Floodplain Lake Water Level Prediction with Strong River-Lake Interaction Using the Ensemble Learning LightGBM-Reference-Cited by-同舟云学术

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Floodplain Lake Water Level Prediction with Strong River-Lake Interaction Using the Ensemble Learning LightGBM

Published:2024-06-18 Issue: Volume: Page:
ISSN:0920-4741
Container-title:Water Resources Management
language:en
Short-container-title:Water Resour Manage

Author:

Gan Min^ORCID,Lai Xijun,Guo Yan,Chen Yongping,Pan Shunqi,Zhang Yinghao

Funder

National Natural Science Foundation of China

State Key Laboratory of Lake Science and Environment

Strategic Priority Research Program of Chinese Academy of Sciences

Science and Technology Planning Project of NIGLAS

Jiangsu Funding Program for Excellent Postdoctoral Talent

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11269-024-03915-8.pdf

Reference58 articles.

1. Abba SI, Hadi SJ, Sammen SS et al (2020) Evolutionary computational intelligence algorithm coupled with self-tuning predictive model for water quality index determination. J Hydrol 587:124974. https://doi.org/10.1016/j.jhydrol.2020.124974

2. Altunkaynak A (2006) Forecasting Surface Water Level fluctuations of Lake Van by Artificial neural networks. Water Resour Manage 21:399–408. https://doi.org/10.1007/s11269-006-9022-6

3. ASCE (2000a) Artificial neural networks in hydrology. I: Preliminary concepts. Journal of Hydrologic Engineering 5:115–123. https://doi.org/10.1061/(asce)1084-0699

4. ASCE (2000b) Artificial neural networks in hydrology. II: Hydrologic applications. Journal of Hydrologic Engineering 5:124–137. https://doi.org/10.1061/(asce)1084-0699

5. Bonnet MP, Barroux G, Martinez JM et al (2008) Floodplain hydrology in an Amazon floodplain lake (Lago Grande De Curuaí). J Hydrol 349:18–30. https://doi.org/10.1016/j.jhydrol.2007.10.055

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