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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model
Rahim Barzegar, Mohammad Taghi Aalami, Jan Adamowski
Stochastic Environmental Research and Risk Assessment (2020) Vol. 34, Iss. 2, pp. 415-433
Closed Access | Times Cited: 400

Showing 1-25 of 400 citing articles:

A comprehensive review of deep learning applications in hydrology and water resources
Muhammed Sit, Bekir Zahit Demiray, Zhongrun Xiang, et al.
Water Science & Technology (2020) Vol. 82, Iss. 12, pp. 2635-2670
Open Access | Times Cited: 393
From Hydrometeorology to River Water Quality: Can a Deep Learning Model Predict Dissolved Oxygen at the Continental Scale?
Wei Zhi, Dapeng Feng, Wen‐Ping Tsai, et al.
Environmental Science & Technology (2021) Vol. 55, Iss. 4, pp. 2357-2368
Closed Access | Times Cited: 207
Prediction of Water Level and Water Quality Using a CNN-LSTM Combined Deep Learning Approach
Sang‐Soo Baek, JongCheol Pyo, Jong Ahn Chun
Water (2020) Vol. 12, Iss. 12, pp. 3399-3399
Open Access | Times Cited: 191
Water quality classification using machine learning algorithms
Nida Nasir, Afreen Kansal, Omar Alshaltone, et al.
Journal of Water Process Engineering (2022) Vol. 48, pp. 102920-102920
Closed Access | Times Cited: 189
Machine learning in natural and engineered water systems
Ruixing Huang, Chengxue Ma, Jun Ma, et al.
Water Research (2021) Vol. 205, pp. 117666-117666
Closed Access | Times Cited: 188
Application of machine learning in intelligent fish aquaculture: A review
Shili Zhao, Song Zhang, Jincun Liu, et al.
Aquaculture (2021) Vol. 540, pp. 736724-736724
Closed Access | Times Cited: 181
Coupling a hybrid CNN-LSTM deep learning model with a Boundary Corrected Maximal Overlap Discrete Wavelet Transform for multiscale Lake water level forecasting
Rahim Barzegar, Mohammad Taghi Aalami, Jan Adamowski
Journal of Hydrology (2021) Vol. 598, pp. 126196-126196
Closed Access | Times Cited: 153
Modelling and Prediction of Water Quality by Using Artificial Intelligence
Mosleh Hmoud Al-Adhaileh, Fawaz Waselallah Alsaade
Sustainability (2021) Vol. 13, Iss. 8, pp. 4259-4259
Open Access | Times Cited: 150
Estimation of SPEI Meteorological Drought Using Machine Learning Algorithms
Ali Mokhtar, Mohammadnabi Jalali, Hongming He, et al.
IEEE Access (2021) Vol. 9, pp. 65503-65523
Open Access | Times Cited: 140
Intelligent fish farm—the future of aquaculture
Cong Wang, Zhen Li, Tan Wang, et al.
Aquaculture International (2021) Vol. 29, Iss. 6, pp. 2681-2711
Open Access | Times Cited: 134
Water quality prediction and classification based on principal component regression and gradient boosting classifier approach
Md. Saikat Islam Khan, Nazrul Islam, Jia Uddin, et al.
Journal of King Saud University - Computer and Information Sciences (2021) Vol. 34, Iss. 8, pp. 4773-4781
Open Access | Times Cited: 133
A study on water quality prediction by a hybrid CNN-LSTM model with attention mechanism
Yurong Yang, Qingyu Xiong, Chao Wu, et al.
Environmental Science and Pollution Research (2021) Vol. 28, Iss. 39, pp. 55129-55139
Closed Access | Times Cited: 104
Water Quality Prediction for Smart Aquaculture Using Hybrid Deep Learning Models
K. P. Rasheed Abdul Haq, V. P. Harigovindan
IEEE Access (2022) Vol. 10, pp. 60078-60098
Open Access | Times Cited: 89
Analysis and prediction of water quality using deep learning and auto deep learning techniques
D. Venkata Vara Prasad, Lokeswari Venkataramana, P. Senthil Kumar, et al.
The Science of The Total Environment (2022) Vol. 821, pp. 153311-153311
Closed Access | Times Cited: 86
Oil well production prediction based on CNN-LSTM model with self-attention mechanism
Shaowei Pan, Bo Yang, Shukai Wang, et al.
Energy (2023) Vol. 284, pp. 128701-128701
Closed Access | Times Cited: 83
Short-Term Solar Power Predicting Model Based on Multi-Step CNN Stacked LSTM Technique
Neethu Elizabeth Michael, Manohar Mishra, Shazia Hasan, et al.
Energies (2022) Vol. 15, Iss. 6, pp. 2150-2150
Open Access | Times Cited: 76
Deep learning in hydrology and water resources disciplines: concepts, methods, applications, and research directions
Kumar Puran Tripathy, Ashok K. Mishra
Journal of Hydrology (2023) Vol. 628, pp. 130458-130458
Closed Access | Times Cited: 75
Application of Machine Learning in Water Resources Management: A Systematic Literature Review
Fatemeh Ghobadi, Doosun Kang
Water (2023) Vol. 15, Iss. 4, pp. 620-620
Open Access | Times Cited: 73
Prediction modelling framework comparative analysis of dissolved oxygen concentration variations using support vector regression coupled with multiple feature engineering and optimization methods: A case study in China
Xizhi Nong, Laifei Cheng, Lihua Chen, et al.
Ecological Indicators (2023) Vol. 146, pp. 109845-109845
Open Access | Times Cited: 60
Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions
Tao Hai, Sani I. Abba, Ahmed M. Al‐Areeq, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 129, pp. 107559-107559
Closed Access | Times Cited: 57
Predicting lake water quality index with sensitivity-uncertainty analysis using deep learning algorithms
Swapan Talukdar, Shahfahad, Shakeel Ahmed, et al.
Journal of Cleaner Production (2023) Vol. 406, pp. 136885-136885
Closed Access | Times Cited: 53
Developing a novel tool for assessing the groundwater incorporating water quality index and machine learning approach
Abdul Majed Sajib, Mir Talas Mahammad Diganta, Azizur Rahman, et al.
Groundwater for Sustainable Development (2023) Vol. 23, pp. 101049-101049
Open Access | Times Cited: 50
Improving LSTM hydrological modeling with spatiotemporal deep learning and multi-task learning: A case study of three mountainous areas on the Tibetan Plateau
Bu Li, Ruidong Li, Ting Sun, et al.
Journal of Hydrology (2023) Vol. 620, pp. 129401-129401
Open Access | Times Cited: 44
Hybrid WT–CNN–GRU-based model for the estimation of reservoir water quality variables considering spatio-temporal features
Mohammad Zamani, Mohammad Reza Nikoo, Ghazi Al-Rawas, et al.
Journal of Environmental Management (2024) Vol. 358, pp. 120756-120756
Closed Access | Times Cited: 21
A Critical Review of RNN and LSTM Variants in Hydrological Time Series Predictions
Muhammad Waqas, Usa Wannasingha Humphries
MethodsX (2024) Vol. 13, pp. 102946-102946
Open Access | Times Cited: 17
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