Novel insights for streamflow forecasting based on deep learning models combined the evolutionary optimization algorithm-Reference-Cited by-同舟云学术

Novel insights for streamflow forecasting based on deep learning models combined the evolutionary optimization algorithm

Published:2021-07-27 Issue: Volume: Page:1-24
ISSN:0272-3646
Container-title:Physical Geography
language:en
Short-container-title:Physical Geography

Author:

Zakhrouf Mousaab¹,Hamid Bouchelkia¹,Kim Sungwon²,Madani Stamboul³

Affiliation:

1. URMER Laboratory, Department of Hydraulics, Faculty of Technology, University of Tlemcen, Tlemcen, Algeria

2. Department of Railroad Construction and Safety Engineering, Dongyang University, Yeongju, South Korea

3. Research Laboratory of Water Resources, Soil and Environment, Department of Civil Engineering, Faculty of Architecture and Civil Engineering, Amar Telidji University, Laghouat, Algeria

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences,Earth and Planetary Sciences (miscellaneous),Atmospheric Science,General Environmental Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/02723646.2021.1943126

Reference61 articles.

1. Radiocarbon chronology and environmental context of Last Glacial Maximum human occupation in Switzerland

2. Simulating monthly streamflow using a hybrid feature selection approach integrated with an intelligence model

3. Criteria for Evaluation of Watershed Models

4. Evolutionary convolutional neural networks: An application to handwriting recognition

5. Evaluation of hydrological models for scenario analyses: signal-to-noise-ratio between scenario effects and model uncertainty

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