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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:

Multiple Random Forests Modelling for Urban Water Consumption Forecasting
Guoqiang Chen, Tianyu Long, Jiangong Xiong, et al.
Water Resources Management (2017) Vol. 31, Iss. 15, pp. 4715-4729
Closed Access | Times Cited: 84

Showing 1-25 of 84 citing articles:

A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources
Hristos Tyralis, Georgia Papacharalampous, Andreas Langousis
Water (2019) Vol. 11, Iss. 5, pp. 910-910
Open Access | Times Cited: 561
Hybridised Artificial Neural Network Model with Slime Mould Algorithm: A Novel Methodology for Prediction of Urban Stochastic Water Demand
Salah L. Zubaidi, Iqbal H. Abdulkareem, Khalid Hashim, et al.
Water (2020) Vol. 12, Iss. 10, pp. 2692-2692
Open Access | Times Cited: 150
A Novel Methodology for Prediction Urban Water Demand by Wavelet Denoising and Adaptive Neuro-Fuzzy Inference System Approach
Salah L. Zubaidi, Hussein Al-Bugharbee, Sandra Ortega‐Martorell, et al.
Water (2020) Vol. 12, Iss. 6, pp. 1628-1628
Open Access | Times Cited: 116
Improving the performance of the mass transfer-based reference evapotranspiration estimation approaches through a coupled wavelet-random forest methodology
Jalal Shiri
Journal of Hydrology (2018) Vol. 561, pp. 737-750
Closed Access | Times Cited: 103
Deep learning with long short-term memory neural networks combining wavelet transform and principal component analysis for daily urban water demand forecasting
Baigang Du, Qiliang Zhou, Jun Guo, et al.
Expert Systems with Applications (2021) Vol. 171, pp. 114571-114571
Closed Access | Times Cited: 93
Hourly and Daily Urban Water Demand Predictions Using a Long Short-Term Memory Based Model
Mu Li, Feifei Zheng, Ruoling Tao, et al.
Journal of Water Resources Planning and Management (2020) Vol. 146, Iss. 9
Open Access | Times Cited: 86
Modelling of soil permeability using different data driven algorithms based on physical properties of soil
Vijay Kumar Singh, Devendra Kumar, P. S. Kashyap, et al.
Journal of Hydrology (2019) Vol. 580, pp. 124223-124223
Closed Access | Times Cited: 76
Modeling and predicting the electricity production in hydropower using conjunction of wavelet transform, long short-term memory and random forest models
Mehdi Zolfaghari, Mohammad Reza Golabi
Renewable Energy (2021) Vol. 170, pp. 1367-1381
Closed Access | Times Cited: 75
Applying human mobility and water consumption data for short-term water demand forecasting using classical and machine learning models
Kamil Smolak, Barbara Kasieczka, W. Fialkiewicz, et al.
Urban Water Journal (2020) Vol. 17, Iss. 1, pp. 32-42
Open Access | Times Cited: 70
A hybrid Wavelet-CNN-LSTM deep learning model for short-term urban water demand forecasting
Zhengheng Pu, Jieru Yan, Lei Chen, et al.
Frontiers of Environmental Science & Engineering (2022) Vol. 17, Iss. 2
Closed Access | Times Cited: 47
Short-term water demand forecasting using data-centric machine learning approaches
Guoxuan Liu, Dragan Savić, Guangtao Fu
Journal of Hydroinformatics (2023) Vol. 25, Iss. 3, pp. 895-911
Open Access | Times Cited: 37
An intelligent decision support system for groundwater supply management and electromechanical infrastructure controls
Parisa Ataei, Amir Takhtravan, Mohammad Gheibi, et al.
Heliyon (2024) Vol. 10, Iss. 3, pp. e25036-e25036
Open Access | Times Cited: 9
Predicting water demand: a review of the methods employed and future possibilities
Gustavo de Souza Groppo, Marcelo Azevedo Costa, Marcelo Libânio
Water Science & Technology Water Supply (2019) Vol. 19, Iss. 8, pp. 2179-2198
Closed Access | Times Cited: 73
Decision Tree-Based Data Mining and Rule Induction for Identifying High Quality Groundwater Zones to Water Supply Management: a Novel Hybrid Use of Data Mining and GIS
Mehrdad Jeihouni, Ara Toomanian, Ali Mansourian
Water Resources Management (2019) Vol. 34, Iss. 1, pp. 139-154
Open Access | Times Cited: 56
A Comparison of Short-Term Water Demand Forecasting Models
E. Pacchin, Francesca Gagliardi, Stefano Alvisi, et al.
Water Resources Management (2019) Vol. 33, Iss. 4, pp. 1481-1497
Open Access | Times Cited: 55
An ensemble stacked model with bias correction for improved water demand forecasting
Maria Xenochristou, Zoran Kapelan
Urban Water Journal (2020) Vol. 17, Iss. 3, pp. 212-223
Open Access | Times Cited: 54
Hybrid Models for Water Demand Forecasting
Prerna Pandey, Neeraj Dhanraj Bokde, Shilpa Dongre, et al.
Journal of Water Resources Planning and Management (2020) Vol. 147, Iss. 2
Closed Access | Times Cited: 52
Probabilistic urban water demand forecasting using wavelet-based machine learning models
Mostafa Rezaali, John Quilty, Abdolreza Karimi
Journal of Hydrology (2021) Vol. 600, pp. 126358-126358
Closed Access | Times Cited: 43
Multi-ahead electrical conductivity forecasting of surface water based on machine learning algorithms
Deepak Kumar, Vijay Singh, Salwan Ali Abed, et al.
Applied Water Science (2023) Vol. 13, Iss. 10
Open Access | Times Cited: 17
Artificial Intelligence for Water Consumption Assessment: State of the Art Review
Almando Morain, Nivedita Ilangovan, Christopher Delhom, et al.
Water Resources Management (2024) Vol. 38, Iss. 9, pp. 3113-3134
Open Access | Times Cited: 5
Machine learning for energy-water nexus: challenges and opportunities
Zaidi, Chandola, Allen, et al.
Big Earth Data (2018) Vol. 2, Iss. 3, pp. 228-267
Open Access | Times Cited: 47
A stochastic wavelet-based data-driven framework for forecasting uncertain multiscale hydrological and water resources processes
John Quilty, Jan Adamowski
Environmental Modelling & Software (2020) Vol. 130, pp. 104718-104718
Closed Access | Times Cited: 46
Comparison of EEMD-ARIMA, EEMD-BP and EEMD-SVM algorithms for predicting the hourly urban water consumption
Xingpo Liu, Yiqing Zhang, Qichen Zhang
Journal of Hydroinformatics (2022) Vol. 24, Iss. 3, pp. 535-558
Open Access | Times Cited: 27
Advanced Techniques for Monitoring and Management of Urban Water Infrastructures—An Overview
Anca Hângan, Costin-Gabriel Chiru, Diana Arsene, et al.
Water (2022) Vol. 14, Iss. 14, pp. 2174-2174
Open Access | Times Cited: 23
Robust adaptive optimization for sustainable water demand prediction in water distribution systems
Ke Wang, Jiayang Meng, Zhangquan Wang, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
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