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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:

Effects of Training Data on the Learning Performance of LSTM Network for Runoff Simulation
Anbang Peng, Xiaoli Zhang, Wei Xu, et al.
Water Resources Management (2022) Vol. 36, Iss. 7, pp. 2381-2394
Closed Access | Times Cited: 27

Showing 1-25 of 27 citing articles:

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: 118
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: 60
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
Machine learning method is an alternative for the hydrological model in an alpine catchment in the Tianshan region, Central Asia
Wenting Liang, Yaning Chen, Gonghuan Fang, et al.
Journal of Hydrology Regional Studies (2023) Vol. 49, pp. 101492-101492
Open Access | Times Cited: 18
Improving Hydrological Modeling with Hybrid Models: A Comparative Study of Different Mechanisms for Coupling Deep Learning Models with Process-based Models
Yiming Wei, Renchao Wang, Ping Feng
Water Resources Management (2024) Vol. 38, Iss. 7, pp. 2471-2488
Closed Access | Times Cited: 6
Monthly Runoff Prediction Via Mode Decomposition-Recombination Technique
Xi Yang, Zhihe Chen, Min Qin
Water Resources Management (2023) Vol. 38, Iss. 1, pp. 269-286
Closed Access | Times Cited: 15
Review of Machine Learning Methods for River Flood Routing
Li Li, Kyung Soo Jun
Water (2024) Vol. 16, Iss. 2, pp. 364-364
Open Access | Times Cited: 4
Temporal Forecasting of Distributed Temperature Sensing in a Thermal Hydraulic System with Machine Learning and Statistical Models
Stella Pantopoulou, M. Weathered, Darius Lisowski, et al.
IEEE Access (2025) Vol. 13, pp. 10252-10264
Open Access
The dynamics of lowland river sections of Danube and Tisza in the Carpathian basin
Imre M. Jánosi, István Zsuffa, Tibor Bíró, et al.
Frontiers in Earth Science (2025) Vol. 13
Open Access
MVIE-LSTM: a deep learning-based method for water quality assessment using monthly river data
Sha Xiong, Junjie Cui, Feifei Hou
Stochastic Environmental Research and Risk Assessment (2025)
Closed Access
Transferred Long Short-Term Memory Network for River Flow Forecasting in Data-Scarce Basins
Zhenglei Xie, Wei Xu, Bing Zhu, et al.
Water Resources Management (2025)
Closed Access
A novel smoothing-based long short-term memory framework for short-to medium-range flood forecasting
Amina Khatun, Chandranath Chatterjee, Gaurav Sahu, et al.
Hydrological Sciences Journal (2023) Vol. 68, Iss. 3, pp. 488-506
Closed Access | Times Cited: 10
Filling the gap between GRACE and GRACE-FO data using a model integrating variational mode decomposition and long short-term memory: a case study of Northwest China
Jiangdong Chu, Xiaoling Su, Tianliang Jiang, et al.
Environmental Earth Sciences (2023) Vol. 82, Iss. 1
Closed Access | Times Cited: 9
A Comparative Analysis of Multiple Machine Learning Methods for Flood Routing in the Yangtze River
Liwei Zhou, Ling Kang
Water (2023) Vol. 15, Iss. 8, pp. 1556-1556
Open Access | Times Cited: 9
Effects of Climate Change and Human Activities on Runoff in the Upper Reach of Jialing River, China
Wei-Zhao Shi, Yi He, Yiting Shao
Remote Sensing (2024) Vol. 16, Iss. 13, pp. 2481-2481
Open Access | Times Cited: 3
Review of Machine Learning Methods for River Flood Routing
Li Li, Kyung Soo Jun
(2024)
Open Access | Times Cited: 2
Quantifying the impacts of climate change and human activities on ecological flow security based on a new framework
Hongxiang Wang, Siyuan Cheng, Xiangyu Bai, et al.
Ecohydrology (2024) Vol. 17, Iss. 6
Closed Access | Times Cited: 2
An improved nonlinear dynamical model for monthly runoff prediction for data scarce basins
Longxia Qian, Nanjun Liu, Mei Hong, et al.
Stochastic Environmental Research and Risk Assessment (2024) Vol. 38, Iss. 10, pp. 3771-3798
Closed Access | Times Cited: 2
Drought driving mechanism and risk situation prediction based on machine learning models in the Yellow River Basin, China
Ling Kang, Yunliang Wen, Liwei Zhou, et al.
Geomatics Natural Hazards and Risk (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 6
Development of a One-Parameter New Exponential (ONE) Model for Simulating Rainfall-Runoff and Comparison with Data-Driven LSTM Model
Jaenam Lee, Jae-Kyoung Noh
Water (2023) Vol. 15, Iss. 6, pp. 1036-1036
Open Access | Times Cited: 2
Multi-source meteorological data assessment on daily runoff simulation in the upper reaches of the Hei River, Northwest China
Huazhu Xue, Y.X. Wang, Guotao Dong, et al.
Journal of Hydrology Regional Studies (2024) Vol. 57, pp. 102100-102100
Closed Access
Effects of stacking LSTM with different patterns and input schemes on streamflow and water quality simulation
Yucong Hu, Yan Jiang, Huiting Yao, et al.
Research Square (Research Square) (2024)
Open Access
Counterfactual Predictions in Shared Markets: A Global Forecasting Approach with Deep Learning and Spillover Considerations
Priscila Grecov, Klaus Ackermann, Christoph Bergmeir
SSRN Electronic Journal (2024)
Closed Access
A Intensified Approach on Aspect Based Sentiment Analysis Using Pre-Trained Deep Learning Model
V. Sri Vinitha, S.K. Manju Bargavi
(2024), pp. 1-5
Closed Access
Advances in Hydroinformatics—SimHydro 2023 Volume 2

Springer water (2024)
Closed Access
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