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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:

A physical process and machine learning combined hydrological model for daily streamflow simulations of large watersheds with limited observation data
Shuyu Yang, Dawen Yang, Jinsong Chen, et al.
Journal of Hydrology (2020) Vol. 590, pp. 125206-125206
Open Access | Times Cited: 183

Showing 1-25 of 183 citing articles:

Improving streamflow prediction in the WRF-Hydro model with LSTM networks
Kyeungwoo Cho, Yeonjoo Kim
Journal of Hydrology (2021) Vol. 605, pp. 127297-127297
Open Access | Times Cited: 216
Physics-guided deep learning for rainfall-runoff modeling by considering extreme events and monotonic relationships
Kang Xie, Pan Liu, Jianyun Zhang, et al.
Journal of Hydrology (2021) Vol. 603, pp. 127043-127043
Closed Access | Times Cited: 146
Spatial heterogeneity modeling of water quality based on random forest regression and model interpretation
Feier Wang, Yixu Wang, Kai Zhang, et al.
Environmental Research (2021) Vol. 202, pp. 111660-111660
Closed Access | Times Cited: 142
Artificial intelligence modelling integrated with Singular Spectral analysis and Seasonal-Trend decomposition using Loess approaches for streamflow predictions
Halit Apaydın, Mohammad Taghi Sattari, Kambiz Falsafian, et al.
Journal of Hydrology (2021) Vol. 600, pp. 126506-126506
Closed Access | Times Cited: 112
Stacked machine learning algorithms and bidirectional long short-term memory networks for multi-step ahead streamflow forecasting: A comparative study
Francesco Granata, Fabio Di Nunno, Giovanni de Marinis
Journal of Hydrology (2022) Vol. 613, pp. 128431-128431
Closed Access | Times Cited: 103
Analysis of runoff generation driving factors based on hydrological model and interpretable machine learning method
Shuo Wang, Hui Peng, Qin Hu, et al.
Journal of Hydrology Regional Studies (2022) Vol. 42, pp. 101139-101139
Open Access | Times Cited: 92
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: 59
Integrating Machine Learning and AI for Improved Hydrological Modeling and Water Resource Management
Djabeur Mohamed Seifeddine Zekrifa, Megha Kulkarni, A Bhagyalakshmi, et al.
Advances in environmental engineering and green technologies book series (2023), pp. 46-70
Closed Access | Times Cited: 59
Enhancing flood risk assessment through integration of ensemble learning approaches and physical-based hydrological modeling
Mohamed Saber, Tayeb Boulmaiz, Mawloud Guermoui, et al.
Geomatics Natural Hazards and Risk (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 47
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
Multiple spatio-temporal scale runoff forecasting and driving mechanism exploration by K-means optimized XGBoost and SHAP
Shuo Wang, Hui Peng
Journal of Hydrology (2024) Vol. 630, pp. 130650-130650
Closed Access | Times Cited: 24
Modeling streamflow in non-gauged watersheds with sparse data considering physiographic, dynamic climate, and anthropogenic factors using explainable soft computing techniques
Charuni I. Madhushani, K. G. S. Dananjaya, I.U. Ekanayake, et al.
Journal of Hydrology (2024) Vol. 631, pp. 130846-130846
Closed Access | Times Cited: 23
A critical review of machine learning algorithms in maritime, offshore, and oil & gas corrosion research: A comprehensive analysis of ANN and RF models
Md Mahadi Hasan Imran, Shahrizan Jamaludin, Ahmad Faisal Mohamad Ayob
Ocean Engineering (2024) Vol. 295, pp. 116796-116796
Closed Access | Times Cited: 18
IHACRES, GR4J and MISD-based multi conceptual-machine learning approach for rainfall-runoff modeling
Babak Mohammadi, Mir Jafar Sadegh Safari, Saeed Vazifehkhah
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 65
A novel ensemble-based conceptual-data-driven approach for improved streamflow simulations
Anna E. Sikorska‐Senoner, John Quilty
Environmental Modelling & Software (2021) Vol. 143, pp. 105094-105094
Open Access | Times Cited: 63
Ensemble streamflow forecasting over a cascade reservoir catchment with integrated hydrometeorological modeling and machine learning
Junjiang Liu, Xing Yuan, Junhan Zeng, et al.
Hydrology and earth system sciences (2022) Vol. 26, Iss. 2, pp. 265-278
Open Access | Times Cited: 61
Predictive performance of ensemble hydroclimatic forecasts: Verification metrics, diagnostic plots and forecast attributes
Zeqing Huang, Tongtiegang Zhao
Wiley Interdisciplinary Reviews Water (2022) Vol. 9, Iss. 2
Open Access | Times Cited: 46
Comparison of machine learning and process-based SWAT model in simulating streamflow in the Upper Indus Basin
Khalil Ur Rahman, Quoc Bao Pham, Khan Zaib Jadoon, et al.
Applied Water Science (2022) Vol. 12, Iss. 8
Open Access | Times Cited: 44
Multi-objective operation of cascade reservoirs based on short-term ensemble streamflow prediction
Shaokun He, Shenglian Guo, Jiayu Zhang, et al.
Journal of Hydrology (2022) Vol. 610, pp. 127936-127936
Closed Access | Times Cited: 42
Modelling river flow in cold and ungauged regions: a review of the purposes, methods, and challenges
Chiara Belvederesi, Mohamed Sherif Zaghloul, Gopal Achari, et al.
Environmental Reviews (2022) Vol. 30, Iss. 1, pp. 159-173
Open Access | Times Cited: 37
Developing a Physics‐Informed Deep Learning Model to Simulate Runoff Response to Climate Change in Alpine Catchments
L. Zhong, Huimin Lei, Bing Gao
Water Resources Research (2023) Vol. 59, Iss. 6
Closed Access | Times Cited: 35
Deep Learning Approach with LSTM for Daily Streamflow Prediction in a Semi-Arid Area: A Case Study of Oum Er-Rbia River Basin, Morocco
Karima Nifa, Abdelghani Boudhar, Hamza Ouatiki, et al.
Water (2023) Vol. 15, Iss. 2, pp. 262-262
Open Access | Times Cited: 32
High temporal resolution urban flood prediction using attention-based LSTM models
Lin Zhang, Huapeng Qin, Junqi Mao, et al.
Journal of Hydrology (2023) Vol. 620, pp. 129499-129499
Closed Access | Times Cited: 31
Future changes in water resources, floods and droughts under the joint impact of climate and land-use changes in the Chao Phraya basin, Thailand
Shuyu Yang, Baoxu Zhao, Dawen Yang, et al.
Journal of Hydrology (2023) Vol. 620, pp. 129454-129454
Open Access | Times Cited: 26
Ensemble physically based semi-distributed models for the rainfall-runoff process modeling in the data-scarce Katar catchment, Ethiopia
Gebre Gelete, Vahid Nourani, Hüseyin Gökçekuş, et al.
Journal of Hydroinformatics (2023) Vol. 25, Iss. 2, pp. 567-592
Open Access | Times Cited: 24
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