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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:

Machine learning for hydrologic sciences: An introductory overview
Tianfang Xu, Feng Liang
Wiley Interdisciplinary Reviews Water (2021) Vol. 8, Iss. 5
Closed Access | Times Cited: 152

Showing 26-50 of 152 citing articles:

Capabilities of deep learning models on learning physical relationships: Case of rainfall-runoff modeling with LSTM
Kazuki Yokoo, Kei Ishida, Ali Ercan, et al.
The Science of The Total Environment (2021) Vol. 802, pp. 149876-149876
Open Access | Times Cited: 45
Gaussian process machine learning and Kriging for groundwater salinity interpolation
Tao Cui, Dan Pagendam, Mat Gilfedder
Environmental Modelling & Software (2021) Vol. 144, pp. 105170-105170
Open Access | Times Cited: 44
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: 37
Multi-objective optimization of urban environmental system design using machine learning
Peiyuan Li, Tianfang Xu, Shiqi Wei, et al.
Computers Environment and Urban Systems (2022) Vol. 94, pp. 101796-101796
Open Access | Times Cited: 37
A catchment-scale model of river water quality by Machine Learning
Maria Grazia Zanoni, Bruno Majone, Alberto Bellin
The Science of The Total Environment (2022) Vol. 838, pp. 156377-156377
Open Access | Times Cited: 36
Hybrid Physically Based and Deep Learning Modeling of a Snow Dominated, Mountainous, Karst Watershed
Tianfang Xu, Qianqiu Longyang, Conor Tyson, et al.
Water Resources Research (2022) Vol. 58, Iss. 3
Open Access | Times Cited: 35
Random forest-based modeling of stream nutrients at national level in a data-scarce region
Holger Virro, Alexander Kmoch, Marko Vainu, et al.
The Science of The Total Environment (2022) Vol. 840, pp. 156613-156613
Open Access | Times Cited: 33
Improving Results of Existing Groundwater Numerical Models Using Machine Learning Techniques: A Review
Cristina Di Salvo
Water (2022) Vol. 14, Iss. 15, pp. 2307-2307
Open Access | Times Cited: 29
Comparison of Hydrological Platforms in Assessing Rainfall-Runoff Behavior in a Mediterranean Watershed of Northern Morocco
Mourad Aqnouy, Mohamed Ahmed, Gebiaw T. Ayele, et al.
Water (2023) Vol. 15, Iss. 3, pp. 447-447
Open Access | Times Cited: 20
Connecting hydrological modelling and forecasting from global to local scales: Perspectives from an international joint virtual workshop
Antara Dasgupta, Louise Arnal, Rebecca Emerton, et al.
Journal of Flood Risk Management (2023)
Open Access | Times Cited: 19
Flood Simulations Using a Sensor Network and Support Vector Machine Model
Jakub Langhammer
Water (2023) Vol. 15, Iss. 11, pp. 2004-2004
Open Access | Times Cited: 19
Enhancing Land Cover/Land Use (LCLU) classification through a comparative analysis of hyperparameters optimization approaches for deep neural network (DNN)
Ali Azedou, Aouatif Amine, Isaya Kisekka, et al.
Ecological Informatics (2023) Vol. 78, pp. 102333-102333
Open Access | Times Cited: 19
AI tools such as ChatGPT will disrupt hydrology, too
Landon J. S. Halloran, Saeed Mhanna, Philip Brunner
Hydrological Processes (2023) Vol. 37, Iss. 3
Open Access | Times Cited: 17
Spatial Downscaling of Satellite-Based Soil Moisture Products Using Machine Learning Techniques: A Review
I.P. Senanayake, Kalani R. L. Pathira Arachchilage, In‐Young Yeo, et al.
Remote Sensing (2024) Vol. 16, Iss. 12, pp. 2067-2067
Open Access | Times Cited: 8
Estimating Irrigation Water Consumption Using Machine Learning and Remote Sensing Data in Kansas High Plains
Shiqi Wei, Tianfang Xu, Guo‐Yue Niu, et al.
Remote Sensing (2022) Vol. 14, Iss. 13, pp. 3004-3004
Open Access | Times Cited: 28
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: 28
LAI estimation across California vineyards using sUAS multi-seasonal multi-spectral, thermal, and elevation information and machine learning
Rui Gao, Alfonso F. Torres‐Rua, Mahyar Aboutalebi, et al.
Irrigation Science (2022) Vol. 40, Iss. 4-5, pp. 731-759
Closed Access | Times Cited: 24
A Framework on Fast Mapping of Urban Flood Based on a Multi-Objective Random Forest Model
Yaoxing Liao, Zhaoli Wang, Chengguang Lai, et al.
International Journal of Disaster Risk Science (2023) Vol. 14, Iss. 2, pp. 253-268
Open Access | Times Cited: 17
Flood forecasting based on radar precipitation nowcasting using U-net and its improved models
Jianzhu Li, Leijing Li, Ting Zhang, et al.
Journal of Hydrology (2024) Vol. 632, pp. 130871-130871
Closed Access | Times Cited: 6
Assessment of the impacts of climate change on the construction of homogeneous climatic regions and ensemble climate projections using CMIP6 data over Pakistan
Muhammad Abbas, Firdos Khan, Yuei‐An Liou, et al.
Atmospheric Research (2024) Vol. 304, pp. 107359-107359
Closed Access | Times Cited: 6
A Probabilistic Approach to Training Machine Learning Models Using Noisy Data
Ayman H. Alzraiee, Richard G. Niswonger
Environmental Modelling & Software (2024) Vol. 179, pp. 106133-106133
Open Access | Times Cited: 6
Deep learning algorithms and their fuzzy extensions for streamflow prediction in climate change framework
Rishith Kumar Vogeti, Rahul Jauhari, Bhavesh Rahul Mishra, et al.
Journal of Water and Climate Change (2024) Vol. 15, Iss. 2, pp. 832-848
Open Access | Times Cited: 5
An explainable hybrid framework for estimating daily reference evapotranspiration: Combining extreme gradient boosting with Nelder-Mead method
Babak Mohammadi, Mingjie Chen, Mohammad Reza Nikoo, et al.
Journal of Hydrology (2024) Vol. 644, pp. 132130-132130
Closed Access | Times Cited: 5
Estimating the Routing Parameter of the Xin’anjiang Hydrological Model Based on Remote Sensing Data and Machine Learning
Yuanhao Fang, Yizhi Huang, Bo Qu, et al.
Remote Sensing (2022) Vol. 14, Iss. 18, pp. 4609-4609
Open Access | Times Cited: 23
Enhancing Flood Simulation in Data-Limited Glacial River Basins through Hybrid Modeling and Multi-Source Remote Sensing Data
Weiwei Ren, Xin Li, Donghai Zheng, et al.
Remote Sensing (2023) Vol. 15, Iss. 18, pp. 4527-4527
Open Access | Times Cited: 13

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