Advanced water level prediction for a large-scale river–lake system using hybrid soft computing approach: a case study in Dongting Lake, China-Reference-Cited by-同舟云学术

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Advanced water level prediction for a large-scale river–lake system using hybrid soft computing approach: a case study in Dongting Lake, China

Published:2021-07-12 Issue:4 Volume:14 Page:1987-2001
ISSN:1865-0473
Container-title:Earth Science Informatics
language:en
Short-container-title:Earth Sci Inform

Author:

Deng Bin,Lai Sai Hin,Jiang Changbo,Kumar Pavitra,El-Shafie Ahmed,Chin Ren Jie

Funder

universiti tunku abdul rahman

National Natural Science Foundation of China

National Science Foundation of Hunan Province, China

Key Laboratory of Dongting Lake Aquatic Eco-Environmental Control and Restoration of Hunan Province

Publisher

Springer Science and Business Media LLC

Subject

General Earth and Planetary Sciences

Link

https://link.springer.com/content/pdf/10.1007/s12145-021-00665-8.pdf

Reference65 articles.

1. Abdullah SS, Abdullah MN, Mustapha A (2015) Feedforward backpropagation, genetic algorithm approaches for predicting reference evapotranspiration. Sains Malaysiana 44(7):1053–1059

2. Adnan R, Zounemat-Kermani M, Kuriqi A, Kisi O (2021) Machine learning method in prediction streamflow considering periodicity component. Intelligent Data Analytics for Decision-Support Systems in Hazard Mitigation. Singapore: Springer

3. Afan HA, Allawi MF, El-Shafie A, Yaseen ZM, Ahmed AN, Malek MA, ... El-Shafie A (2020) Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting. Sci Rep 10:4684

4. Aghelpour P, Bahrami-Pichaghchi H, Kisi O (2020a) Comparison of three different bio-inspired algorithms to improve ability of neuro fuzzy approach in prediction of agricultural drought, based on three different indexes. Comp Electron Agric 170:105279

5. Aghelpour P, Mohammadi B, Biazar SM, Kisi O, Sourmirinezhad Z (2020b) A theoretical approach for forecasting different types of drought simultaneously, using entropy theory and machine-learning methods. Int J Geo-Inf 9(12):701

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