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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:

Improving daily streamflow simulations for data-scarce watersheds using the coupled SWAT-LSTM approach
Shengyue Chen, Jinliang Huang, Jr‐Chuan Huang
Journal of Hydrology (2023) Vol. 622, pp. 129734-129734
Closed Access | Times Cited: 55

Showing 1-25 of 55 citing articles:

A review of hybrid deep learning applications for streamflow forecasting
Kin‐Wang Ng, Yuk Feng Huang, Chai Hoon Koo, et al.
Journal of Hydrology (2023) Vol. 625, pp. 130141-130141
Closed Access | Times Cited: 75
Unraveling the Interactions between Flooding Dynamics and Agricultural Productivity in a Changing Climate
Thidarat Rupngam, Aimé J. Messiga
Sustainability (2024) Vol. 16, Iss. 14, pp. 6141-6141
Open Access | Times Cited: 21
Coupling the remote sensing data-enhanced SWAT model with the bidirectional long short-term memory model to improve daily streamflow simulations
Lei Jin, Huazhu Xue, Guotao Dong, et al.
Journal of Hydrology (2024) Vol. 634, pp. 131117-131117
Open Access | Times Cited: 16
Deep learning model based on coupled SWAT and interpretable methods for water quality prediction under the influence of non-point source pollution
Juan Huan, Yixiong Fan, Xiangen Xu, et al.
Computers and Electronics in Agriculture (2025) Vol. 231, pp. 109985-109985
Closed Access | Times Cited: 1
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
Enhancing flow rate prediction of the Chao Phraya River Basin using SWAT–LSTM model coupling
Kritnipit Phetanan, Seok Min Hong, Daeun Yun, et al.
Journal of Hydrology Regional Studies (2024) Vol. 53, pp. 101820-101820
Open Access | Times Cited: 11
Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review
Bisrat Ayalew Yifru, Kyoung Jae Lim, Seoro Lee
Sustainability (2024) Vol. 16, Iss. 4, pp. 1376-1376
Open Access | Times Cited: 9
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
A new interpretable streamflow prediction approach based on SWAT-BiLSTM and SHAP
Feiyun Huang, Xuyue Zhang
Environmental Science and Pollution Research (2024) Vol. 31, Iss. 16, pp. 23896-23908
Closed Access | Times Cited: 7
Enhancing daily streamflow simulation using the coupled SWAT-BiLSTM approach for climate change impact assessment in Hai-River Basin
Xianqi Zhang, Yu Qi, Fang Liu, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 20
Assessing the response of non-point source nitrogen pollution to land use change based on SWAT model
Xianqi Zhang, Yu Qi, Haiyang Li, et al.
Ecological Indicators (2023) Vol. 158, pp. 111391-111391
Open Access | Times Cited: 20
A comparative survey between cascade correlation neural network (CCNN) and feedforward neural network (FFNN) machine learning models for forecasting suspended sediment concentration
Bhupendra Joshi, Vijay Kumar Singh, Dinesh Kumar Vishwakarma, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 6
Comparison and integration of physical and interpretable AI-driven models for rainfall-runoff simulation
Sara Asadi, Patricia Jimeno‐Sáez, Adrián López-Ballesteros, et al.
Results in Engineering (2024) Vol. 24, pp. 103048-103048
Open Access | Times Cited: 6
Improving estimation capacity of a hybrid model of LSTM and SWAT by reducing parameter uncertainty
Hyemin Jeong, Byeongwon Lee, Dongho Kim, et al.
Journal of Hydrology (2024) Vol. 633, pp. 130942-130942
Closed Access | Times Cited: 5
Exploring the potential of deep learning for streamflow forecasting: A comparative study with hydrological models for seasonal and perennial rivers
Ardalan Izadi, Nastaran Zarei, Mohammad Reza Nikoo, et al.
Expert Systems with Applications (2024) Vol. 252, pp. 124139-124139
Closed Access | Times Cited: 5
Enhancing physically-based hydrological modeling with an ensemble of machine-learning reservoir operation modules under heavy human regulation using easily accessible data
Tongbi Tu, Yilan Li, Kai Duan, et al.
Journal of Environmental Management (2024) Vol. 359, pp. 121044-121044
Closed Access | Times Cited: 5
Streamflow Simulation Using a Hybrid Approach Combining HEC-HMS and LSTM Model in the Tlawng River Basin of Mizoram, India
Sagar Debbarma, Arnab Bandyopadhyay, Aditi Bhadra
Environmental Modeling & Assessment (2025)
Closed Access
Spatiotemporal variations of surface and groundwater interactions under climate and land use land cover change scenarios
Kotapati Narayana Loukika, K. Venkata Reddy, Eswar Sai Buri, et al.
Frontiers in Water (2025) Vol. 6
Open Access
Coupled SWAT, Stationary Wavelet Transform, and Interpretable Machine Learning to Improve Watershed Streamflow Simulation
Chengqing Ren, Jianxia Chang, Xuebin Wang, et al.
Water Resources Management (2025)
Closed Access
Incorporating multi-timescale data in a single long short-term memory network to enhance reservoir-regulated streamflow simulation
Laura Lang, Xing Gao, Yongkun Li, et al.
Journal of Hydrology (2025), pp. 132806-132806
Closed Access
Coupling SWAT+ with LSTM for enhanced and interpretable streamflow estimation in arid and semi-arid watersheds, a case study of the Tagus Headwaters River Basin, Spain
Sara Asadi, Patricia Jimeno‐Sáez, Adrián López-Ballesteros, et al.
Environmental Modelling & Software (2025), pp. 106360-106360
Open Access
Integrating machine learning with process-based glacio-hydrological model for improving the performance of runoff simulation in cold regions
Babak Mohammadi, Hongkai Gao, Petter Pilesjö, et al.
Journal of Hydrology (2025), pp. 132963-132963
Open Access
Improving land surface model accuracy in soil moisture simulations using parametric schemes and machine learning
Xi Zhao, Chiyuan Miao, Jinlong Hu, et al.
Journal of Hydrology (2025), pp. 133109-133109
Closed Access
Enhancing daily runoff prediction: A hybrid model combining GR6J-CemaNeige with wavelet-based gradient boosting technique
Babak Mohammadi, Mingjie Chen, Mohammad Reza Nikoo, et al.
Journal of Hydrology (2025), pp. 133114-133114
Closed Access
Predicting Effects of Non-Point Source Pollution Emission Control Schemes Based on VMD-BiLSTM and MIKE21
Xianqi Zhang, Yu Qi, Fang Liu, et al.
Environmental Modeling & Assessment (2024)
Open Access | Times Cited: 4
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