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Comparison of local and global approximators in multivariate chaotic forecasting of daily streamflow.
- Source :
- Hydrological Sciences Journal/Journal des Sciences Hydrologiques; Jun2020, Vol. 65 Issue 7, p1129-1144, 16p
- Publication Year :
- 2020
-
Abstract
- Although it is conceptually assumed that global models are relatively ineffective in modelling the highly unstable structure of chaotic hydrologic dynamics, there is not a detailed study of comparing the performances of local and global models in a hydrological context, especially with new emerging machine learning models. In this study, the performance of a local model (k-nearest neighbour, k-nn) and, as global models, several recent machine learning models – artificial neural network (ANN), least square-support vector regression (LS-SVR), random forest (RF), M5 model tree (M5), multivariate adaptive regression splines (MARS) – was analysed in multivariate chaotic forecasting of streamflow. The models were developed for Australia's largest river, the River Murray. The results indicate that the k-nn model was more successful than the global models in capturing the streamflow dynamics. Furthermore, coupled with the multivariate phase-space, it was shown that the global models can be successfully used for obtaining reliable uncertainty estimates for streamflow. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02626667
- Volume :
- 65
- Issue :
- 7
- Database :
- Complementary Index
- Journal :
- Hydrological Sciences Journal/Journal des Sciences Hydrologiques
- Publication Type :
- Academic Journal
- Accession number :
- 143114305
- Full Text :
- https://doi.org/10.1080/02626667.2020.1732983