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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:

Research on particle swarm optimization in LSTM neural networks for rainfall-runoff simulation
Yuanhao Xu, Caihong Hu, Qiang Wu, et al.
Journal of Hydrology (2022) Vol. 608, pp. 127553-127553
Closed Access | Times Cited: 243

Showing 1-25 of 243 citing articles:

Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review
Mehrdad Kaveh, Mohammad Saadi Mesgari
Neural Processing Letters (2022) Vol. 55, Iss. 4, pp. 4519-4622
Open Access | Times Cited: 131
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: 120
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: 104
Runoff Forecasting using Convolutional Neural Networks and optimized Bi-directional Long Short-term Memory
Tunhua Wu, Zhaocai Wang, Yuan Hu, et al.
Water Resources Management (2023) Vol. 37, Iss. 2, pp. 937-953
Closed Access | Times Cited: 84
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: 83
A novel model for water quality prediction caused by non-point sources pollution based on deep learning and feature extraction methods
Hang Wan, Rui Xu, Meng Zhang, et al.
Journal of Hydrology (2022) Vol. 612, pp. 128081-128081
Closed Access | Times Cited: 75
Deep transfer learning based on transformer for flood forecasting in data-sparse basins
Yuanhao Xu, Kairong Lin, Caihong Hu, et al.
Journal of Hydrology (2023) Vol. 625, pp. 129956-129956
Closed Access | Times Cited: 67
Particle swarm optimization based LSTM networks for water level forecasting: A case study on Bangladesh river network
Jannatul Ferdous Ruma, Mohammed Sarfaraz Gani Adnan, Ashraf Dewan, et al.
Results in Engineering (2023) Vol. 17, pp. 100951-100951
Open Access | Times Cited: 58
An enhanced monthly runoff time series prediction using extreme learning machine optimized by salp swarm algorithm based on time varying filtering based empirical mode decomposition
Wenchuan Wang, Qi Cheng, Kwok‐wing Chau, et al.
Journal of Hydrology (2023) Vol. 620, pp. 129460-129460
Closed Access | Times Cited: 46
Spatio-temporal deep learning model for accurate streamflow prediction with multi-source data fusion
Zhaocai Wang, Nannan Xu, Xiaoguang Bao, et al.
Environmental Modelling & Software (2024) Vol. 178, pp. 106091-106091
Closed Access | Times Cited: 41
A Novel Runoff Prediction Model Based on Support Vector Machine and Gate Recurrent unit with Secondary Mode Decomposition
Jinghan Dong, Zhaocai Wang, Tunhua Wu, et al.
Water Resources Management (2024) Vol. 38, Iss. 5, pp. 1655-1674
Closed Access | Times Cited: 31
How accurate are the machine learning models in improving monthly rainfall prediction in hyper arid environment?
Faisal Baig, Luqman Ali, Muhammad Abrar Faiz, et al.
Journal of Hydrology (2024) Vol. 633, pp. 131040-131040
Closed Access | Times Cited: 28
An interpretable hybrid deep learning model for flood forecasting based on Transformer and LSTM
Wenzhong Li, Chengshuai Liu, Yingying Xu, et al.
Journal of Hydrology Regional Studies (2024) Vol. 54, pp. 101873-101873
Open Access | Times Cited: 20
A runoff prediction approach based on machine learning, ensemble forecasting and error correction: A case study of source area of Yellow River
Jingyang Wang, Xiang Li, Ruiyan Wu, et al.
Journal of Hydrology (2025), pp. 133190-133190
Closed Access | Times Cited: 2
Flood Forecasting by Using Machine Learning: A Study Leveraging Historic Climatic Records of Bangladesh
Adel Rajab, Hira Farman, Noman Islam, et al.
Water (2023) Vol. 15, Iss. 22, pp. 3970-3970
Open Access | Times Cited: 40
An ensemble model for monthly runoff prediction using least squares support vector machine based on variational modal decomposition with dung beetle optimization algorithm and error correction strategy
Dongmei Xu, Zong Li, Wenchuan Wang
Journal of Hydrology (2023) Vol. 629, pp. 130558-130558
Closed Access | Times Cited: 40
A comparative study of data-driven models for runoff, sediment, and nitrate forecasting
Mohammad Zamani, Mohammad Reza Nikoo, Dana Rastad, et al.
Journal of Environmental Management (2023) Vol. 341, pp. 118006-118006
Closed Access | Times Cited: 35
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: 34
Long short-term memory models of water quality in inland water environments
JongCheol Pyo, Yakov Pachepsky, Soobin Kim, et al.
Water Research X (2023) Vol. 21, pp. 100207-100207
Open Access | Times Cited: 32
Rapid prediction of urban flood based on disaster-breeding environment clustering and Bayesian optimized deep learning model in the coastal city
Huiliang Wang, Shanlun Xu, Hongshi Xu, et al.
Sustainable Cities and Society (2023) Vol. 99, pp. 104898-104898
Closed Access | Times Cited: 29
BK-SWMM flood simulation framework is being proposed for urban storm flood modeling based on uncertainty parameter crowdsourcing data from a single functional region
Chengshuai Liu, Wenzhong Li, Chenchen Zhao, et al.
Journal of Environmental Management (2023) Vol. 344, pp. 118482-118482
Closed Access | Times Cited: 27
Application of a New Hybrid Deep Learning Model That Considers Temporal and Feature Dependencies in Rainfall–Runoff Simulation
Feng Zhou, Yangbo Chen, Jun Liu
Remote Sensing (2023) Vol. 15, Iss. 5, pp. 1395-1395
Open Access | Times Cited: 25
Streamflow forecasting using a hybrid LSTM-PSO approach: the case of Seyhan Basin
Bülent Haznedar, Hüseyin Çağan Kılınç, Furkan Ozkan, et al.
Natural Hazards (2023) Vol. 117, Iss. 1, pp. 681-701
Closed Access | Times Cited: 24
Application of a hybrid algorithm of LSTM and Transformer based on random search optimization for improving rainfall-runoff simulation
Wenzhong Li, Chengshuai Liu, Caihong Hu, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 17
Novel hybrid intelligence predictive model based on successive variational mode decomposition algorithm for monthly runoff series
Abbas Parsaie, Redvan Ghasemlounıa, Amin Gharehbaghi, et al.
Journal of Hydrology (2024) Vol. 634, pp. 131041-131041
Closed Access | Times Cited: 16
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