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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:

Prediction of harmful algal blooms in large water bodies using the combined EFDC and LSTM models
Lei Zheng, Huipeng Wang, Liu Cao, et al.
Journal of Environmental Management (2021) Vol. 295, pp. 113060-113060
Closed Access | Times Cited: 78

Showing 1-25 of 78 citing articles:

Water quality forecasting based on data decomposition, fuzzy clustering and deep learning neural network
Jin‐Won Yu, Ju-Song Kim, Xia Li, et al.
Environmental Pollution (2022) Vol. 303, pp. 119136-119136
Closed Access | Times Cited: 63
Algal bloom forecasting with time-frequency analysis: A hybrid deep learning approach
Muyuan Liu, Junyu He, Yuzhou Huang, et al.
Water Research (2022) Vol. 219, pp. 118591-118591
Closed Access | Times Cited: 52
Long-term prediction of sea surface chlorophyll-a concentration based on the combination of spatio-temporal features
Liu Na, Shaoyang Chen, Cheng Zhenyan, et al.
Water Research (2022) Vol. 211, pp. 118040-118040
Closed Access | Times Cited: 44
Simultaneous feature engineering and interpretation: Forecasting harmful algal blooms using a deep learning approach
TaeHo Kim, Jihoon Shin, Doyeon Lee, et al.
Water Research (2022) Vol. 215, pp. 118289-118289
Closed Access | Times Cited: 39
Ensemble Machine Learning of Gradient Boosting (XGBoost, LightGBM, CatBoost) and Attention-Based CNN-LSTM for Harmful Algal Blooms Forecasting
Jung Min Ahn, Jungwook Kim, Kyunghyun Kim
Toxins (2023) Vol. 15, Iss. 10, pp. 608-608
Open Access | Times Cited: 38
Harmful Cyanobacterial Blooms: Biological Traits, Mechanisms, Risks, and Control Strategies
Lirong Song, Yunlu Jia, Boqiang Qin, et al.
Annual Review of Environment and Resources (2023) Vol. 48, Iss. 1, pp. 123-147
Open Access | Times Cited: 36
Current status and prospects of algal bloom early warning technologies: A Review
X.L. Xiao, Yazhou Peng, Wei Zhang, et al.
Journal of Environmental Management (2023) Vol. 349, pp. 119510-119510
Closed Access | Times Cited: 27
Long short-term memory models of water quality in inland water environments
JongCheol Pyo, Yakov Pachepsky, Soobin Kim, et al.
Water Research X (2023) Vol. 21, pp. 100207-100207
Open Access | Times Cited: 26
Deep learning-based algorithms for long-term prediction of chlorophyll-a in catchment streams
Ather Abbas, Minji Park, Sang‐Soo Baek, et al.
Journal of Hydrology (2023) Vol. 626, pp. 130240-130240
Open Access | Times Cited: 22
A coupled model to improve river water quality prediction towards addressing non-stationarity and data limitation
Shengyue Chen, Jinliang Huang, Peng Wang, et al.
Water Research (2023) Vol. 248, pp. 120895-120895
Closed Access | Times Cited: 22
Analysis of pollutant dispersion patterns in rivers under different rainfall based on an integrated water-land model
Fei Lin, Honglei Ren, Jingsha Qin, et al.
Journal of Environmental Management (2024) Vol. 354, pp. 120314-120314
Closed Access | Times Cited: 8
Water quality prediction based on sparse dataset using enhanced machine learning
Huang Sheng, Jun Xia, Yueling Wang, et al.
Environmental Science and Ecotechnology (2024) Vol. 20, pp. 100402-100402
Open Access | Times Cited: 8
An Intelligent Early Warning System for Harmful Algal Blooms: Harnessing the Power of Big Data and Deep Learning
Jing Qian, Li Qian, Nan Pu, et al.
Environmental Science & Technology (2024) Vol. 58, Iss. 35, pp. 15607-15618
Closed Access | Times Cited: 8
A state-of-the-art review of long short-term memory models with applications in hydrology and water resources
Zhong-kai Feng, J. Zhang, Wen-jing Niu
Applied Soft Computing (2024), pp. 112352-112352
Closed Access | Times Cited: 8
Coupling Machine Learning Into Hydrodynamic Models to Improve River Modeling With Complex Boundary Conditions
Huang Sheng, Jun Xia, Yueling Wang, et al.
Water Resources Research (2022) Vol. 58, Iss. 10
Closed Access | Times Cited: 32
A novel hybrid water quality forecast model based on real-time data decomposition and error correction
Jusong Kim, Jin‐Won Yu, Chollyong Kang, et al.
Process Safety and Environmental Protection (2022) Vol. 162, pp. 553-565
Closed Access | Times Cited: 31
Development of a sensitivity analysis framework for aquatic biogeochemical models using machine learning
Haibin Cai, Yuko Shimoda, Jingqiao Mao, et al.
Ecological Informatics (2023) Vol. 75, pp. 102079-102079
Closed Access | Times Cited: 19
Causality Analysis and Prediction of Riverine Algal Blooms by Combining Empirical Dynamic Modeling and Machine Learning Techniques
Jing Tian, Gangsheng Wang, Daifeng Xiang, et al.
Water Resources Research (2024) Vol. 60, Iss. 5
Open Access | Times Cited: 7
Effects of riparian pioneer plants on soil aggregate stability: Roles of root traits and rhizosphere microorganisms
Xiaoxiao Wang, Ping Huang, Maohua Ma, et al.
The Science of The Total Environment (2024) Vol. 940, pp. 173584-173584
Closed Access | Times Cited: 7
Chlorophyll-a Detection Algorithms at Different Depths Using In Situ, Meteorological, and Remote Sensing Data in a Chilean Lake
Lien Rodríguez‐López, Denisse Álvarez, David Francisco Bustos Usta, et al.
Remote Sensing (2024) Vol. 16, Iss. 4, pp. 647-647
Open Access | Times Cited: 6
Dissolved Oxygen Forecasting for Lake Erie’s Central Basin Using Hybrid Long Short-Term Memory and Gated Recurrent Unit Networks
Daiwei Pan, Yue Zhang, Ying Deng, et al.
Water (2024) Vol. 16, Iss. 5, pp. 707-707
Open Access | Times Cited: 6
Temporal prediction of algal parameters in Three Gorges Reservoir based on highly time-resolved monitoring and long short-term memory network
Kun Shan, Tian Ouyang, Xiaoxiao Wang, et al.
Journal of Hydrology (2021) Vol. 605, pp. 127304-127304
Closed Access | Times Cited: 36
Applying Deep Learning in the Prediction of Chlorophyll-a in the East China Sea
Haobin Cen, Jiahan Jiang, Guoqing Han, et al.
Remote Sensing (2022) Vol. 14, Iss. 21, pp. 5461-5461
Open Access | Times Cited: 26
Preemptive warning and control strategies for algal blooms in the downstream of Han River, China
Jing Tian, Shenglian Guo, Jun Wang, et al.
Ecological Indicators (2022) Vol. 142, pp. 109190-109190
Open Access | Times Cited: 25
Combining physical-based model and machine learning to forecast chlorophyll-a concentration in freshwater lakes
Cheng Chen, Qiuwen Chen, Siyang Yao, et al.
The Science of The Total Environment (2023) Vol. 907, pp. 168097-168097
Closed Access | Times Cited: 15
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