Incorporating synoptic-scale climate signals for streamflow modelling over the Mediterranean region using machine learning models |
| |
| Authors: | Ozgur Kisi Bahram Choubin Ravinesh C Deo |
| |
| Institution: | 1. Faculty of Natural Sciences and Engineering, Ilia State University, Tbilisi, Georgia;2. Department of Watershed Management, Sari University of Agricultural Sciences and Natural Resources, Sari, Iran;3. School of Agricultural Computational and Environmental Sciences, Centre of Sustainable Agricultural Catchments &4. Centre for Applied Climate Sciences, Institute of Life Sciences and the Environment, University of Southern Queensland, Springfield, Australia |
| |
| Abstract: | ABSTRACTUnderstanding streamflow patterns by incorporating climate signal information can contribute remarkably to the 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 a 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 |
|
|
