Kisi, Ozgur and Choubin, Bahram and Deo, Ravinesh C. 
ORCID: https://orcid.org/0000-0002-2290-6749 and Yaseen, Zaheer Mundher
(2019)
Incorporating synoptic-scale climate signals for streamflow modelling over the Mediterranean region using machine learning models.
Hydrological Sciences Journal, 64 (10).
pp. 1240-1252.
ISSN 0262-6667
Abstract
Understanding streamflow patterns by incorporating climate signal information can contribute remarkably to knowledge of future local environmental flows. Three machine learning models, the multivariate adaptive regression splines (MARS), the M5 Model Tree and the least squares support vector machine (LSSVM), are established to predict the streamflow pattern over the Mediterranean region of Turkey (Besiri and Baykan stations). The structure of the predictive models is built using synoptic-scale climate signal information and river flow data from antecedent records. The predictive models are evaluated and assessed using quantitative and graphical statistics. The correlation analysis demonstrates that the North Pacific (NP) and the East Central Tropical Pacific Sea Surface Temperature (Niño3.4) indices have substantial influence on the streamflow patterns, in addition to the historical information obtained from the river flow data. The model results reveal the utility of the LSSVM model over the other models through incorporating climate signal information for modelling streamflow
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| Item Type: | Article (Commonwealth Reporting Category C) |
|---|---|
| Refereed: | Yes |
| Item Status: | Live Archive |
| Additional Information: | Accepted author version posted online: 18 June 2019. Permanent restricted access in accordance with the copyright policy of the publisher. |
| Faculty/School / Institute/Centre: | Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019) |
| Faculty/School / Institute/Centre: | Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019) |
| Date Deposited: | 25 Jun 2019 07:12 |
| Last Modified: | 19 Apr 2021 01:07 |
| Uncontrolled Keywords: | climate signal information, machine learning models, streamflow prediction, Mediterranean region |
| Fields of Research (2008): | 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems 05 Environmental Sciences > 0502 Environmental Science and Management > 050205 Environmental Management |
| Socio-Economic Objectives (2008): | E Expanding Knowledge > 97 Expanding Knowledge > 970105 Expanding Knowledge in the Environmental Sciences |
| Identification Number or DOI: | https://doi.org/10.1080/02626667.2019.1632460 |
| URI: | http://eprints.usq.edu.au/id/eprint/36624 |
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