An integrated machine learning, noise suppression, and population-based algorithm to improve total dissolved solids prediction

Author:

Sun Kangjie1,Rajabtabar Mohammad2,Samadi Seyedehzahra3,Rezaie-Balf Mohammad4,Ghaemi Alireza5,Band Shahab S.67,Mosavi Amir89101112ORCID

Affiliation:

1. School of Mechanical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China

2. Department of Computer Science and Software Engineering, Islamic Azad University Babol Branch, Babol, Iran

3. Department of Agricultural Sciences, Clemson University, Clemson, SC, USA

4. Department of Water Engineering, Graduate University of Advanced Technology, Kerman, Iran

5. Department of Water Engineering, University of Sistan and Baluchestan, Zahedan, Iran

6. Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam

7. Future Technology Research Center, College of Future, National Yunlin University of Science and Technology, Douliou, Yunlin 64002, Taiwan, ROC

8. Faculty of Civil Engineering, Technische Universität Dresden, Dresden, Germany

9. John von Neumann Faculty of Informatics, Obuda University, Budapest, Hungary

10. School of Economics and Business, Norwegian University of Life Sciences, Ås, Norway

11. School of the Built Environment, Oxford Brookes University, Oxford, UK

12. Department of Informatics, J. Selye University, Komarno, Slovakia

Publisher

Informa UK Limited

Subject

Modeling and Simulation,General Computer Science

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