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:

Assessing the performance of GIS- based machine learning models with different accuracy measures for determining susceptibility to gully erosion
Younes Garosi, Mohsen Sheklabadi, Christian Conoscenti, et al.
The Science of The Total Environment (2019) Vol. 664, pp. 1117-1132
Closed Access | Times Cited: 189

Showing 1-25 of 189 citing articles:

Spatial prediction of groundwater potential mapping based on convolutional neural network (CNN) and support vector regression (SVR)
Mahdi Panahi, Nitheshnirmal Sãdhasivam, Hamid Reza Pourghasemi, et al.
Journal of Hydrology (2020) Vol. 588, pp. 125033-125033
Closed Access | Times Cited: 290
Susceptibility Mapping of Soil Water Erosion Using Machine Learning Models
Amirhosein Mosavi, Farzaneh Sajedi Hosseini, Bahram Choubin, et al.
Water (2020) Vol. 12, Iss. 7, pp. 1995-1995
Open Access | Times Cited: 144
Decision tree based ensemble machine learning approaches for landslide susceptibility mapping
Alireza Arabameri, Subodh Chandra Pal, Fatemeh Rezaie, et al.
Geocarto International (2021) Vol. 37, Iss. 16, pp. 4594-4627
Closed Access | Times Cited: 105
Digital exploration of selected heavy metals using Random Forest and a set of environmental covariates at the watershed scale
Shohreh Moradpour, Mojgan Entezari, Shamsollah Ayoubi, et al.
Journal of Hazardous Materials (2023) Vol. 455, pp. 131609-131609
Closed Access | Times Cited: 49
Machine learning models for gully erosion susceptibility assessment in the Tensift catchment, Haouz Plain, Morocco for sustainable development
Youssef Bammou, Brahim Benzougagh, Abdessalam Ouallali, et al.
Journal of African Earth Sciences (2024) Vol. 213, pp. 105229-105229
Open Access | Times Cited: 24
Comparative assessment of the flash-flood potential within small mountain catchments using bivariate statistics and their novel hybrid integration with machine learning models
Romulus Costache, Haoyuan Hong, Quoc Bao Pham
The Science of The Total Environment (2019) Vol. 711, pp. 134514-134514
Closed Access | Times Cited: 127
GIS-Based Machine Learning Algorithms for Gully Erosion Susceptibility Mapping in a Semi-Arid Region of Iran
Xinxiang Lei, Wei Chen, Mohammadtaghi Avand, et al.
Remote Sensing (2020) Vol. 12, Iss. 15, pp. 2478-2478
Open Access | Times Cited: 121
Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process
Hamid Reza Pourghasemi, Nitheshnirmal Sãdhasivam, Narges Kariminejad, et al.
Geoscience Frontiers (2020) Vol. 11, Iss. 6, pp. 2207-2219
Open Access | Times Cited: 115
Novel ensembles of COPRAS multi-criteria decision-making with logistic regression, boosted regression tree, and random forest for spatial prediction of gully erosion susceptibility
Alireza Arabameri, M Yamani, Biswajeet Pradhan, et al.
The Science of The Total Environment (2019) Vol. 688, pp. 903-916
Closed Access | Times Cited: 114
A Comparative Assessment of Random Forest and k-Nearest Neighbor Classifiers for Gully Erosion Susceptibility Mapping
Mohammadtaghi Avand, Saeid Janizadeh, Seyed Amir Naghibi, et al.
Water (2019) Vol. 11, Iss. 10, pp. 2076-2076
Open Access | Times Cited: 103
Identifying the Contributions of Multi-Source Data for Winter Wheat Yield Prediction in China
Juan Cao, Zhao Zhang, Fulu Tao, et al.
Remote Sensing (2020) Vol. 12, Iss. 5, pp. 750-750
Open Access | Times Cited: 98
GIS-Based Gully Erosion Susceptibility Mapping: A Comparison of Computational Ensemble Data Mining Models
Viet‐Ha Nhu, Saeid Janizadeh, Mohammadtaghi Avand, et al.
