Sourcing CHIRPS precipitation data for streamflow forecasting using intrinsic time-scale decomposition based machine learning models-Reference-Cited by-同舟云学术

Sourcing CHIRPS precipitation data for streamflow forecasting using intrinsic time-scale decomposition based machine learning models

Published:2021-06-21 Issue:9 Volume:66 Page:1437-1456
ISSN:0262-6667
Container-title:Hydrological Sciences Journal
language:en
Short-container-title:Hydrological Sciences Journal

Author:

Wang Maofa¹,Rezaie-Balf Mohammad²,Naganna Sujay Raghavendra³^ORCID,Yaseen Zaher Mundher⁴^ORCID

Affiliation:

1. School of Computer and Information Security, Guilin University of Electronic Technology, Guilin, China

2. Department of Civil Engineering, Graduate University of Advanced Technology Kerman, Kerman, Iran

3. Department of Civil Engineering, Siddaganga Institute of Technology, Tumakuru, India

4. New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, Iraq

Funder

external funding

Publisher

Informa UK Limited

Subject

Water Science and Technology

Link

https://www.tandfonline.com/doi/pdf/10.1080/02626667.2021.1928138

Reference90 articles.

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3. Daily streamflow prediction using optimally pruned extreme learning machine

4. Anastasakis, L. and Mort, N., 2001. The development of self-organization techniques in modelling: a review of the Group Method of Data Handling (GMDH). UK: Department of Automatic Control & Systems Engineering, The University of Sheffield, Tech. Rep. 813.

5. PERSIANN-CDR: Daily Precipitation Climate Data Record from Multisatellite Observations for Hydrological and Climate Studies

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