Incorporating synoptic-scale climate signals for streamflow modelling over the Mediterranean region using machine learning models
Article
Article Title | Incorporating synoptic-scale climate signals for streamflow modelling over the Mediterranean region using machine learning models |
---|---|
ERA Journal ID | 30188 |
Article Category | Article |
Authors | Kisi, Ozgur (Author), Choubin, Bahram (Author), Deo, Ravinesh C. (Author) and Yaseen, Zaheer Mundher (Author) |
Journal Title | Hydrological Sciences Journal |
Journal Citation | 64 (10), pp. 1240-1252 |
Number of Pages | 13 |
Year | 2019 |
Place of Publication | United Kingdom |
ISSN | 0262-6667 |
2150-3435 | |
Digital Object Identifier (DOI) | https://doi.org/10.1080/02626667.2019.1632460 |
Web Address (URL) | https://www.tandfonline.com/doi/full/10.1080/02626667.2019.1632460 |
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 |
Keywords | climate signal information, machine learning models, streamflow prediction, Mediterranean region |
ANZSRC Field of Research 2020 | 460510. Recommender systems |
410404. Environmental management | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Ilia State University, United States |
Sari Agricultural Sciences and Natural Resources University, Iran | |
School of Agricultural, Computational and Environmental Sciences | |
Ton Duc Thang University, Vietnam | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q54v1/incorporating-synoptic-scale-climate-signals-for-streamflow-modelling-over-the-mediterranean-region-using-machine-learning-models
178
total views8
total downloads3
views this month0
downloads this month