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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:

Rainfall-runoff modelling using improved machine learning methods: Harris hawks optimizer vs. particle swarm optimization
Yazid Tikhamarine, Doudja Souag-Gamane, Ali Najah Ahmed, et al.
Journal of Hydrology (2020) Vol. 589, pp. 125133-125133
Closed Access | Times Cited: 116

Showing 1-25 of 116 citing articles:

Groundwater level prediction using machine learning models: A comprehensive review
Tao Hai, Mohammed Majeed Hameed, Haydar Abdulameer Marhoon, et al.
Neurocomputing (2022) Vol. 489, pp. 271-308
Open Access | Times Cited: 253
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
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: 116
An ensemble CNN-LSTM and GRU adaptive weighting model based improved sparrow search algorithm for predicting runoff using historical meteorological and runoff data as input
Zhiyuan Yao, Zhaocai Wang, Dangwei Wang, et al.
Journal of Hydrology (2023) Vol. 625, pp. 129977-129977
Closed Access | Times Cited: 91
Harris Hawks Optimization Algorithm: Variants and Applications
Mohammad Shehab, Ibrahim Mashal, Zaid Momani, et al.
Archives of Computational Methods in Engineering (2022) Vol. 29, Iss. 7, pp. 5579-5603
Closed Access | Times Cited: 72
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: 69
Iterative integration of deep learning in hybrid Earth surface system modelling
Min Chen, Zhen Qian, Niklas Boers, et al.
Nature Reviews Earth & Environment (2023) Vol. 4, Iss. 8, pp. 568-581
Closed Access | Times Cited: 53
Modeling Various Drought Time Scales via a Merged Artificial Neural Network with a Firefly Algorithm
Babak Mohammadi
Hydrology (2023) Vol. 10, Iss. 3, pp. 58-58
Open Access | Times Cited: 50
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
A Comprehensive Review of Machine Learning for Water Quality Prediction over the Past Five Years
Xiaohui Yan, Tianqi Zhang, Wenying Du, et al.
Journal of Marine Science and Engineering (2024) Vol. 12, Iss. 1, pp. 159-159
Open Access | Times Cited: 15
Prediction of meteorological drought by using hybrid support vector regression optimized with HHO versus PSO algorithms
Anurag Malik, Yazid Tikhamarine, Saad Sh. Sammen, et al.
Environmental Science and Pollution Research (2021) Vol. 28, Iss. 29, pp. 39139-39158
Closed Access | Times Cited: 97
Rainfall-runoff modeling using LSTM-based multi-state-vector sequence-to-sequence model
Hanlin Yin, Xiuwei Zhang, Fandu Wang, et al.
Journal of Hydrology (2021) Vol. 598, pp. 126378-126378
Closed Access | Times Cited: 96
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: 84
Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India
Kusum Pandey, Shiv Kumar, Anurag Malik, et al.
Sustainability (2020) Vol. 12, Iss. 21, pp. 8932-8932
Open Access | Times Cited: 81
Sizing optimization and design of an autonomous AC microgrid for commercial loads using Harris Hawks Optimization algorithm
İpek Çetinbaş, Bünyamin Tamyürek, Mehmet Demirtaş
Energy Conversion and Management (2021) Vol. 245, pp. 114562-114562
Closed Access | Times Cited: 66
Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network
Bhatawdekar Ramesh Murlidhar, Hoang Nguyen, Jamal Rostami, et al.
Journal of Rock Mechanics and Geotechnical Engineering (2021) Vol. 13, Iss. 6, pp. 1413-1427
Open Access | Times Cited: 66
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
Real-time rainfall-runoff prediction using light gradient boosting machine coupled with singular spectrum analysis
Zhongjie Cui, Qing Xiaoxia, Hongxiang Chai, et al.
Journal of Hydrology (2021) Vol. 603, pp. 127124-127124
Closed Access | Times Cited: 64
The potential of a novel support vector machine trained with modified mayfly optimization algorithm for streamflow prediction
Rana Muhammad Adnan, Özgür Kişi, Reham R. Mostafa, et al.
Hydrological Sciences Journal (2021) Vol. 67, Iss. 2, pp. 161-174
Closed Access | Times Cited: 60
A hybrid Harris hawks-moth-flame optimization algorithm including fractional-order chaos maps and evolutionary population dynamics
Mohamed Abd Elaziz, Dalia Yousri, Seyedali Mirjalili
Advances in Engineering Software (2021) Vol. 154, pp. 102973-102973
Closed Access | Times Cited: 57
Rainfall-runoff modeling using long short-term memory based step-sequence framework
Hanlin Yin, Fandu Wang, Xiuwei Zhang, et al.
Journal of Hydrology (2022) Vol. 610, pp. 127901-127901
Closed Access | Times Cited: 54
Application of Machine Learning and Process-Based Models for Rainfall-Runoff Simulation in DuPage River Basin, Illinois
Amrit Bhusal, Utsav Parajuli, Sushmita Regmi, et al.
Hydrology (2022) Vol. 9, Iss. 7, pp. 117-117
Open Access | Times Cited: 49
Water level prediction using various machine learning algorithms: a case study of Durian Tunggal river, Malaysia
Ali Najah Ahmed, Ayman Yafouz, Ahmed H. Birima, et al.
Engineering Applications of Computational Fluid Mechanics (2022) Vol. 16, Iss. 1, pp. 422-440
Open Access | Times Cited: 41
Harris Hawk Optimization: A Survey onVariants and Applications
B. K. Tripathy, Praveen Kumar Reddy Maddikunta, Quoc‐Viet Pham, et al.
Computational Intelligence and Neuroscience (2022) Vol. 2022, pp. 1-20
Open Access | Times Cited: 39
Which one is more important in daily runoff forecasting using data driven models: Input data, model type, preprocessing or data length?
Vahid Moosavi, Zeinab Gheisoori Fard, Mehdi Vafakhah
Journal of Hydrology (2022) Vol. 606, pp. 127429-127429
Closed Access | Times Cited: 37
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