Explainable machine learning models for estimating daily dissolved oxygen concentration of the Tualatin River-Reference-Cited by-同舟云学术

Explainable machine learning models for estimating daily dissolved oxygen concentration of the Tualatin River

Published:2024-01-23 Issue:1 Volume:18 Page:
ISSN:1994-2060
Container-title:Engineering Applications of Computational Fluid Mechanics
language:en
Short-container-title:Engineering Applications of Computational Fluid Mechanics

Author:

Li Shuguang¹,Qasem Sultan Noman²³,Band Shahab S.⁴⁵,Ameri Rasoul⁶,Pai Hao-Ting⁷,Mehdizadeh Saeid⁸

Affiliation:

1. School of Computer Science and Technology, Shandong Technology and Business University, Yantai, People’s Republic of China

2. Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia

3. Computer Science Department, Faculty of Applied Science, Taiz University, Taiz, Yemen

4. Future Technology Research Center, National Yunlin University of Science and Technology, Douliu, Taiwan

5. Department of Information Management, International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliu, Taiwan

6. Department of Information Management, National Yunlin University of Science and Technology, Douliu, Taiwan

7. Bachelor Program of Big Data Applications in Business, National Pingtung University, Pingtung, Taiwan

8. Water Engineering Department, Urmia University, Urmia, Iran

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/19942060.2024.2304094

Reference49 articles.

1. Comparative implementation between neuro-emotional genetic algorithm and novel ensemble computing techniques for modelling dissolved oxygen concentration

2. Hybrid Machine Learning Ensemble Techniques for Modeling Dissolved Oxygen Concentration

3. Modeling Multistep Ahead Dissolved Oxygen Concentration Using Improved Support Vector Machines by a Hybrid Metaheuristic Algorithm

4. The development of dissolved oxygen forecast model using hybrid machine learning algorithm with hydro-meteorological variables

5. Dissolved oxygen concentration predictions for running waters with different land use land cover using a quantile regression forest machine learning technique

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