A comparative study between dynamic and soft computing models for sediment forecasting
Author:
Funder
King Khalid University
Publisher
Springer Science and Business Media LLC
Subject
Geometry and Topology,Theoretical Computer Science,Software
Link
https://link.springer.com/content/pdf/10.1007/s00500-021-05834-x.pdf
Reference71 articles.
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2. Abba SI, Elkiran G, Nourani V (2020) Non-linear Ensemble Modeling for Multi-step Ahead Prediction of Treated COD in Wastewater Treatment Plant. In: Aliev R., Kacprzyk J., Pedrycz W., Jamshidi M., Babanli M., Sadikoglu F. (eds) 10th International Conference on Theory and Application of Soft Computing, Computing with Words and Perceptions - ICSCCW-2019. ICSCCW 2019. Advances in Intelligent Systems and Computing, vol 1095. Springer, Cham.
3. Abba SI, Hadi SJ, Sammen SS, Salih SQ, Abdulkadir RA, Pham QB, Yaseen ZM (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
4. Adamowski J, Chan HF (2011) A wavelet neural network conjunction model for groundwater level forecasting. J Hydrol 407(1–4):28–40
5. Agarwal A, Mishra SK, Ram S, Singh JK (2006) Simulation of runoff and sediment yield using artificial neural networks. Biosyst Eng 94(4):597–613
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