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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:

Predictive mapping of aquatic ecosystems by means of support vector machines and random forests
Pedro Martínez‐Santos, H.F. Aristizábal, Silvia Díaz-Alcaide, et al.
Journal of Hydrology (2021) Vol. 595, pp. 126026-126026
Closed Access | Times Cited: 24

Showing 24 citing articles:

Preprocessing approaches in machine-learning-based groundwater potential mapping: an application to the Koulikoro and Bamako regions, Mali
Víctor Gómez‐Escalonilla, Pedro Martínez‐Santos, Miguel Martín-Loeches
Hydrology and earth system sciences (2022) Vol. 26, Iss. 2, pp. 221-243
Open Access | Times Cited: 49
Prediction of soil salinity parameters using machine learning models in an arid region of northwest China
Chao Xiao, Qingyuan Ji, Junqing Chen, et al.
Computers and Electronics in Agriculture (2022) Vol. 204, pp. 107512-107512
Closed Access | Times Cited: 42
Mapping groundwater-dependent ecosystems by means of multi-layer supervised classification
Pedro Martínez‐Santos, Silvia Díaz-Alcaide, África de la Hera Portillo, et al.
Journal of Hydrology (2021) Vol. 603, pp. 126873-126873
Open Access | Times Cited: 43
Salt stress responses of microalgae biofilm communities under controlled microcosm conditions
Khawla Lazrak, Maren Nothof, Zakaria Tazart, et al.
Algal Research (2024) Vol. 78, pp. 103430-103430
Closed Access | Times Cited: 4
Delineation of groundwater potential zones by means of ensemble tree supervised classification methods in the Eastern Lake Chad basin
Víctor Gómez‐Escalonilla, Marie-Louise Vogt, Elisa Destro, et al.
Geocarto International (2021) Vol. 37, Iss. 25, pp. 8924-8951
Open Access | Times Cited: 25
Pore pressure prediction assisted by machine learning models combined with interpretations: A case study of an HTHP gas field, Yinggehai Basin
Xiaobo Zhao, Xiaojun Chen, Zhangjian Lan, et al.
Geoenergy Science and Engineering (2023) Vol. 229, pp. 212114-212114
Closed Access | Times Cited: 9
Deep-Learning-Based Automatic Extraction of Aquatic Vegetation from Sentinel-2 Images—A Case Study of Lake Honghu
Hangyu Gao, Ruren Li, Qian Shen, et al.
Remote Sensing (2024) Vol. 16, Iss. 5, pp. 867-867
Open Access | Times Cited: 3
A parallel integrated learning technique of improved particle swarm optimization and BP neural network and its application
Jingming Li, Dong Xu, Sumei Ruan, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 14
A comparison of multiple methods for mapping groundwater levels in the Mu Us Sandy Land, China
Pinzeng Rao, Yicheng Wang, Yang Liu, et al.
Journal of Hydrology Regional Studies (2022) Vol. 43, pp. 101189-101189
Closed Access | Times Cited: 13
Atlantic salmon habitat-abundance modeling using machine learning methods
Bähar Jelovica, Jaakko Erkinaro, Panu Orell, et al.
Ecological Indicators (2024) Vol. 160, pp. 111832-111832
Open Access | Times Cited: 2
Modeling potential wetland distributions in China based on geographic big data and machine learning algorithms
Hengxing Xiang, Yanbiao Xi, Dehua Mao, et al.
International Journal of Digital Earth (2023) Vol. 16, Iss. 1, pp. 3706-3724
Open Access | Times Cited: 6
Insights into biogeochemistry and hot spots distribution characteristics of redox-sensitive elements in the hyporheic zone: Transformation mechanisms and contributing factors
Yu Li, Mingzhu Liu, Xiong Wu
The Science of The Total Environment (2024) Vol. 918, pp. 170587-170587
Closed Access | Times Cited: 2
Within and among farm variability of coffee quality of smallholders in southwest Ethiopia
Merkebu Getachew, Pascal Boeckx, Kris Verheyen, et al.
Agroforestry Systems (2023) Vol. 97, Iss. 5, pp. 883-905
Closed Access | Times Cited: 2
Identification of non-conventional groundwater resources by means of machine learning in the Aconcagua basin, Chile
M. Aliaga-Alvarado, Víctor Gómez‐Escalonilla, Pedro Martínez‐Santos
Journal of Hydrology Regional Studies (2023) Vol. 49, pp. 101502-101502
Open Access | Times Cited: 2
Using Unmanned Aerial Vehicle and LiDAR-Derived DEMs to Estimate Channels of Small Tributary Streams
Joan Grau, Kang Liang, Jae Ogilvie, et al.
Remote Sensing (2021) Vol. 13, Iss. 17, pp. 3380-3380
Open Access | Times Cited: 5
Automated identification of earthen berms in Western US rangelands from LiDAR‐based digital elevation models
Haiqing Xu, Mary Nichols, Dana Lapides, et al.
Earth Surface Processes and Landforms (2024)
Open Access
Quantifying Streamflow Prediction Uncertainty Through Process‐Aware Data‐Driven Models
Abhinanda Roy, K. S. Kasiviswanathan
Hydrological Processes (2024) Vol. 38, Iss. 11
Closed Access
Comparing Pixel-and Object-Based Approaches for Classifying Benthic Habitats
H. Clifton Simmons, Oli Dalby, Daniel Ierodiaconou, et al.
Research Square (Research Square) (2024)
Open Access
Preprocessing approaches in machine learning-based groundwater potential mapping: an application to the Koulikoro and Bamako regions, Mali
Víctor Gómez‐Escalonilla, Pedro Martínez‐Santos, Miguel Martín-Loeches
(2021)
Open Access | Times Cited: 2
Shedding light on the decline of Iberian freshwater fish species over the period 1980–2020
Carlotta Valerio, Rocío A. Baquero, Graciela Gómez Nicola, et al.
Freshwater Biology (2022) Vol. 67, Iss. 10, pp. 1690-1707
Open Access | Times Cited: 1
Prediction of Rural Investment and Construction Funds Based on Logistic Regression and Support Vector Machine Combination Model
Long Jing
(2022), pp. 185-190
Closed Access | Times Cited: 1
Reply on RC2
Victor Gómez-Escalonilla
(2021)
Open Access
Reply on RC1
Victor Gómez-Escalonilla
(2021)
Open Access
Comment on hess-2021-261
Víctor Gà mez-Escalonilla, Pedro Martínez-Santos, Miguel Martín-Loeches
(2021)
Open Access
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