Bayesian model averaging is theoretically a good way to cope with these difficulties, but it has not been widely used on this and similar problems. This paper uses real-world data to illustrate why. Bayesian model averaging can give a better prediction, but only if the amount of data is small — if the data is consistent with a wide range of different models (instead of unambiguously consistent with only a narrow range of near-identical models), then the weighted votes of those diverse models will give a better prediction than the single best model. In contrast, with plenty of data, only a narrow range of near-identical models will fit that data, and they all vote the same way, so there is no improvement in the prediction. But even when the data supports a diverse range of models, the improvement is far from large, but it is the direction of the improvement that can predict more accurately. Working around these caveats lets us better predict floods and similar problems, using limited or noisy data.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
This paper uses weekly data which is available from November 1981 onwards, from http://ioc-goos-oopc.org/state_of_the_ocean/sur/pac. For a quick introduction to the Nino3 and Nino4 sea surface temperature numbers, please see https://climatedataguide.ucar.edu/climate-data/.
- 2.
See http://ioc-goos-oopc.org/state_of_the_ocean/sur/ind for weekly data on the Indian Ocean sea surface temperature indices, including the DMI.
- 3.
The vertical axis in Fig. 4 is the un-normalized posterior probability (assuming a flat prior probability), so while it’s a linear scale, we cannot calculate the actual probability without doing the entire distribution of models, and instead we stopped at the best 150,000 models.
References
Darwen, P.J.: Two levels of Bayesian model averaging for optimal control of stochastic systems. Int. J. Syst. Sci. 44(2), 201–213 (2013)
Duan, Q., Ajami, N.K., Gao, X., Sorooshian, S.: Multi-model ensemble hydrologic prediction using Bayesian model averaging. Adv. Water Resour. 30, 1371–1386 (2007)
Gelman, A., Carlin, J.B., Stern, H.S., Rubin, D.B.: Bayesian Data Analysis. Texts in Statistical Science Series, 2nd edn. Chapman-Hall, Boca Raton (2004)
Hoeting, J.A., Madigan, D., Raftery, A.E., Volinsky, C.T.: Bayesian model averaging: a tutorial. Stat. Sci. 14(4), 382–417 (1999)
Kotz, S., Nadarajah, S.: Extreme Value Distributions: Theory and Applications. Imperial College Press, London (2001)
Madadgar, S., Moradkhani, H.: A Bayesian framework for probabilistic seasonal drought forecasting. J. Hydrometeorol. 14(6), 1685–1705 (2013)
Marshall, L., Nott, D., Sharma, A.: Towards dynamic catchment modelling: a Bayesian hierarchical mixtures of experts framework. Hydrol. Processes 21(7), 847–861 (2007)
Mazzarella, A., Giuliacci, A., Liritzis, I.: On the 60-month cycle of multivariate ENSO index. Theor. Appl. Climatol. 100, 23–27 (2010)
Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)
Najafi, M., Moradkhani, H., Jung, I.: Assessing the uncertainties of hydrologic model selection in climate change impact studies. Hydrol. Processes 25(18), 2814–2826 (2011)
Parrish, M.A., Moradkhani, H., DeChant, C.M.: Toward reduction of model uncertainty: integration of Bayesian model averaging and data assimilation. Water Resour. Res. 48(3), W03519 (2012). doi:10.1029/2011WR011116
Qu, B., Zhang, X., Pappenberger, F., Zhang, T., Fang, Y.: Multi-model grand ensemble hydrologic forecasting in the Fu river basin using Bayesian model averaging. Water 9(2), 74 (2017)
Taleb, N.: Dynamic Hedging: Managing Vanilla and Exotic Options. Wiley Finance, New York (1997)
Wang, Q.J., Robertson, D.E., Chiew, F.H.S.: A Bayesian joint probability modeling approach for seasonal forecasting of streamflows at multiple sites. Water Resour. Res. 45, 5407–5425 (2009)
Acknowledgments
The author thanks Matthew Fuller for technical support on the JCUB HPC cluster.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Darwen, P.J. (2017). Bayesian Model Averaging for Streamflow Prediction of Intermittent Rivers. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10351. Springer, Cham. https://doi.org/10.1007/978-3-319-60045-1_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-60045-1_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60044-4
Online ISBN: 978-3-319-60045-1
eBook Packages: Computer ScienceComputer Science (R0)