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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 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

Showing 23 citing articles:

Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm
Masoud Karbasi, Mumtaz Ali, Sayed M. Bateni, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 6
Modeling the effect of meteorological drought on lake level changes with machine learning techniques
Özlem Terzi, Dilek Taylan, Tahsin Baykal
Elsevier eBooks (2025), pp. 227-246
Closed Access
Exploring the applicability of the experiment-based ANN and LSTM models for streamflow estimation
Muhammed Ernur Akıner, Veysi Kartal, Anıl Can Güzeler, et al.
Earth Science Informatics (2024) Vol. 17, Iss. 4, pp. 3111-3135
Open Access | Times Cited: 3
Predicting mine water inflow volumes using a decomposition-optimization algorithm-machine learning approach
Jiaxin Bian, Tao Hou, Dengjun Ren, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 3
An Approach for Future Droughts in Northwest Türkiye: SPI and LSTM Methods
Dilek Taylan
Sustainability (2024) Vol. 16, Iss. 16, pp. 6905-6905
Open Access | Times Cited: 3
Multimodal Fusion of Optimized GRU–LSTM with Self-Attention Layer for Hydrological Time Series Forecasting
Hüseyin Çağan Kılınç, Sina Apak, Furkan Ozkan, et al.
Water Resources Management (2024) Vol. 38, Iss. 15, pp. 6045-6062
Closed Access | Times Cited: 3
Hybrid deep learning models for multi-ahead river water level forecasting
Abul Kashem, Pobithra Das, Md. Mahmudul Hasan, et al.
Earth Science Informatics (2024) Vol. 17, Iss. 4, pp. 3021-3037
Closed Access | Times Cited: 2
Mountain Flood Level Forecasting in Small Watersheds Based on Recurrent Neural Networks and Multi-Dimensional Data
Songsong Wang, Ouguan Xu
IEEE Access (2024) Vol. 12, pp. 82706-82719
Open Access | Times Cited: 2
Predicting rainfall using machine learning, deep learning, and time series models across an altitudinal gradient in the North-Western Himalayas
Owais Ali Wani, Syed Sheraz Mahdi, Md Yeasin, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access | Times Cited: 2
A SOM-LSTM combined model for groundwater level prediction in karst critical zone aquifers considering connectivity characteristics
Fei Guo, Shilong Li, Gang Zhao, et al.
Environmental Earth Sciences (2024) Vol. 83, Iss. 9
Open Access | Times Cited: 1
Diel temperature patterns unveiled: High-frequency monitoring and deep learning in Lake Kasumigaura
Senlin Zhu, Ryuichiro Shinohara, Shin‐ichiro S. Matsuzaki, et al.
Ecological Indicators (2024) Vol. 169, pp. 112958-112958
Open Access | Times Cited: 1
Uncertainty Assessment of Ensemble Base Machine Learning Modeling for Multi-step Ahead Forecasting of Dam Reservoir Inflows
Vahid Nourani, Bagher Nikoufar, Nardin Jabbarian Paknezhad, et al.
Iranian Journal of Science and Technology Transactions of Civil Engineering (2024)
Closed Access | Times Cited: 1
Deep Learning Ensemble for Flood Probability Analysis
Fred Sseguya, Kyung Soo Jun
Water (2024) Vol. 16, Iss. 21, pp. 3092-3092
Open Access | Times Cited: 1
Incorporating Dynamic Drainage Supervision into Deep Learning for Accurate Real-Time Flood Simulation in Urban Areas
Hancheng Ren, Bo Pang, Gang Zhao, et al.
Water Research (2024) Vol. 270, pp. 122816-122816
Closed Access | Times Cited: 1
Time Series Analysis of Visitor Trends at Pratama Mitra Sehat Clinic, Kabupaten Sukoharjo, Using LSTM
Yusephus Decupertino Sumanto, Susilo Hariyanto
International Journal of Software & Hardware Research in Engineering (2024) Vol. 12, Iss. 5, pp. 12-17
Open Access
A novel hybrid approach based on outlier and error correction methods to predict river discharge using meteorological variables
Maha Shabbir, Sohail Chand, Farhat Iqbal
Environmental and Ecological Statistics (2024)
Closed Access
A hybrid model for monthly runoff forecasting based on mixed signal processing and machine learning
Shu Chen, Wei Sun, Miaomiao Ren, et al.
Environmental Science and Pollution Research (2024) Vol. 31, Iss. 57, pp. 65866-65883
Closed Access
Global monthly sea surface temperature forecasting using the SARIMA, LSTM, and GRU models
Mehmet Bilgili, Engin Pınar, Tahir Durhasan
Earth Science Informatics (2024) Vol. 18, Iss. 1
Closed Access
Impact Assessment of Coupling Mode of Hydrological Model and Machine Learning Model on Runoff Simulation: A Case of Washington
Junqi Zhang, Jing Li, Hua Zhao, et al.
Atmosphere (2024) Vol. 15, Iss. 12, pp. 1461-1461
Open Access
Assessing the Performance of Machine Learning Algorithms for Water Level Prediction in the Chao Phraya River and its Tributaries: A Focus on Low and High Water Levels
Wilmat D.S.M. Priyasiri, Areeya Rittima, Jidapa Kraisangka, et al.
(2024)
Closed Access
A novel hybrid model by integrating TCN with TVFEMD and permutation entropy for monthly non-stationary runoff prediction
Huifang Wang, Xuehua Zhao, Qiucen Guo, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access
A maximal overlap discrete wavelet packet transform coupled with an LSTM deep learning model for improving multilevel groundwater level forecasts
Dilip Kumar Roy, Ahmed A. Hashem, Michele L. Reba, et al.
Discover Water (2024) Vol. 4, Iss. 1
Open Access
Variational mode decomposition coupled LSTM with encoder-decoder framework: an efficient method for daily streamflow forecasting
Jiadong Liu, Teng Xu, Chunhui Lu, et al.
Earth Science Informatics (2024) Vol. 18, Iss. 1
Closed Access
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