Enhancing the prediction accuracy of data-driven models for monthly streamflow in Urmia Lake Basin based upon the autoregressive conditionally heteroskedastic time-series model
Hydrological modeling is one of the important subjects in managing water resources and the processes of predicting stochastic behavior. Developing Data-Driven Models (DDMs) to apply to hydrological modeling is a very complex issue because of the stochastic nature of the observed data, like seasonali...
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
MDPI AG
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|