Articles | Volume 45
https://doi.org/10.5194/adgeo-45-201-2018
https://doi.org/10.5194/adgeo-45-201-2018
27 Aug 2018
 | 27 Aug 2018

Evaluation of random forests and Prophet for daily streamflow forecasting

Georgia A. Papacharalampous and Hristos Tyralis

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Large-scale assessment of Prophet for multi-step ahead forecasting of monthly streamflow
Hristos Tyralis and Georgia A. Papacharalampous
Adv. Geosci., 45, 147–153, https://doi.org/10.5194/adgeo-45-147-2018,https://doi.org/10.5194/adgeo-45-147-2018, 2018
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Cited articles

Abrahart, R. J., See, L. M., and Dawson, C. W.: Neural Network Hydroinformatics: Maintaining Scientific Rigour, in: Practical Hydroinformatics, edited by: Abrahart, R. J., See, L. M., and Solomatine, D. P., Springer-Verlag Berlin Heidelberg, 33–47, https://doi.org/10.1007/978-3-540-79881-1_3, 2008. 
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: Catchment attributes for large-sample studies, Boulder, CO, UCAR/NCAR, https://doi.org/10.5065/D6G73C3Q, 2017a. 
Addor, N., Newman, A. J., Mizukami, N., and Clark, M. P.: The CAMELS data set: catchment attributes and meteorology for large-sample studies, Hydrol. Earth Syst. Sci., 21, 5293–5313, https://doi.org/10.5194/hess-21-5293-2017, 2017b. 
Allaire, J. J., Xie, Y., McPherson, J., Luraschi, J., Ushey, K., Atkins, A., Wickham, H., Cheng, J., and Chang, W.: rmarkdown: Dynamic Documents for R. R package version 1.10, available at: https://CRAN.R-project.org/package=rmarkdown (last access: 16 August 2018), 2018. 
Auguie, B.: gridExtra: Miscellaneous Functions for “Grid” Graphics, R package version 2.3, available at: https://CRAN.R-project.org/package=gridExtra (last access: 16 August 2018), 2017. 
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Short summary
The predictive performance of random forests (a machine learning algorithm) and three configurations of Prophet (a method largely implemented in Facebook) is assessed in daily streamflow forecasting in a river in the US. Random forests perform better compared to the utilized benchmarks, i.e. a naïve method and a multiple regression linear model, while Prophet's performance is subject to improvements. Random forests are recommended for daily streamflow forecasting.