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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:

An improved long short-term memory network for streamflow forecasting in the upper Yangtze River
Shuang Zhu, Xiangang Luo, Xiaohui Yuan, et al.
Stochastic Environmental Research and Risk Assessment (2020) Vol. 34, Iss. 9, pp. 1313-1329
Closed Access | Times Cited: 125

Showing 1-25 of 125 citing articles:

A comprehensive review of deep learning applications in hydrology and water resources
Muhammed Sit, Bekir Zahit Demiray, Zhongrun Xiang, et al.
Water Science & Technology (2020) Vol. 82, Iss. 12, pp. 2635-2670
Open Access | Times Cited: 395
Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States
Kieran M. R. Hunt, Gwyneth Matthews, Florian Pappenberger, et al.
Hydrology and earth system sciences (2022) Vol. 26, Iss. 21, pp. 5449-5472
Open Access | Times Cited: 79
Short term rainfall-runoff modelling using several machine learning methods and a conceptual event-based model
Rana Muhammad Adnan, Andrea Petroselli‬, Salim Heddam, et al.
Stochastic Environmental Research and Risk Assessment (2020) Vol. 35, Iss. 3, pp. 597-616
Closed Access | Times Cited: 87
Rapid forecasting of urban flood inundation using multiple machine learning models
Jingming Hou, Nie Zhou, Guangzhao Chen, et al.
Natural Hazards (2021) Vol. 108, Iss. 2, pp. 2335-2356
Closed Access | Times Cited: 84
Streamflow simulation in data-scarce basins using Bayesian and physics-informed machine learning models
Dan Lu, Goutam Konapala, Scott Painter, et al.
Journal of Hydrometeorology (2021)
Open Access | Times Cited: 70
Effective improvement of multi-step-ahead flood forecasting accuracy through encoder-decoder with an exogenous input structure
Zhen Cui, Yanlai Zhou, Shenglian Guo, et al.
Journal of Hydrology (2022) Vol. 609, pp. 127764-127764
Open Access | Times Cited: 59
Utilization of the Long Short-Term Memory network for predicting streamflow in ungauged basins in Korea
Jeonghyeon Choi, Jeonghoon Lee, Sangdan Kim
Ecological Engineering (2022) Vol. 182, pp. 106699-106699
Closed Access | Times Cited: 38
Streamflow Prediction Utilizing Deep Learning and Machine Learning Algorithms for Sustainable Water Supply Management
Sarmad Dashti Latif, Ali Najah Ahmed
Water Resources Management (2023) Vol. 37, Iss. 8, pp. 3227-3241
Closed Access | Times Cited: 37
A deep-learning-technique-based data-driven model for accurate and rapid flood predictions in temporal and spatial dimensions
Qianqian Zhou, Shuai Teng, Zuxiang Situ, et al.
Hydrology and earth system sciences (2023) Vol. 27, Iss. 9, pp. 1791-1808
Open Access | Times Cited: 23
Optimization of LSTM Parameters for Flash Flood Forecasting Using Genetic Algorithm
You-Da Jhong, Chang‐Shian Chen, Bing-Chen Jhong, et al.
Water Resources Management (2024) Vol. 38, Iss. 3, pp. 1141-1164
Closed Access | Times Cited: 14
Improving urban flood prediction using LSTM-DeepLabv3+ and Bayesian optimization with spatiotemporal feature fusion
Zuxiang Situ, Qi Wang, Shuai Teng, et al.
Journal of Hydrology (2024) Vol. 630, pp. 130743-130743
Closed Access | Times Cited: 14
Enhancing Flooding Depth Forecasting Accuracy in an Urban Area Using a Novel Trend Forecasting Method
Song-Yue Yang, You-Da Jhong, Bing-Chen Jhong, et al.
Water Resources Management (2024) Vol. 38, Iss. 4, pp. 1359-1380
Closed Access | Times Cited: 10
Evaluation of streamflow predictions from LSTM models in water- and energy-limited regions in the United States
Kul Khand, G. B. Senay
Machine Learning with Applications (2024) Vol. 16, pp. 100551-100551
Open Access | Times Cited: 8
A state-of-the-art review of long short-term memory models with applications in hydrology and water resources
Zhong-kai Feng, J. Zhang, Wen-jing Niu
Applied Soft Computing (2024), pp. 112352-112352
Closed Access | Times Cited: 8
A Comprehensive Review of Deep Learning Applications in Hydrology and Water Resources
Muhammed Sit, Bekir Zahit Demiray, Zhongrun Xiang, et al.
EarthArXiv (California Digital Library) (2020)
Open Access | Times Cited: 54
Concepts, procedures, and applications of artificial neural network models in streamflow forecasting
Arash Malekian, Nastaran Chitsaz
Elsevier eBooks (2021), pp. 115-147
Closed Access | Times Cited: 54
Comparison of multiple learning artificial intelligence models for estimation of long-term monthly temperatures in Turkey
Hatice Çıtakoğlu
Arabian Journal of Geosciences (2021) Vol. 14, Iss. 20
Closed Access | Times Cited: 51
A large-scale comparison of Artificial Intelligence and Data Mining (AI&DM) techniques in simulating reservoir releases over the Upper Colorado Region
Tiantian Yang, Lujun Zhang, Taereem Kim, et al.
Journal of Hydrology (2021) Vol. 602, pp. 126723-126723
Open Access | Times Cited: 50
Spatiotemporal modeling of land subsidence using a geographically weighted deep learning method based on PS-InSAR
Huijun Li, Lin Zhu, Zhenxue Dai, et al.
The Science of The Total Environment (2021) Vol. 799, pp. 149244-149244
Closed Access | Times Cited: 47
Uncertainty quantification for hydrological models based on neural networks: the dropout ensemble
Daniel Althoff, Lineu Neiva Rodrigues, Helizani Couto Bazame
Stochastic Environmental Research and Risk Assessment (2021) Vol. 35, Iss. 5, pp. 1051-1067
Closed Access | Times Cited: 45
Application of machine learning algorithms in hydrology
Hamidreza Mosaffa, Mojtaba Sadeghi, Iman Mallakpour, et al.
Elsevier eBooks (2021), pp. 585-591
Closed Access | Times Cited: 45
Capabilities of deep learning models on learning physical relationships: Case of rainfall-runoff modeling with LSTM
Kazuki Yokoo, Kei Ishida, Ali Ercan, et al.
The Science of The Total Environment (2021) Vol. 802, pp. 149876-149876
Open Access | Times Cited: 44
Performance improvement of machine learning models via wavelet theory in estimating monthly river streamflow
Kegang Wang, Shahab S. Band, Rasoul Ameri, et al.
Engineering Applications of Computational Fluid Mechanics (2022) Vol. 16, Iss. 1, pp. 1833-1848
Open Access | Times Cited: 36
Spatiotemporal deep learning rainfall-runoff forecasting combined with remote sensing precipitation products in large scale basins
Shuang Zhu, Jianan Wei, Hairong Zhang, et al.
Journal of Hydrology (2022) Vol. 616, pp. 128727-128727
Closed Access | Times Cited: 34
Daily Streamflow Forecasts Based on Cascade Long Short-Term Memory (LSTM) Model over the Yangtze River Basin
Jiayuan Li, Xing Yuan
Water (2023) Vol. 15, Iss. 6, pp. 1019-1019
Open Access | Times Cited: 19
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