Evaluation of random forests and Prophet for daily streamflow forecasting
Georgia A. Papacharalampous
CORRESPONDING AUTHOR
Department of Water Resources and Environmental Engineering, National
Technical University of Athens, Zografou, 157 80, Greece
Hristos Tyralis
Air Force Support Command, Hellenic Air Force, Elefsina, 192 00,
Greece
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Latest update: 19 Nov 2024
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.
The predictive performance of random forests (a machine learning algorithm)
and three...