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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:

Deep learning, explained: Fundamentals, explainability, and bridgeability to process-based modelling
Saman Razavi
Environmental Modelling & Software (2021) Vol. 144, pp. 105159-105159
Open Access | Times Cited: 114

Showing 1-25 of 114 citing articles:

A review of spatially-explicit GeoAI applications in Urban Geography
Pengyuan Liu, Filip Biljecki
International Journal of Applied Earth Observation and Geoinformation (2022) Vol. 112, pp. 102936-102936
Open Access | Times Cited: 95
Smart city re-imagined: City planning and GeoAI in the age of big data
Reza Mortaheb, Piotr Jankowski
Journal of Urban Management (2022) Vol. 12, Iss. 1, pp. 4-15
Open Access | Times Cited: 73
Coevolution of machine learning and process‐based modelling to revolutionize Earth and environmental sciences: A perspective
Saman Razavi, David M. Hannah, Amin Elshorbagy, et al.
Hydrological Processes (2022) Vol. 36, Iss. 6
Open Access | Times Cited: 69
Recent Advances and New Frontiers in Riverine and Coastal Flood Modeling
Keighobad Jafarzadegan, Hamid Moradkhani, Florian Pappenberger, et al.
Reviews of Geophysics (2023) Vol. 61, Iss. 2
Open Access | Times Cited: 64
Iterative integration of deep learning in hybrid Earth surface system modelling
Min Chen, Zhen Qian, Niklas Boers, et al.
Nature Reviews Earth & Environment (2023) Vol. 4, Iss. 8, pp. 568-581
Closed Access | Times Cited: 53
Exploding the myths: An introduction to artificial neural networks for prediction and forecasting
Holger R. Maier, Stefano Galelli, Saman Razavi, et al.
Environmental Modelling & Software (2023) Vol. 167, pp. 105776-105776
Open Access | Times Cited: 50
A Critical Review of RNN and LSTM Variants in Hydrological Time Series Predictions
Muhammad Waqas, Usa Wannasingha Humphries
MethodsX (2024) Vol. 13, pp. 102946-102946
Open Access | Times Cited: 17
Sensitivity analysis: A discipline coming of age
Andrea Saltelli, Anthony J. Jakeman, Saman Razavi, et al.
Environmental Modelling & Software (2021) Vol. 146, pp. 105226-105226
Open Access | Times Cited: 58
Hybrid modelling of water resource recovery facilities: status and opportunities
Mariane Yvonne Schneider, Ward Quaghebeur, Sina Borzooei, et al.
Water Science & Technology (2022)
Open Access | Times Cited: 45
Machine learning based downscaling of GRACE-estimated groundwater in Central Valley, California
Vibhor Agarwal, Orhan Akyılmaz, C. K. Shum, et al.
The Science of The Total Environment (2022) Vol. 865, pp. 161138-161138
Open Access | Times Cited: 42
Developing a Physics‐Informed Deep Learning Model to Simulate Runoff Response to Climate Change in Alpine Catchments
L. Zhong, Huimin Lei, Bing Gao
Water Resources Research (2023) Vol. 59, Iss. 6
Closed Access | Times Cited: 35
Data-driven surrogate modeling: Introducing spatial lag to consider spatial autocorrelation of flooding within urban drainage systems
Heng Li, Chunxiao Zhang, Min Chen, et al.
Environmental Modelling & Software (2023) Vol. 161, pp. 105623-105623
Open Access | Times Cited: 28
Event-based decision support algorithm for real-time flood forecasting in urban drainage systems using machine learning modelling
Farzad Piadeh, Kourosh Behzadian, Albert Chen, et al.
Environmental Modelling & Software (2023) Vol. 167, pp. 105772-105772
Open Access | Times Cited: 25
Enhancing streamflow estimation by integrating a data-driven evapotranspiration submodel into process-based hydrological models
Lian Xie, Xiaolong Hu, Jiang Bian, et al.
Journal of Hydrology (2023) Vol. 621, pp. 129603-129603
Closed Access | Times Cited: 22
Using Machine Learning to Identify Hydrologic Signatures With an Encoder–Decoder Framework
Tom Botterill, Hilary McMillan
Water Resources Research (2023) Vol. 59, Iss. 3
Open Access | Times Cited: 21
On the need for physical constraints in deep learning rainfall–runoff projections under climate change: a sensitivity analysis to warming and shifts in potential evapotranspiration
Sungwook Wi, Scott Steinschneider
Hydrology and earth system sciences (2024) Vol. 28, Iss. 3, pp. 479-503
Open Access | Times Cited: 13
An automatic prediction of students’ performance to support the university education system: a deep learning approach
Yazn Alshamaila, Hamad Alsawalqah, Ibrahim Aljarah, et al.
Multimedia Tools and Applications (2024)
Closed Access | Times Cited: 10
Recent advances in the electrochemical production of hydrogen peroxide
Nishu Dhanda, Yogesh Kumar Panday, Sudesh Kumar
Electrochimica Acta (2024), pp. 143872-143872
Closed Access | Times Cited: 9
Deep learning-based Raman spectroscopy qualitative analysis algorithm: A convolutional neural network and transformer approach
Zilong Wang, Yunfeng Li, Jinglei Zhai, et al.
Talanta (2024) Vol. 275, pp. 126138-126138
Closed Access | Times Cited: 8
ATLANTIS: A benchmark for semantic segmentation of waterbody images
Seyed Mohammad Hassan Erfani, Zhenyao Wu, Xinyi Wu, et al.
Environmental Modelling & Software (2022) Vol. 149, pp. 105333-105333
Open Access | Times Cited: 36
Adaptive precipitation nowcasting using deep learning and ensemble modeling
Amirmasoud Amini, Mehri Dolatshahi, Reza Kerachian
Journal of Hydrology (2022) Vol. 612, pp. 128197-128197
Closed Access | Times Cited: 35
Exploration of machine learning algorithms for predicting the changes in abundance of antibiotic resistance genes in anaerobic digestion
Nervana Haffiez, Tae Hyun Chung, Basem S. Zakaria, et al.
The Science of The Total Environment (2022) Vol. 839, pp. 156211-156211
Closed Access | Times Cited: 34
An Outlook for Deep Learning in Ecosystem Science
George L. W. Perry, Rupert Seidl, André M. Bellvé, et al.
Ecosystems (2022) Vol. 25, Iss. 8, pp. 1700-1718
Open Access | Times Cited: 32
Geospatial Artificial Intelligence (GeoAI) in the Integrated Hydrological and Fluvial Systems Modeling: Review of Current Applications and Trends
Carlos Gonzales‐Inca, Mikel Calle, Danny Croghan, et al.
Water (2022) Vol. 14, Iss. 14, pp. 2211-2211
Open Access | Times Cited: 27
On how data are partitioned in model development and evaluation: Confronting the elephant in the room to enhance model generalization
Holger R. Maier, Feifei Zheng, Hoshin V. Gupta, et al.
Environmental Modelling & Software (2023) Vol. 167, pp. 105779-105779
Open Access | Times Cited: 19
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