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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:

Data Decomposition, Seasonal Adjustment Method and Machine Learning Combined for Runoff Prediction: A Case Study
Yang Hao, Weide Li
Water Resources Management (2022) Vol. 37, Iss. 1, pp. 557-581
Closed Access | Times Cited: 18

Showing 18 citing articles:

A Hybrid CNN-LSTM Approach for Monthly Reservoir Inflow Forecasting
Saeed Khorram, Nima Jehbez
Water Resources Management (2023) Vol. 37, Iss. 10, pp. 4097-4121
Closed Access | Times Cited: 36
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
Quantitative improvement of streamflow forecasting accuracy in the Atlantic zones of Canada based on hydro-meteorological signals: A multi-level advanced intelligent expert framework
Mozhdeh Jamei, Mehdi Jamei, Mumtaz Ali, et al.
Ecological Informatics (2024) Vol. 80, pp. 102455-102455
Open Access | Times Cited: 10
Improved monthly runoff time series prediction using the CABES-LSTM mixture model based on CEEMDAN-VMD decomposition
Dong-mei Xu, An-dong Liao, Wenchuan Wang, et al.
Journal of Hydroinformatics (2023) Vol. 26, Iss. 1, pp. 255-283
Open Access | Times Cited: 19
Exploring Runoff Response to Simulated Rainfall: A Study of the Rising Limb of a Hydrograph on Sandy Slopes
Radha S. Mohril, Avinash D. Vasudeo
Water Resources Management (2025)
Closed Access
Enhancing monthly runoff prediction: a data-driven framework integrating variational mode decomposition, enhanced artificial rabbit optimization, support vector regression, and error correction
Ning He, Wenchuan Wang
Earth Science Informatics (2025) Vol. 18, Iss. 3
Closed Access
EFFECT OF SEASONAL-TREND DECOMPOSITION ON MACHINE LEARNING-BASED SUSPENDED SEDIMENT LOAD PREDICTION PERFORMANCE
Cihangir Köyceğiz, Meral Büyükyıldız
Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi (2025) Vol. 28, Iss. 1, pp. 1-18
Open Access
Geographic heterogeneity of activation functions in urban real-time flood forecasting: Based on seasonal trend decomposition using Loess-Temporal Convolutional Network-Gated Recurrent Unit model
Songhua Huan
Journal of Hydrology (2024) Vol. 636, pp. 131279-131279
Closed Access | Times Cited: 4
An Integrated Approach Using Lars-Wg and Deep Learning for River Flow Prediction in Diverse Regions
Fatemeh Avazpour, Mohammad Hadian, Ali Talebi, et al.
(2025)
Closed Access
Stochastic (S[ARIMA]), shallow (NARnet, NAR-GMDH, OS-ELM), and deep learning (LSTM, Stacked-LSTM, CNN-GRU) models, application to river flow forecasting
Marwan Kheimi, Mohammad Almadani, Mohammad Zounemat‐Kermani
Acta Geophysica (2023) Vol. 72, Iss. 4, pp. 2679-2693
Closed Access | Times Cited: 9
Runoff Prediction Under Extreme Precipitation and Corresponding Meteorological Conditions
Jinping Zhang, Dong Wang, Yuhao Wang, et al.
Water Resources Management (2023) Vol. 37, Iss. 9, pp. 3377-3394
Closed Access | Times Cited: 8
A runoff prediction method based on hyperparameter optimisation of a kernel extreme learning machine with multi-step decomposition
Xianqi Zhang, Fang Liu, Qiuwen Yin, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 7
Two-step hybrid model for monthly runoff prediction utilizing integrated machine learning algorithms and dual signal decompositions
Shujun Wu, Zengchuan Dong, Sandra M. Guzmán, et al.
Ecological Informatics (2024), pp. 102914-102914
Open Access | Times Cited: 1
Data-Driven and Knowledge-Guided Heterogeneous Graphs and Temporal Convolution Networks for Flood Forecasting
Pingping Shao, Jun Feng, Yirui Wu, et al.
Applied Sciences (2023) Vol. 13, Iss. 12, pp. 7191-7191
Open Access | Times Cited: 3
Monthly Runoff Forecasting Using a Climate‑Driven Model Based on Two-Stage Decomposition and Optimized Support Vector Regression
Zhuo Jia, Yuhao Peng, Qin Li, et al.
(2024)
Closed Access
Materials requirement prediction challenges addressed through SDM and MEIO
Tripathi Atuldev Ashok, T. Sathish, Ahmed Ahmed Ibrahim, et al.
AIP Advances (2024) Vol. 14, Iss. 5
Open Access
Research on Optimal Selection of Runoff Prediction Models Based on Coupled Machine Learning Methods
Wei Xing, M.M. Chen, Yulin Zhou, et al.
Research Square (Research Square) (2024)
Open Access
Research on optimal selection of runoff prediction models based on coupled machine learning methods
Wei Xing, Mengen Chen, Yulin Zhou, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access
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