Applied Sciences (2020) Vol. 10, Iss. 6, pp. 2039-2039
Open Access | Times Cited: 98
Machine learning applications for water-induced soil erosion modeling and mapping
Hossein Sahour, Vahid Gholami, Mehdi Vazifedan, et al.
Soil and Tillage Research (2021) Vol. 211, pp. 105032-105032
Closed Access | Times Cited: 82
Hybrid ensemble machine learning approaches for landslide susceptibility mapping using different sampling ratios at East Sikkim Himalayan, India
Sunil Saha, Jagabandhu Roy, Biswajeet Pradhan, et al.
Advances in Space Research (2021) Vol. 68, Iss. 7, pp. 2819-2840
Closed Access | Times Cited: 82
Novel Machine Learning Approaches for Modelling the Gully Erosion Susceptibility
Alireza Arabameri, Omid Asadi Nalivan, Subodh Chandra Pal, et al.
Remote Sensing (2020) Vol. 12, Iss. 17, pp. 2833-2833
Open Access | Times Cited: 78
Using machine learning algorithms to map the groundwater recharge potential zones
Hamid Reza Pourghasemi, Nitheshnirmal Sãdhasivam, Saleh Yousefi, et al.
Journal of Environmental Management (2020) Vol. 265, pp. 110525-110525
Closed Access | Times Cited: 75
Machine learning algorithm-based risk assessment of riparian wetlands in Padma River Basin of Northwest Bangladesh
Abu Reza Md. Towfiqul Islam, Swapan Talukdar, Susanta Mahato, et al.
Environmental Science and Pollution Research (2021) Vol. 28, Iss. 26, pp. 34450-34471
Closed Access | Times Cited: 72
Field based index of flood vulnerability (IFV): A new validation technique for flood susceptible models
Susanta Mahato, Swades Pal, Swapan Talukdar, et al.
Geoscience Frontiers (2021) Vol. 12, Iss. 5, pp. 101175-101175
Open Access | Times Cited: 68
Correlation of banana productivity levels and soil morphological properties using regularized optimal scaling regression
Barlín Orlando Olivares, Julio Calero, Juan Carlos Rey, et al.
CATENA (2021) Vol. 208, pp. 105718-105718
Open Access | Times Cited: 66
Evaluation of different DEMs for gully erosion susceptibility mapping using in-situ field measurement and validation
Indrajit Chowdhuri, Subodh Chandra Pal, Asish Saha, et al.
Ecological Informatics (2021) Vol. 65, pp. 101425-101425
Closed Access | Times Cited: 65
Predicting the number of dusty days around the desert wetlands in southeastern Iran using feature selection and machine learning techniques
Zohre Ebrahimi‐Khusfi, Ali Reza Nafarzadegan, Fatemeh Dargahian
Ecological Indicators (2021) Vol. 125, pp. 107499-107499
Open Access | Times Cited: 64
How far spatial resolution affects the ensemble machine learning based flood susceptibility prediction in data sparse region
Tamal Kanti Saha, Swades Pal, Swapan Talukdar, et al.
Journal of Environmental Management (2021) Vol. 297, pp. 113344-113344
Open Access | Times Cited: 59
Spatial modeling of gully head erosion on the Loess Plateau using a certainty factor and random forest model
Chengcheng Jiang, Wen Fan, Ningyu Yu, et al.
The Science of The Total Environment (2021) Vol. 783, pp. 147040-147040
Closed Access | Times Cited: 58
Identification of Soil Properties Associated with the Incidence of Banana Wilt Using Supervised Methods
Barlín Orlando Olivares, Andrés Vega, M. Angélica Rueda Calderón, et al.
Plants (2022) Vol. 11, Iss. 15, pp. 2070-2070
Open Access | Times Cited: 51
Landslide Susceptibility Mapping Using Machine Learning Algorithm: A Case Study Along Karakoram Highway (KKH), Pakistan
Muhammad Afaq Hussain, Zhanlong Chen, Isma Kalsoom, et al.
Journal of the Indian Society of Remote Sensing (2022) Vol. 50, Iss. 5, pp. 849-866
Closed Access | Times Cited: 49
Page 1 - Next Page

Scroll to top