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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:

Streamflow estimation by support vector machine coupled with different methods of time series decomposition in the upper reaches of Yangtze River, China
Shuang Zhu, Jianzhong Zhou, Lei Ye, et al.
Environmental Earth Sciences (2016) Vol. 75, Iss. 6
Closed Access | Times Cited: 95

Showing 1-25 of 95 citing articles:

Flood Prediction Using Machine Learning Models: Literature Review
Amir Mosavi, Pınar Öztürk, Kwok‐wing Chau
Water (2018) Vol. 10, Iss. 11, pp. 1536-1536
Open Access | Times Cited: 474
Flood Prediction Using Machine Learning, Literature Review
Amir Mosavi, Pınar Öztürk, Kwok‐wing Chau
(2018)
Open Access | Times Cited: 225
Decomposition ensemble model based on variational mode decomposition and long short-term memory for streamflow forecasting
Ganggang Zuo, Jungang Luo, Ni Wang, et al.
Journal of Hydrology (2020) Vol. 585, pp. 124776-124776
Closed Access | Times Cited: 207
Flood susceptibility mapping using multi-temporal SAR imagery and novel integration of nature-inspired algorithms into support vector regression
Soroosh Mehravar, Seyed Vahid Razavi-Termeh, Armin Moghimi, et al.
Journal of Hydrology (2023) Vol. 617, pp. 129100-129100
Open Access | Times Cited: 76
Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling
Keighobad Jafarzadegan, Hamid Moradkhani, Florian Pappenberger, et al.
Reviews of Geophysics (2023) Vol. 61, Iss. 2
Open Access | Times Cited: 67
Hybrid forecasting model for non-stationary daily runoff series: A case study in the Han River Basin, China
Tuo Xie, Gang Zhang, Jinwang Hou, et al.
Journal of Hydrology (2019) Vol. 577, pp. 123915-123915
Closed Access | Times Cited: 131
Comparison of Multiple Linear Regression, Artificial Neural Network, Extreme Learning Machine, and Support Vector Machine in Deriving Operation Rule of Hydropower Reservoir
Wen-jing Niu, Zhong-kai Feng, Bao-fei Feng, et al.
Water (2019) Vol. 11, Iss. 1, pp. 88-88
Open Access | Times Cited: 115
Streamflow Prediction Using Deep Learning Neural Network: Case Study of Yangtze River
Darong Liu, Wenchao Jiang, Lin Mu, et al.
IEEE Access (2020) Vol. 8, pp. 90069-90086
Open Access | Times Cited: 114
Development of novel hybridized models for urban flood susceptibility mapping
Omid Rahmati, Hamid Darabi, Mahdi Panahi, et al.
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 107
Daily runoff forecasting by deep recursive neural network
Jiang-Wei Zhang, Xiaohui Chen, Amirul Khan, et al.
Journal of Hydrology (2021) Vol. 596, pp. 126067-126067
Closed Access | Times Cited: 94
Data Pre-Analysis and Ensemble of Various Artificial Neural Networks for Monthly Streamflow Forecasting
Jianzhong Zhou, Peng Tian, Chu Zhang, et al.
Water (2018) Vol. 10, Iss. 5, pp. 628-628
Open Access | Times Cited: 86
Stacking ensemble learning models for daily runoff prediction using 1D and 2D CNNs
Yutong Xie, Wei Sun, Miaomiao Ren, et al.
Expert Systems with Applications (2022) Vol. 217, pp. 119469-119469
Closed Access | Times Cited: 67
Artificial intelligence for suspended sediment load prediction: a review
Deepak Gupta, Barenya Bikash Hazarika, M. Berlin, et al.
Environmental Earth Sciences (2021) Vol. 80, Iss. 9
Closed Access | Times Cited: 61
An adaptive daily runoff forecast model using VMD-LSTM-PSO hybrid approach
Wang Xiu-jie, Yanpeng Wang, Pei-Xian Yuan, et al.
Hydrological Sciences Journal (2021) Vol. 66, Iss. 9, pp. 1488-1502
Closed Access | Times Cited: 59
Combining two-stage decomposition based machine learning methods for annual runoff forecasting
Shu Chen, Miaomiao Ren, Wei Sun
Journal of Hydrology (2021) Vol. 603, pp. 126945-126945
Closed Access | Times Cited: 56
Deep insight into daily runoff forecasting based on a CNN-LSTM model
Huiqi Deng, Wenjie Chen, Guoru Huang
Natural Hazards (2022) Vol. 113, Iss. 3, pp. 1675-1696
Closed Access | Times Cited: 46
Improved Transformer Model for Enhanced Monthly Streamflow Predictions of the Yangtze River
Chuanfeng Liu, Darong Liu, Lin Mu
IEEE Access (2022) Vol. 10, pp. 58240-58253
Open Access | Times Cited: 44
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
Enhancing drought prediction precision with EEMD-ARIMA modeling based on standardized precipitation index
Reza Rezaiy, Ani Shabri
Water Science & Technology (2024) Vol. 89, Iss. 3, pp. 745-770
Open Access | Times Cited: 10
Hybrid Models Combining EMD/EEMD and ARIMA for Long-Term Streamflow Forecasting
Zhiyu Wang, Jun Qiu, Fang‐Fang Li
Water (2018) Vol. 10, Iss. 7, pp. 853-853
Open Access | Times Cited: 80
Artificial Neural Network and Support Vector Machine Models for Inflow Prediction of Dam Reservoir (Case Study: Zayandehroud Dam Reservoir)
Mohammad Babaei, Ramtin Moeini, Eghbal Ehsanzadeh
Water Resources Management (2019) Vol. 33, Iss. 6, pp. 2203-2218
Closed Access | Times Cited: 59
Calibration of SWAT and Two Data-Driven Models for a Data-Scarce Mountainous Headwater in Semi-Arid Konya Closed Basin
Cihangir Köyceğiz, Meral Büyükyıldız
Water (2019) Vol. 11, Iss. 1, pp. 147-147
Open Access | Times Cited: 55
A multiscale long short-term memory model with attention mechanism for improving monthly precipitation prediction
Lizhi Tao, Xinguang He, Jiajia Li, et al.
Journal of Hydrology (2021) Vol. 602, pp. 126815-126815
Closed Access | Times Cited: 46
Reconstructing Daily Discharge in a Megadelta Using Machine Learning Techniques
Hung Vo Thanh, Đoàn Văn Bình, Sameh A. Kantoush, et al.
Water Resources Research (2022) Vol. 58, Iss. 5
Open Access | Times Cited: 29
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