Super ensemble learning for daily streamflow forecasting: large-scale demonstration and comparison with multiple machine learning algorithms

Daily streamflow forecasting through data-driven approaches is traditionally performed using a single machine learning algorithm. Existing applications are mostly restricted to examination of few case studies, not allowing accurate assessment of the predictive performance of the algorithms involved....

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Vydáno v:Neural computing & applications Ročník 33; číslo 8; s. 3053 - 3068
Hlavní autoři: Tyralis, Hristos, Papacharalampous, Georgia, Langousis, Andreas
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Springer London 01.04.2021
Springer Nature B.V
Témata:
Algorithms
Artificial Intelligence
Autoregressive models
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Data Mining and Knowledge Discovery
Decision trees
Ensemble learning
Forecasting
Image Processing and Computer Vision
Loess
Machine learning
Neural networks
Original Article
Performance prediction
Probability and Statistics in Computer Science
Regression
Regression analysis
Statistical analysis
Stream flow
Support vector machines
Hydrology
Ensemble learning
Stacking
Combining forecasts
ISSN:0941-0643, 1433-3058
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Popis
Shrnutí:Daily streamflow forecasting through data-driven approaches is traditionally performed using a single machine learning algorithm. Existing applications are mostly restricted to examination of few case studies, not allowing accurate assessment of the predictive performance of the algorithms involved. Here, we propose super learning (a type of ensemble learning) by combining 10 machine learning algorithms. We apply the proposed algorithm in one-step-ahead forecasting mode. For the application, we exploit a big dataset consisting of 10-year long time series of daily streamflow, precipitation and temperature from 511 basins. The super ensemble learner improves over the performance of the linear regression algorithm by 20.06%, outperforming the “hard to beat in practice” equal weight combiner. The latter improves over the performance of the linear regression algorithm by 19.21%. The best performing individual machine learning algorithm is neural networks, which improves over the performance of the linear regression algorithm by 16.73%, followed by extremely randomized trees (16.40%), XGBoost (15.92%), loess (15.36%), random forests (12.75%), polyMARS (12.36%), MARS (4.74%), lasso (0.11%) and support vector regression (− 0.45%). Furthermore, the super ensemble learner outperforms exponential smoothing and autoregressive integrated moving average (ARIMA). These latter two models improve over the performance of the linear regression algorithm by 13.89% and 8.77%, respectively. Based on the obtained large-scale results, we propose super ensemble learning for daily streamflow forecasting.
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-020-05172-3