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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:

Developing a novel framework for forecasting groundwater level fluctuations using Bi-directional Long Short-Term Memory (BiLSTM) deep neural network
Redvan Ghasemlounıa, Amin Gharehbaghi, Farshad Ahmadi, et al.
Computers and Electronics in Agriculture (2021) Vol. 191, pp. 106568-106568
Closed Access | Times Cited: 48

Showing 1-25 of 48 citing articles:

A Comprehensive Review of Conventional, Machine Leaning, and Deep Learning Models for Groundwater Level (GWL) Forecasting
Junaid Khan, Eunkyu Lee, Awatef Salem Balobaid, et al.
Applied Sciences (2023) Vol. 13, Iss. 4, pp. 2743-2743
Open Access | Times Cited: 51
Coupling SWAT and Bi-LSTM for improving daily-scale hydro-climatic simulation and climate change impact assessment in a tropical river basin
Shuai Yang, Mou Leong Tan, Qixuan Song, et al.
Journal of Environmental Management (2023) Vol. 330, pp. 117244-117244
Closed Access | Times Cited: 45
Groundwater level forecasting with machine learning models: A review
Kenneth Beng Wee Boo, Ahmed El‐Shafie, Faridah Othman, et al.
Water Research (2024) Vol. 252, pp. 121249-121249
Closed Access | Times Cited: 26
Time series-based groundwater level forecasting using gated recurrent unit deep neural networks
Haiping Lin, Amin Gharehbaghi, Qian Zhang, et al.
Engineering Applications of Computational Fluid Mechanics (2022) Vol. 16, Iss. 1, pp. 1655-1672
Open Access | Times Cited: 51
Self-attention (SA) temporal convolutional network (SATCN)-long short-term memory neural network (SATCN-LSTM): an advanced python code for predicting groundwater level
Mohammad Ehteram, Elham Ghanbari-Adivi
Environmental Science and Pollution Research (2023) Vol. 30, Iss. 40, pp. 92903-92921
Closed Access | Times Cited: 23
Novel hybrid intelligence predictive model based on successive variational mode decomposition algorithm for monthly runoff series
Abbas Parsaie, Redvan Ghasemlounıa, Amin Gharehbaghi, et al.
Journal of Hydrology (2024) Vol. 634, pp. 131041-131041
Closed Access | Times Cited: 14
Groundwater level prediction with meteorologically sensitive Gated Recurrent Unit (GRU) neural networks
Amin Gharehbaghi, Redvan Ghasemlounıa, Farshad Ahmadi, et al.
Journal of Hydrology (2022) Vol. 612, pp. 128262-128262
Closed Access | Times Cited: 37
CNN-Bi LSTM Neural Network for Simulating Groundwater Level
Ahmed Ali, Saman Ebrahimi, Muhammad Masood Ashiq, et al.
Computational Research Progress in Applied Science and Engineering (2022) Vol. 8, Iss. 1, pp. 1-7
Open Access | Times Cited: 31
Prediction of crop yield in India using machine learning and hybrid deep learning models
Krithikha Sanju Saravanan, Velammal Bhagavathiappan
Acta Geophysica (2024) Vol. 72, Iss. 6, pp. 4613-4632
Closed Access | Times Cited: 7
Predicting Effects of Non-Point Source Pollution Emission Control Schemes Based on VMD-BiLSTM and MIKE21
Xianqi Zhang, Yu Qi, Fang Liu, et al.
Environmental Modeling & Assessment (2024)
Open Access | Times Cited: 5
Shallow vs. Deep Learning Models for Groundwater Level Prediction: A Multi-Piezometer Data Integration Approach
Ali Yeganeh, Farshad Ahmadi, Yong Jie Wong, et al.
Water Air & Soil Pollution (2024) Vol. 235, Iss. 7
Closed Access | Times Cited: 5
Long-run forecasting surface and groundwater dynamics from intermittent observation data: An evaluation for 50 years
M.T. Vu, Abderrahim Jardani, Nicolas Masseï, et al.
The Science of The Total Environment (2023) Vol. 880, pp. 163338-163338
Open Access | Times Cited: 15
Statistical and deep learning models for reference evapotranspiration time series forecasting: A comparison of accuracy, complexity, and data efficiency
Arman Ahmadi, André Daccache, Mojtaba Sadegh, et al.
Computers and Electronics in Agriculture (2023) Vol. 215, pp. 108424-108424
Open Access | Times Cited: 13
Carbon emission reduction potential analysis of fuel cell vehicles in China: Based on GRA-BiLSTM prediction model
Bingchun Liu, Shize Zheng, Mingzhao Lai
International Journal of Hydrogen Energy (2024) Vol. 66, pp. 110-121
Closed Access | Times Cited: 4
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
Forecasting groundwater table for the sustenance and conservation of water-dependent ecosystems in protected areas: the case of the Wielkopolski National Park in Poland
Renata Graf, Lech Kaczmarek, Mariusz Pełechaty, et al.
Stochastic Environmental Research and Risk Assessment (2025)
Closed Access
Daily Groundwater Level Prediction and Uncertainty Using LSTM Coupled with PMI and Bootstrap Incorporating Teleconnection Patterns Information
Haibo Chu, Jianmin Bian, Qi Lang, et al.
Sustainability (2022) Vol. 14, Iss. 18, pp. 11598-11598
Open Access | Times Cited: 21
Prediction of Glacially Derived Runoff in the Muzati River Watershed Based on the PSO-LSTM Model
Xiazi Yang, Balati Maihemuti, Zibibula Simayi, et al.
Water (2022) Vol. 14, Iss. 13, pp. 2018-2018
Open Access | Times Cited: 19
A novel IBAS-ELM model for prediction of water levels in front of pumping stations
Peiru Yan, Zhao Zhang, Qingzhi Hou, et al.
Journal of Hydrology (2022) Vol. 616, pp. 128810-128810
Closed Access | Times Cited: 19
Anomaly detection in groundwater monitoring data using LSTM-Autoencoder neural networks
Fatemeh Rezaiezadeh Roukerd, Mohammad Mahdi Rajabi
Environmental Monitoring and Assessment (2024) Vol. 196, Iss. 8
Closed Access | Times Cited: 3
Impacts of LULC changes on runoff from rivers through a coupled SWAT and BiLSTM model: A case study in Zhanghe River Basin, China
Jiawen Liu, Xianqi Zhang, Xiaoyan Wu, et al.
Ecological Informatics (2024), pp. 102866-102866
Open Access | Times Cited: 3
Drilling Rate of Penetration Prediction Based on CBT-LSTM Neural Network
Kai Bai, Siyi Jin, Zeqing Zhang, et al.
Sensors (2024) Vol. 24, Iss. 21, pp. 6966-6966
Open Access | Times Cited: 3
Band-Optimized Bidirectional LSTM Deep Learning Model for Bathymetry Inversion
Xiaotao Xi, Ming Chen, Yingxi Wang, et al.
Remote Sensing (2023) Vol. 15, Iss. 14, pp. 3472-3472
Open Access | Times Cited: 8
Short-Term Prediction of Groundwater Level Based on Spatiotemporal Correlation
Ming Liu, Xiao Kang Chen, Guang Hui Wang, et al.
Water Resources (2024) Vol. 51, Iss. 3, pp. 207-220
Closed Access | Times Cited: 2
A groundwater level spatiotemporal prediction model based on graph convolutional networks with a long short-term memory
Lifang Wang, Zhengwen Jiang, Lei Song, et al.
Journal of Hydroinformatics (2024) Vol. 26, Iss. 11, pp. 2962-2979
Closed Access | Times Cited: 2
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