Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques-Reference-Cited by-同舟云学术

Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques

Published:2021-01-01 Issue:1 Volume:15 Page:1420-1439
ISSN:1994-2060
Container-title:Engineering Applications of Computational Fluid Mechanics
language:en
Short-container-title:Engineering Applications of Computational Fluid Mechanics

Author:

Afan Haitham Abdulmohsin¹^ORCID,Ibrahem Ahmed Osman Ahmedbahaaaldin²,Essam Yusuf²,Ahmed Ali Najah³^ORCID,Huang Yuk Feng⁴,Kisi Ozgur⁵⁶^ORCID,Sherif Mohsen⁷^ORCID,Sefelnasr Ahmed⁷^ORCID,Chau Kwok-wing⁸^ORCID,El-Shafie Ahmed⁷⁹^ORCID

Affiliation:

1. Department of Civil Engineering, Al-Maarif University College, Ramadi, Iraq

2. Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), Kajang, Malaysia

3. Institute for Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN), Kajang, Malaysia

4. Department of Civil Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Selangor, Malaysia

5. Civil Engineering Department, Ilia State University, Tbilisi, Georgia

6. Institute of Research and Development, Duy Tan University, Da Nang, Viet Nam

7. National Water and Energy Center, United Arab Emirates University, Al Ain, United Arab Emirates

8. Department of Civil and Environmental Engineering, Hong Kong Polytechnic University, Hung Hom, Hong Kong

9. Department of Civil Engineering, Faculty of Engineering, University of Malaya (UM), Kuala Lumpur, Malaysia

Funder

Office of the Associate Provost for Research, UAE University

Publisher

Informa UK Limited

Subject

Modeling and Simulation,General Computer Science

Link

https://www.tandfonline.com/doi/pdf/10.1080/19942060.2021.1974093

Reference45 articles.

1. A wavelet neural network conjunction model for groundwater level forecasting

2. Advanced machine learning model for better prediction accuracy of soil temperature at different depths

3. ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module

4. Enhancement of Groundwater-Level Prediction Using an Integrated Machine Learning Model Optimized by Whale Algorithm

5. Forecasting of groundwater level fluctuations using ensemble hybrid multi-wavelet neural network-based models

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