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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 new approach for simulating and forecasting the rainfall-runoff process within the next two months
Mohamad Javad Alizadeh, Mohammad Reza Kavianpour, Özgür Kişi, et al.
Journal of Hydrology (2017) Vol. 548, pp. 588-597
Closed Access | Times Cited: 107

Showing 1-25 of 107 citing articles:

Remote sensing and machine learning for crop water stress determination in various crops: a critical review
Shyamal S. Virnodkar, Vinod Pachghare, V. C. Patil, et al.
Precision Agriculture (2020) Vol. 21, Iss. 5, pp. 1121-1155
Closed Access | Times Cited: 237
Long lead-time daily and monthly streamflow forecasting using machine learning methods
Meiling Cheng, F. Fang, Tsuyoshi Kinouchi, et al.
Journal of Hydrology (2020) Vol. 590, pp. 125376-125376
Closed Access | Times Cited: 227
Addressing the incorrect usage of wavelet-based hydrological and water resources forecasting models for real-world applications with best practices and a new forecasting framework
John Quilty, Jan Adamowski
Journal of Hydrology (2018) Vol. 563, pp. 336-353
Closed Access | Times Cited: 211
Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years
Magali Troin, Richard Arsenault, Andrew W. Wood, et al.
Water Resources Research (2021) Vol. 57, Iss. 7
Open Access | Times Cited: 151
Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity
A. A. Masrur Ahmed, Ravinesh C. Deo, Qi Feng, et al.
Journal of Hydrology (2021) Vol. 599, pp. 126350-126350
Closed Access | Times Cited: 112
Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters
Shahaboddin Shamshirband, Ehsan Jafari Nodoushan, Jason E. Adolf, et al.
Engineering Applications of Computational Fluid Mechanics (2018) Vol. 13, Iss. 1, pp. 91-101
Open Access | Times Cited: 159
Evapotranspiration estimation using four different machine learning approaches in different terrestrial ecosystems
Xianming Dou, Yongguo Yang
Computers and Electronics in Agriculture (2018) Vol. 148, pp. 95-106
Closed Access | Times Cited: 142
A Comparative Study of MLR, KNN, ANN and ANFIS Models with Wavelet Transform in Monthly Stream Flow Prediction
Ahmad Khazaee Poul, Mojtaba Shourian, Hadi Ebrahimi
Water Resources Management (2019) Vol. 33, Iss. 8, pp. 2907-2923
Closed Access | Times Cited: 132
Two-phase extreme learning machines integrated with the complete ensemble empirical mode decomposition with adaptive noise algorithm for multi-scale runoff prediction problems
Xiaohu Wen, Qi Feng, Ravinesh C. Deo, et al.
Journal of Hydrology (2019) Vol. 570, pp. 167-184
Closed Access | Times Cited: 129
Uncertainty Analysis of Climate Change Impacts on Flood Frequency by Using Hybrid Machine Learning Methods
Mahdi Valikhan Anaraki, Saeed Farzin, Sayed‐Farhad Mousavi, et al.
Water Resources Management (2020) Vol. 35, Iss. 1, pp. 199-223
Closed Access | Times Cited: 111
Short term rainfall-runoff modelling using several machine learning methods and a conceptual event-based model
Rana Muhammad Adnan, Andrea Petroselli‬, Salim Heddam, et al.
Stochastic Environmental Research and Risk Assessment (2020) Vol. 35, Iss. 3, pp. 597-616
Closed Access | Times Cited: 87
LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenarios
A. A. Masrur Ahmed, Ravinesh C. Deo, Afshin Ghahramani, et al.
Stochastic Environmental Research and Risk Assessment (2021) Vol. 35, Iss. 9, pp. 1851-1881
Closed Access | Times Cited: 65
Robust Runoff Prediction With Explainable Artificial Intelligence and Meteorological Variables From Deep Learning Ensemble Model
Junhao Wu, Zhaocai Wang, Jinghan Dong, et al.
Water Resources Research (2023) Vol. 59, Iss. 9
Closed Access | Times Cited: 30
A Novel Physics‐Aware Machine Learning‐Based Dynamic Error Correction Model for Improving Streamflow Forecast Accuracy
Abhinanda Roy, K. S. Kasiviswanathan, Sandhya Patidar, et al.
Water Resources Research (2023) Vol. 59, Iss. 2
Closed Access | Times Cited: 24
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: 10
Toward multi-day-ahead forecasting of suspended sediment concentration using ensemble models
Mohamad Javad Alizadeh, Ehsan Jafari Nodoushan, Naghi Kalarestaghi, et al.
Environmental Science and Pollution Research (2017) Vol. 24, Iss. 36, pp. 28017-28025
Closed Access | Times Cited: 81
Daily river flow forecasting using ensemble empirical mode decomposition based heuristic regression models: Application on the perennial rivers in Iran and South Korea
Mohammad Rezaie-Balf, Sungwon Kim, Hossein Fallah, et al.
Journal of Hydrology (2019) Vol. 572, pp. 470-485
Closed Access | Times Cited: 73
Evaluating Different Machine Learning Models for Runoff and Suspended Sediment Simulation
Ashish Kumar, Pravendra Kumar, Vijay Kumar Singh
Water Resources Management (2019) Vol. 33, Iss. 3, pp. 1217-1231
Closed Access | Times Cited: 68
Evapotranspiration Estimation with Small UAVs in Precision Agriculture
Haoyu Niu, Derek Hollenbeck, Tiebiao Zhao, et al.
Sensors (2020) Vol. 20, Iss. 22, pp. 6427-6427
Open Access | Times Cited: 63
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
Simulation of rainfall-runoff process using an artificial neural network (ANN) and field plots data
Vahid Gholami, Hossein Sahour
Theoretical and Applied Climatology (2021) Vol. 147, Iss. 1-2, pp. 87-98
Closed Access | Times Cited: 41
Comparative study of machine learning methods and GR2M model for monthly runoff prediction
Pakorn Ditthakit, Sirimon Pinthong, Nureehan Salaeh, et al.
Ain Shams Engineering Journal (2022) Vol. 14, Iss. 4, pp. 101941-101941
Open Access | Times Cited: 33
Reconstructing Daily Discharge in a Megadelta Using Machine Learning Techniques
Hung Vo Thanh, Đoàn Văn Bình, Sameh A. Kantoush, et al.
Water Resources Research (2022) Vol. 58, Iss. 5
Open Access | Times Cited: 29
Comparative analysis of data driven rainfall-runoff models in the Kolar river basin
Deepak Kumar Tiwari, Vijendra Kumar, Anuj Goyal, et al.
Results in Engineering (2024) Vol. 23, pp. 102682-102682
Open Access | Times Cited: 6
Streamflow Forecasting Using Four Wavelet Transformation Combinations Approaches with Data-Driven Models: A Comparative Study
Sinan Jasim Hadi, Mustafa Tombul
Water Resources Management (2018) Vol. 32, Iss. 14, pp. 4661-4679
Closed Access | Times Cited: 56
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