Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

A novel ensemble-based conceptual-data-driven approach for improved streamflow simulations
Anna E. Sikorska‐Senoner, John Quilty
Environmental Modelling & Software (2021) Vol. 143, pp. 105094-105094
Open Access | Times Cited: 63

Showing 1-25 of 63 citing articles:

IHACRES, GR4J and MISD-based multi conceptual-machine learning approach for rainfall-runoff modeling
Babak Mohammadi, Mir Jafar Sadegh Safari, Saeed Vazifehkhah
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 65
Forecasting weekly reference evapotranspiration using Auto Encoder Decoder Bidirectional LSTM model hybridized with a Boruta-CatBoost input optimizer
Masoud Karbasi, Mehdi Jamei, Mumtaz Ali, et al.
Computers and Electronics in Agriculture (2022) Vol. 198, pp. 107121-107121
Closed Access | Times Cited: 62
Application of a modern multi-level ensemble approach for the estimation of critical shear stress in cohesive sediment mixture
U. K. Singh, Mehdi Jamei, Masoud Karbasi, et al.
Journal of Hydrology (2022) Vol. 607, pp. 127549-127549
Closed Access | Times Cited: 52
A quantile-based encoder-decoder framework for multi-step ahead runoff forecasting
Mohammad Sina Jahangir, John You, John Quilty
Journal of Hydrology (2023) Vol. 619, pp. 129269-129269
Closed Access | Times Cited: 36
A Comprehensive Experimental and Computational Investigation on Estimation of Scour Depth at Bridge Abutment: Emerging Ensemble Intelligent Systems
Manish Pandey, Masoud Karbasi, Mehdi Jamei, et al.
Water Resources Management (2023) Vol. 37, Iss. 9, pp. 3745-3767
Closed Access | Times Cited: 26
Enhancing Streamflow Prediction Physically Consistently Using Process-Based Modeling and Domain Knowledge: A Review
Bisrat Ayalew Yifru, Kyoung Jae Lim, Seoro Lee
Sustainability (2024) Vol. 16, Iss. 4, pp. 1376-1376
Open Access | Times Cited: 9
Optimizing building energy performance predictions: A comparative study of artificial intelligence models
Omer A. Alawi, Haslinda Mohamed Kamar, Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬
Journal of Building Engineering (2024) Vol. 88, pp. 109247-109247
Closed Access | Times Cited: 8
AI‐Based Ensemble Flood Forecasts and Its Implementation in Multi‐Objective Robust Optimization Operation for Reservoir Flood Control
Yuxue Guo, Yue‐Ping Xu, Xinting Yu, et al.
Water Resources Research (2024) Vol. 60, Iss. 5
Open Access | Times Cited: 8
Data driven insights for parabolic trough solar collectors: Artificial intelligence-based energy and exergy performance analysis
Tao Hai, Omer A. Alawi, Raad Z. Homod, et al.
Journal of Cleaner Production (2024) Vol. 443, pp. 141069-141069
Closed Access | Times Cited: 7
A stochastic conceptual-data-driven approach for improved hydrological simulations
John Quilty, Anna E. Sikorska‐Senoner, David Hah
Environmental Modelling & Software (2022) Vol. 149, pp. 105326-105326
Open Access | Times Cited: 35
Coupling Machine Learning Into Hydrodynamic Models to Improve River Modeling With Complex Boundary Conditions
Huang Sheng, Jun Xia, Yueling Wang, et al.
Water Resources Research (2022) Vol. 58, Iss. 10
Closed Access | Times Cited: 32
A systematic and critical review on development of machine learning based-ensemble models for prediction of adsorption process efficiency
Elahe Abbasi, Mohammad Reza Alavi Moghaddam, Elaheh Kowsari
Journal of Cleaner Production (2022) Vol. 379, pp. 134588-134588
Closed Access | Times Cited: 31
Streamflow prediction in mountainous region using new machine learning and data preprocessing methods: a case study
Rana Muhammad Adnan Ikram, Barenya Bikash Hazarika, Deepak Gupta, et al.
Neural Computing and Applications (2022)
Closed Access | Times Cited: 31
Air quality monitoring based on chemical and meteorological drivers: Application of a novel data filtering-based hybridized deep learning model
Mehdi Jamei, Mumtaz Ali, Anurag Malik, et al.
Journal of Cleaner Production (2022) Vol. 374, pp. 134011-134011
Closed Access | Times Cited: 30
Identifying the possible driving mechanisms in Precipitation-Runoff relationships with nonstationary and nonlinear theory approaches
Tongfang Li, Tian Lan, Hongbo Zhang, et al.
Journal of Hydrology (2024) Vol. 639, pp. 131535-131535
Closed Access | Times Cited: 6
Machine learning optimization of operating parameters to achieve high power density and efficiency of polymer electrolyte membrane fuel cell
Rashed Kaiser, Jong-Chun Park
Chemical Engineering Science (2024) Vol. 297, pp. 120309-120309
Closed Access | Times Cited: 5
A hybrid model coupling process-driven and data-driven models for improved real-time flood forecasting
Chengjing Xu, Ping‐an Zhong, Feilin Zhu, et al.
Journal of Hydrology (2024) Vol. 638, pp. 131494-131494
Closed Access | Times Cited: 5
Harnessing Novel Data‐Driven Techniques for Precise Rainfall–Runoff Modeling
Saad Sh. Sammen, Reza Mohammadpour, Karam Alsafadi, et al.
Journal of Flood Risk Management (2025) Vol. 18, Iss. 1
Open Access
A review of machine learning concepts and methods for addressing challenges in probabilistic hydrological post-processing and forecasting
Georgia Papacharalampous, Hristos Tyralis
Frontiers in Water (2022) Vol. 4
Open Access | Times Cited: 26
Designing a decomposition-based multi-phase pre-processing strategy coupled with EDBi-LSTM deep learning approach for sediment load forecasting
Mehdi Jamei, Mumtaz Ali, Anurag Malik, et al.
Ecological Indicators (2023) Vol. 153, pp. 110478-110478
Open Access | Times Cited: 12
Coupled SWAT, Stationary Wavelet Transform, and Interpretable Machine Learning to Improve Watershed Streamflow Simulation
Chengqing Ren, Jianxia Chang, Xuebin Wang, et al.
Water Resources Management (2025)
Closed Access
Exploring the potential processes controlling changes in precipitation–runoff relationships in non-stationary environments
Tian Lan, Tongfang Li, Hongbo Zhang, et al.
Hydrology and earth system sciences (2025) Vol. 29, Iss. 4, pp. 903-924
Open Access
Quantile-Based Hydrological Modelling
Hristos Tyralis, Georgia Papacharalampous
Water (2021) Vol. 13, Iss. 23, pp. 3420-3420
Open Access | Times Cited: 28
Hydrological post-processing for predicting extreme quantiles
Hristos Tyralis, Georgia Papacharalampous
Journal of Hydrology (2023) Vol. 617, pp. 129082-129082
Open Access | Times Cited: 9
Pollution loads in the Middle-Lower Yangtze River by coupling water quality models with machine learning
Huang Sheng, Jun Xia, Yueling Wang, et al.
Water Research (2024) Vol. 263, pp. 122191-122191
Closed Access | Times Cited: 3
Page 1 - Next Page

Scroll to top