Evaluation of random forests and Prophet for daily streamflow forecasting

Abstract

Abstract. We assess the performance of random forests and Prophet in forecasting daily streamflow up to seven days ahead in a river in the US. Both the assessed forecasting methods use past streamflow observations, while random forests additionally use past precipitation information. For benchmarking purposes we also implement a naïve method based on the previous streamflow observation, as well as a multiple linear regression model utilizing the same information as random forests. Our aim is to illustrate important points about the forecasting methods when implemented for the examined problem. Therefore, the assessment is made in detail at a sufficient number of starting points and for several forecast horizons. The results suggest that random forests perform better in general terms, while Prophet outperforms the naïve method for forecast horizons longer than three days. Finally, random forests forecast the abrupt streamflow fluctuations more satisfactorily than the three other methods.

A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources

“…In this Section, we examine the streamflow forecasting case study by Papacharalampous and Tyralis [132], and how this could have been improved, by considering the findings of Sections 2-4. Papacharalampous and Tyralis [132] use previous-day observed streamflow and precipitation as predictor variables to produce next-day forecasts; i.e., a common problem in hydrology (see e.g., Table 1), where numerous machine learning algorithms have been applied.…”

“…In this Section, we examine the streamflow forecasting case study by Papacharalampous and Tyralis [132], and how this could have been improved, by considering the findings of Sections 2-4. Papacharalampous and Tyralis [132] use previous-day observed streamflow and precipitation as predictor variables to produce next-day forecasts; i.e., a common problem in hydrology (see e.g., Table 1), where numerous machine learning algorithms have been applied. Forecasts are generated by implementing random forests (specifically the ranger R package, with root mean square errors and mean absolute forecast errors as performance indicators), with recursive retraining (i.e., the algorithm is retrained based on past data at each step of the forecast sequence), and predictor variables selected using linear metrics (i.e., the estimated streamflow autocorrelations, and the estimated cross-correlations between precipitation and streamflow, at different lag times).…”

Applicability of machine learning in modeling of atmospheric particle pollution in Bangladesh

“…PROPHET implements the decomposition of the time series with three main components which are seasonality, overall trends, and holidays (Papacharalampous and Tyralis 2018 ). The Eq.…”

Quantification of predictive uncertainty in hydrological modelling by harnessing the wisdom of the crowd: A large-sample experiment at monthly timescale

“…For benchmarking purposes, we also apply the working methodology using the linear regression model (see e.g., James et al 2013;Hastie et al 2009) as error model, and the two naïve probabilistic data-driven schemes. For the merits of using benchmarks in hydrological modelling, the reader is referred to Pappenberger et al (2015); see also benchmarking examples in Montanari and Brath (2004), Papacharalampous and Tyralis (2018), Papacharalampous et al (2018aPapacharalampous et al ( ,b,c, 2019a, Quilty et al (2019), Evin et al (2014), Sikorska et al (2015), Papacharalampous (2017, 2018), Tyralis et al ( , 2019a, and Xu et al (2018).…”