EGU26-746, updated on 13 Mar 2026
https://doi.org/10.5194/egusphere-egu26-746
EGU General Assembly 2026
© Author(s) 2026. This work is distributed under the Creative Commons Attribution 4.0 License.
A Hybrid Deep Learning Approach: Constructing prediction intervals for Streamflow forecasting
1- 1Department of Water Resources Development and Management, Indian Institute of Technology Roorkee, Roorkee, India (bpr8055@gmail.com)
- 2Mehta Family School of Data Science and Artificial Intelligence, Indian Institute of Technology Roorkee, Roorkee, India (k.kasiviswanathan@wr.iitr.ac.in)
How to cite: Balasundaram, P. and Kasiviswanathan, K. S.: A Hybrid Deep Learning Approach: Constructing prediction intervals for Streamflow forecasting, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-746, https://doi.org/10.5194/egusphere-egu26-746, 2026.