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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.

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Showing 1-25 of 45 citing articles:

Extreme gradient boosting (Xgboost) model to predict the groundwater levels in Selangor Malaysia
Ahmedbahaaaldin Ibrahem Ahmed Osman, Ali Najah Ahmed, Chow Ming Fai, et al.
Ain Shams Engineering Journal (2021) Vol. 12, Iss. 2, pp. 1545-1556
Open Access | Times Cited: 382
A novel hybrid BPNN model based on adaptive evolutionary Artificial Bee Colony Algorithm for water quality index prediction
Lingxuan Chen, Tunhua Wu, Zhaocai Wang, et al.
Ecological Indicators (2023) Vol. 146, pp. 109882-109882
Open Access | Times Cited: 117
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: 31
Modeling the fluctuations of groundwater level by employing ensemble deep learning techniques
Haitham Abdulmohsin Afan, Ahmedbahaaaldin Ibrahem Ahmed Osman, Yusuf Essam, et al.
Engineering Applications of Computational Fluid Mechanics (2021) Vol. 15, Iss. 1, pp. 1420-1439
Open Access | Times Cited: 104
Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India
Kusum Pandey, Shiv Kumar, Anurag Malik, et al.
Sustainability (2020) Vol. 12, Iss. 21, pp. 8932-8932
Open Access | Times Cited: 84
Real-time water level prediction of cascaded channels based on multilayer perception and recurrent neural network
Tao Ren, Xuefeng Liu, Jianwei Niu, et al.
Journal of Hydrology (2020) Vol. 585, pp. 124783-124783
Closed Access | Times Cited: 83
Past, Present and Perspective Methodology for Groundwater Modeling-Based Machine Learning Approaches
Ahmedbahaaaldin Ibrahem Ahmed Osman, Ali Najah Ahmed, Yuk Feng Huang, et al.
Archives of Computational Methods in Engineering (2022) Vol. 29, Iss. 6, pp. 3843-3859
Closed Access | Times Cited: 55
Prediction of the corrosion depth of oil well cement corroded by carbon dioxide using GA-BP neural network
Rongyao Chen, Jianjian Song, Mingbiao Xu, et al.
Construction and Building Materials (2023) Vol. 394, pp. 132127-132127
Closed Access | Times Cited: 33
Groundwater quality evaluation using hybrid model of the multi-layer perceptron combined with neural-evolutionary regression techniques: case study of Shiraz plain
Hossein Moayedi, Marjan Salari, Atefeh Ahmadi Dehrashid, et al.
Stochastic Environmental Research and Risk Assessment (2023) Vol. 37, Iss. 8, pp. 2961-2976
Closed Access | Times Cited: 30
Seasonal and Inter-Annual Variability of Groundwater and Their Responses to Climate Change and Human Activities in Arid and Desert Areas: A Case Study in Yaoba Oasis, Northwest China
Huanhuan Li, Yudong Lu, Ce Zheng, et al.
Water (2020) Vol. 12, Iss. 1, pp. 303-303
Open Access | Times Cited: 42
Strategic assessment of groundwater potential zones: a hybrid geospatial approach
Hamid Nazaripour, Mahdi Sedaghat, Vahid Shafaie, et al.
Applied Water Science (2024) Vol. 14, Iss. 8
Open Access | Times Cited: 5
Enhancing Groundwater Recharge Prediction: A Feature Selection‐Based Deep Forest Model With Bayesian Optimisation
Bao Liu, Yaohua Sun, Lei Gao
Hydrological Processes (2024) Vol. 38, Iss. 10
Open Access | Times Cited: 5
Prediction of groundwater level in basement complex terrain using artificial neural network: a case of Ijebu-Jesa, southwestern Nigeria
K.A.N. Adiat, O. F. Ajayi, Ayokunle Adewale Akinlalu, et al.
Applied Water Science (2019) Vol. 10, Iss. 1
Open Access | Times Cited: 38
Soil salinity prediction using hybrid machine learning and remote sensing in Ben Tre province on Vietnam’s Mekong River Delta
Huu Duy Nguyen, Chien Pham Van, Tien Giang Nguyen, et al.
Environmental Science and Pollution Research (2023) Vol. 30, Iss. 29, pp. 74340-74357
Closed Access | Times Cited: 12
An Efficient Hybrid Mine Blast Algorithm for Tackling Software Fault Prediction Problem
Mohammed Alweshah, Sofian Kassaymeh, Saleh Alkhalaileh, et al.
Neural Processing Letters (2023) Vol. 55, Iss. 8, pp. 10925-10950
Closed Access | Times Cited: 11
Characteristics of Groundwater Level Dynamics in Xi’an (2010–2020) and Groundwater Level Prediction for 2030 Using Regression Analysis and BP Neural Network: Implication to Groundwater Resources Management
Junyan Yang, Jianhua Wu, Yan Tian, et al.
Springer hydrogeology (2025), pp. 193-214
Closed Access
Predictive modeling of combustion cycle variations in spark ignition engine based on backpropagation neural network and artificial bee colony algorithm
Mingzhang Pan, Yue Pan, Changcheng Fu, et al.
Engineering Applications of Artificial Intelligence (2025) Vol. 152, pp. 110813-110813
Closed Access
Bee-inspired insights: Unleashing the potential of artificial bee colony optimized hybrid neural networks for enhanced groundwater level time series prediction
Okan Mert Katipoğlu, Babak Mohammadi, Mehdi Keblouti
Environmental Monitoring and Assessment (2024) Vol. 196, Iss. 8
Closed Access | Times Cited: 3
Prediction of residual stress, surface roughness, and grain refinement of 42CrMo steel subjected to shot peening by combining finite element method and artificial neural network
Haiquan Huang, Senhui Wang, Cheng Wang, et al.
The International Journal of Advanced Manufacturing Technology (2023) Vol. 127, Iss. 7-8, pp. 3441-3461
Closed Access | Times Cited: 8
Weighted error-output recurrent echo kernel state network for multi-step water level prediction
Zongying Liu, Xiaohan Xu, Mingyang Pan, et al.
Applied Soft Computing (2023) Vol. 137, pp. 110131-110131
Closed Access | Times Cited: 7
Research on the Prediction of the Water Demand of Construction Engineering Based on the BP Neural Network
Hao Peng, Han Wu, Junwu Wang
Advances in Civil Engineering (2020) Vol. 2020, Iss. 1
Open Access | Times Cited: 17
Dimension Reduction of Machine Learning-Based Forecasting Models Employing Principal Component Analysis
Yinghui Meng, Sultan Noman Qasem, Manouchehr Shokri, et al.
Mathematics (2020) Vol. 8, Iss. 8, pp. 1233-1233
Open Access | Times Cited: 15
A deep neural multi-model ensemble (DNM2E) framework for modelling groundwater levels over Kerala using dynamic variables
A. Keerthana, Archana Nair
Stochastic Environmental Research and Risk Assessment (2023)
Closed Access | Times Cited: 5
A Novel Hybrid Algorithm for the Forward Kinematics Problem of 6 DOF Based on Neural Networks
Huizhi Zhu, Wenxia Xu, Baocheng Yu, et al.
Sensors (2022) Vol. 22, Iss. 14, pp. 5318-5318
Open Access | Times Cited: 8
Hybrid metaheuristic machine learning approach for water level prediction: A case study in Dongting Lake
Bin Deng, Pan Liu, Ren Jie Chin, et al.
Frontiers in Earth Science (2022) Vol. 10
Open Access | Times Cited: 8
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