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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:

Comparison of different methodologies for rainfall–runoff modeling: machine learning vs conceptual approach
Rana Muhammad Adnan, Andrea Petroselli‬, Salim Heddam, et al.
Natural Hazards (2021) Vol. 105, Iss. 3, pp. 2987-3011
Closed Access | Times Cited: 71

Showing 1-25 of 71 citing articles:

Study on optimization and combination strategy of multiple daily runoff prediction models coupled with physical mechanism and LSTM
Jun Guo, Yi Liu, Qiang Zou, et al.
Journal of Hydrology (2023) Vol. 624, pp. 129969-129969
Closed Access | Times Cited: 119
An Integrated Statistical-Machine Learning Approach for Runoff Prediction
Abhinav Kumar Singh, Pankaj Kumar, Rawshan Ali, et al.
Sustainability (2022) Vol. 14, Iss. 13, pp. 8209-8209
Open Access | Times Cited: 74
DeepGR4J: A deep learning hybridization approach for conceptual rainfall-runoff modelling
Arpit Kapoor, Sahani Pathiraja, Lucy Marshall, et al.
Environmental Modelling & Software (2023) Vol. 169, pp. 105831-105831
Open Access | Times Cited: 44
Improving River Routing Using a Differentiable Muskingum‐Cunge Model and Physics‐Informed Machine Learning
Tadd Bindas, Wen‐Ping Tsai, Jiangtao Liu, et al.
Water Resources Research (2024) Vol. 60, Iss. 1
Open Access | Times Cited: 27
Enhancing river flow predictions: Comparative analysis of machine learning approaches in modeling stage-discharge relationship
Özgür Kişi, Hazi Mohammad Azamathulla, Fatih Cevat, et al.
Results in Engineering (2024) Vol. 22, pp. 102017-102017
Open Access | Times Cited: 20
Hybridization of Stochastic Hydrological Models and Machine Learning Methods for Improving Rainfall-Runoff Modelling
Sianou Ezéckiel Houénafa, Olatunji Johnson, Erick Kiplangat Ronoh, et al.
Results in Engineering (2025), pp. 104079-104079
Open Access | Times Cited: 3
Daily streamflow forecasting in Sobradinho Reservoir using machine learning models coupled with wavelet transform and bootstrapping
Samuel Vitor Saraiva, Frede de Oliveira Carvalho, Celso Augusto Guimarães Santos, et al.
Applied Soft Computing (2021) Vol. 102, pp. 107081-107081
Closed Access | Times Cited: 94
A review on the applications of machine learning for runoff modeling
Babak Mohammadi
Sustainable Water Resources Management (2021) Vol. 7, Iss. 6
Open Access | Times Cited: 90
Optimal Design and Feature Selection by Genetic Algorithm for Emotional Artificial Neural Network (EANN) in Rainfall-Runoff Modeling
Amir Molajou, Vahid Nourani, Abbas Afshar, et al.
Water Resources Management (2021) Vol. 35, Iss. 8, pp. 2369-2384
Closed Access | Times Cited: 75
Deep insight into daily runoff forecasting based on a CNN-LSTM model
Huiqi Deng, Wenjie Chen, Guoru Huang
Natural Hazards (2022) Vol. 113, Iss. 3, pp. 1675-1696
Closed Access | Times Cited: 46
Inclusive Multiple Model Using Hybrid Artificial Neural Networks for Predicting Evaporation
Mohammad Ehteram, Fatemeh Panahi, Ali Najah Ahmed, et al.
Frontiers in Environmental Science (2022) Vol. 9
Open Access | Times Cited: 44
Improving the simulations of the hydrological model in the karst catchment by integrating the conceptual model with machine learning models
Cenk Sezen, Mojca Šraj
The Science of The Total Environment (2024) Vol. 926, pp. 171684-171684
Open Access | Times Cited: 15
In-depth simulation of rainfall–runoff relationships using machine learning methods
Mehdi Fuladipanah, Alireza Shahhosseini, Namal Rathnayake, et al.
Water Practice & Technology (2024) Vol. 19, Iss. 6, pp. 2442-2459
Open Access | Times Cited: 11
Modeling of stage-discharge using back propagation ANN-, ANFIS-, and WANN-based computing techniques
Ravi U. Shukla, Pravendra Kumar, Dinesh Kumar Vishwakarma, et al.
Theoretical and Applied Climatology (2021) Vol. 147, Iss. 3-4, pp. 867-889
Open Access | Times Cited: 42
Applying Machine Learning Methods to Improve Rainfall–Runoff Modeling in Subtropical River Basins
Haoyuan Yu, Qichun Yang
Water (2024) Vol. 16, Iss. 15, pp. 2199-2199
Open Access | Times Cited: 7
Fine-tuning inflow prediction models: integrating optimization algorithms and TRMM data for enhanced accuracy
Enas Ali, Bilel Zerouali, Aqil Tariq, et al.
Water Science & Technology (2024) Vol. 90, Iss. 3, pp. 844-877
Open Access | Times Cited: 6
Assessing machine learning models for streamflow estimation: a case study in Oued Sebaou watershed (Northern Algeria)
Zaki Abda, Bilel Zerouali, Mohamed Chettih, et al.
Hydrological Sciences Journal (2022) Vol. 67, Iss. 9, pp. 1328-1341
Closed Access | Times Cited: 25
Forecasting monthly pan evaporation using hybrid additive regression and data-driven models in a semi-arid environment
Ahmed Elbeltagi, Mustafa Al-Mukhtar, Nand Lal Kushwaha, et al.
Applied Water Science (2022) Vol. 13, Iss. 2
Open Access | Times Cited: 25
Numerical and Experimental Investigation of Meteorological Data Using Adaptive Linear M5 Model Tree for the Prediction of Rainfall
Sheikh Amir Fayaz, Majid Zaman, Muheet Ahmed Butt
Review of Computer Engineering Research (2022) Vol. 9, Iss. 1, pp. 1-12
Open Access | Times Cited: 24
Convergence of mechanistic modeling and artificial intelligence in hydrologic science and engineering
Rafael Muñoz‐Carpena, Álvaro Carmona-Cabrero, Ziwen Yu, et al.
PLOS Water (2023) Vol. 2, Iss. 8, pp. e0000059-e0000059
Open Access | Times Cited: 13
Parameter Estimation for Some Probability Distributions Used in Hydrology
Cristian Gabriel Anghel, Cornel Ilinca
Applied Sciences (2022) Vol. 12, Iss. 24, pp. 12588-12588
Open Access | Times Cited: 20
Prediction of reservoir evaporation considering water temperature and using ANFIS hybridized with metaheuristic algorithms
Boudjerda Marouane, Mu’azu Mohammed Abdullahi, Andrea Petroselli‬
Earth Science Informatics (2024) Vol. 17, Iss. 2, pp. 1779-1798
Closed Access | Times Cited: 4
Analyzing the effects of data splitting and covariate shift on machine learning based streamflow prediction in ungauged basins
William Crossley, Sayan Dey, Venkatesh Merwade
Journal of Hydrology (2025), pp. 132731-132731
Closed Access
Integration of Gaussian process regression and K means clustering for enhanced short term rainfall runoff modeling
Özgür Kişi, Salim Heddam, Kulwinder Singh Parmar, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Assessing the effectiveness of artificial intelligence approaches for streamflow modeling in the Indravathi subbasin, India
Subbarayan Saravanan, Nagireddy Masthan Reddy
Environment Development and Sustainability (2025)
Closed Access
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