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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:

Improving streamflow prediction using a new hybrid ELM model combined with hybrid particle swarm optimization and grey wolf optimization
Rana Muhammad Adnan, Reham R. Mostafa, Özgür Kişi, et al.
Knowledge-Based Systems (2021) Vol. 230, pp. 107379-107379
Closed Access | Times Cited: 162

Showing 1-25 of 162 citing articles:

Integrated framework of extreme learning machine (ELM) based on improved atom search optimization for short-term wind speed prediction
Lei Hua, Chu Zhang, Peng Tian, et al.
Energy Conversion and Management (2021) Vol. 252, pp. 115102-115102
Closed Access | Times Cited: 125
Forecasting wholesale prices of yellow corn through the Gaussian process regression
Bingzi Jin, Xiaojie Xu
Neural Computing and Applications (2024) Vol. 36, Iss. 15, pp. 8693-8710
Closed Access | Times Cited: 122
A novel hybrid of meta-optimization approach for flash flood-susceptibility assessment in a monsoon-dominated watershed, Eastern India
Dipankar Ruidas, Rabin Chakrabortty, Abu Reza Md. Towfiqul Islam, et al.
Environmental Earth Sciences (2022) Vol. 81, Iss. 5
Closed Access | Times Cited: 92
Combining autoregressive integrated moving average with Long Short-Term Memory neural network and optimisation algorithms for predicting ground water level
Zohreh Sheikh Khozani, Fatemeh Barzegari Banadkooki, Mohammad Ehteram, et al.
Journal of Cleaner Production (2022) Vol. 348, pp. 131224-131224
Closed Access | Times Cited: 86
Application of Machine Learning in Water Resources Management: A Systematic Literature Review
Fatemeh Ghobadi, Doosun Kang
Water (2023) Vol. 15, Iss. 4, pp. 620-620
Open Access | Times Cited: 72
Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions
Tao Hai, Sani I. Abba, Ahmed M. Al‐Areeq, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 129, pp. 107559-107559
Closed Access | Times Cited: 56
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: 42
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: 27
Runoff prediction using a multi-scale two-phase processing hybrid model
Xuehua Zhao, Huifang Wang, Qiucen Guo, et al.
Stochastic Environmental Research and Risk Assessment (2025)
Closed Access | Times Cited: 1
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
Offshore wind speed short-term forecasting based on a hybrid method: Swarm decomposition and meta-extreme learning machine
Emrah Dokur, Nuh Erdoğan, Mahdi Ebrahimi Salari, et al.
Energy (2022) Vol. 248, pp. 123595-123595
Open Access | Times Cited: 58
Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms
Yusuf Essam, Yuk Feng Huang, Jing Lin Ng, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 53
Modelling groundwater level fluctuations by ELM merged advanced metaheuristic algorithms using hydroclimatic data
Rana Muhammad Adnan, Hongliang Dai, Reham R. Mostafa, et al.
Geocarto International (2022) Vol. 38, Iss. 1
Open Access | Times Cited: 46
An Investigation on Hybrid Particle Swarm Optimization Algorithms for Parameter Optimization of PV Cells
Abha Singh, Abhishek Sharma, Shailendra Rajput, et al.
Electronics (2022) Vol. 11, Iss. 6, pp. 909-909
Open Access | Times Cited: 45
Enhancing robustness of monthly streamflow forecasting model using embedded-feature selection algorithm based on improved gray wolf optimizer
Qingjie Wang, Chunfang Yue, Xiaoqing Li, et al.
Journal of Hydrology (2022) Vol. 617, pp. 128995-128995
Closed Access | Times Cited: 45
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: 43
Modeling Multistep Ahead Dissolved Oxygen Concentration Using Improved Support Vector Machines by a Hybrid Metaheuristic Algorithm
Rana Muhammad Adnan, Hongliang Dai, Reham R. Mostafa, et al.
Sustainability (2022) Vol. 14, Iss. 6, pp. 3470-3470
Open Access | Times Cited: 39
Daily Streamflow Forecasting Based on the Hybrid Particle Swarm Optimization and Long Short-Term Memory Model in the Orontes Basin
Hüseyin Çağan Kılınç
Water (2022) Vol. 14, Iss. 3, pp. 490-490
Open Access | Times Cited: 38
A new hybrid model for monthly runoff prediction using ELMAN neural network based on decomposition-integration structure with local error correction method
Dongmei Xu, Xiao-xue Hu, Wenchuan Wang, et al.
Expert Systems with Applications (2023) Vol. 238, pp. 121719-121719
Closed Access | Times Cited: 34
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: 29
Improving the forecasting accuracy of monthly runoff time series of the Brahmani River in India using a hybrid deep learning model
Sonali Swagatika, Jagadish Chandra Paul, Bibhuti Bhusan Sahoo, et al.
Journal of Water and Climate Change (2023) Vol. 15, Iss. 1, pp. 139-156
Open Access | Times Cited: 23
A Comparison of Artificial Neural Network and Time Series Models for Timber Price Forecasting
Anna Kożuch, Dominika Cywicka, Krzysztof Adamowicz
Forests (2023) Vol. 14, Iss. 2, pp. 177-177
Open Access | Times Cited: 22
A conceptual metaheuristic-based framework for improving runoff time series simulation in glacierized catchments
Babak Mohammadi, Saeed Vazifehkhah, Zheng Duan
Engineering Applications of Artificial Intelligence (2023) Vol. 127, pp. 107302-107302
Open Access | Times Cited: 22
DTTR: Encoding and decoding monthly runoff prediction model based on deep temporal attention convolution and multimodal fusion
Wenchuan Wang, Wei-can Tian, Xiao-xue Hu, et al.
Journal of Hydrology (2024) Vol. 643, pp. 131996-131996
Closed Access | Times Cited: 13
Improving performance of extreme learning machine for classification challenges by modified firefly algorithm and validation on medical benchmark datasets
Nebojša Bačanin, Cătălin Stoean, Dušan Marković, et al.
Multimedia Tools and Applications (2024) Vol. 83, Iss. 31, pp. 76035-76075
Closed Access | Times Cited: 8
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