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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:

Using Optimized Deep Learning to Predict Daily Streamflow: A Comparison to Common Machine Learning Algorithms
Khabat Khosravi, Ali Golkarian, John P. Tiefenbacher
Water Resources Management (2022) Vol. 36, Iss. 2, pp. 699-716
Closed Access | Times Cited: 61

Showing 51-75 of 61 citing articles:

Monthly streamflow forecasting based on meteorological data from a nearby station
Ebrahim Nohani, Ahmadreza Karimipour, Solmaz Khazaei, et al.
Water Practice & Technology (2024) Vol. 19, Iss. 5, pp. 1659-1675
Open Access
Comparison and Integration of Hydrological Models and Machine Learning Models in Global Monthly Streamflow Simulation
Jiawen Zhang, Dongdong Kong, Jianfeng Li, et al.
(2024)
Closed 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
Runoff Prediction for Hydrological Applications Using an INFO-Optimized Deep Learning Model
Weisheng Wang, Yongkang Hao, Xiaozhen Zheng, et al.
Processes (2024) Vol. 12, Iss. 8, pp. 1776-1776
Open Access
Enhanced Forecasting Accuracy: Leveraging GRU+BiLSTM with Attention for Precise Floodwater Level Prediction
Sudhir Kumar, Vaneet Kour, Anil Verma, et al.
(2024) Vol. 10, pp. 108-115
Closed Access
Hybrid Daily Streamflow Forecasting Based on Variational Mode Decomposition Random Vector Functional Link Network-Based Ensemble Forecasting
Salim Heddam
Springer climate (2023), pp. 225-244
Closed Access | Times Cited: 1
Simulation of the projected river flow changes using integrated downscaling and Bayesian optimization-tuned kernel-based models
Kiyoumars Roushangar, Sadegh Abdelzad, Saman Shahnazi
International Journal of Environmental Science and Technology (2023)
Closed Access | Times Cited: 1
Comparison of Different Machine Learning Models in Breast Cancer
Shuai Wu, Wenjia Xiong
Highlights in Science Engineering and Technology (2022) Vol. 8, pp. 624-629
Open Access | Times Cited: 2
Leveraging Contextual Cues from a Conceptual Model with Predictive Skills of Machine Learning for Improved Predictability and Interpretability in the Hydrological Processes
Pravin Bhasme, Udit Bhatia
Authorea (Authorea) (2023)
Open Access
THE STOCHASTIC HYDROLOGICAL FORECAST MODELS FOR RAINFALL PREDICTIONS TO AID CLIMATE CHANGE RESILIENCE WITHIN THE TANA ALLUVIAL AQUIFER ZONES USING THE ARIMA BOX-JENKINS ALGORITHMS

International Research Journal of Modernization in Engineering Technology and Science (2023)
Open Access
Optimizing Water Resources With IoT and ML
Rakhi Chauhan, Neera Batra, Sonali Goyal, et al.
Advances in civil and industrial engineering book series (2023), pp. 94-109
Closed Access
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