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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:

Spatial prediction of flood potential using new ensembles of bivariate statistics and artificial intelligence: A case study at the Putna river catchment of Romania
Romulus Costache, Dieu Tien Bui
The Science of The Total Environment (2019) Vol. 691, pp. 1098-1118
Closed Access | Times Cited: 149

Showing 1-25 of 149 citing articles:

GIS-based comparative assessment of flood susceptibility mapping using hybrid multi-criteria decision-making approach, naïve Bayes tree, bivariate statistics and logistic regression: A case of Topľa basin, Slovakia
Sk Ajim Ali, Farhana Parvin, Quoc Bao Pham, et al.
Ecological Indicators (2020) Vol. 117, pp. 106620-106620
Open Access | Times Cited: 264
Application of remote sensing and machine learning algorithms for forest fire mapping in a Mediterranean area
Meriame Mohajane, Romulus Costache, Firoozeh Karimi, et al.
Ecological Indicators (2021) Vol. 129, pp. 107869-107869
Open Access | Times Cited: 262
Prediction Success of Machine Learning Methods for Flash Flood Susceptibility Mapping in the Tafresh Watershed, Iran
Saeid Janizadeh, Mohammadtaghi Avand, Abolfazl Jaafari, et al.
Sustainability (2019) Vol. 11, Iss. 19, pp. 5426-5426
Open Access | Times Cited: 234
Flood hazard mapping methods: A review
Rofiat Bunmi Mudashiru, Nuridah Sabtu, Ismail Abustan, et al.
Journal of Hydrology (2021) Vol. 603, pp. 126846-126846
Closed Access | Times Cited: 233
Flash-Flood Susceptibility Assessment Using Multi-Criteria Decision Making and Machine Learning Supported by Remote Sensing and GIS Techniques
Romulus Costache, Quoc Bao Pham, Ehsan Sharifi, et al.
Remote Sensing (2019) Vol. 12, Iss. 1, pp. 106-106
Open Access | Times Cited: 230
Flood susceptibility modeling in Teesta River basin, Bangladesh using novel ensembles of bagging algorithms
Swapan Talukdar, Bonosri Ghose, Shahfahad, et al.
Stochastic Environmental Research and Risk Assessment (2020) Vol. 34, Iss. 12, pp. 2277-2300
Closed Access | Times Cited: 184
Optimization of state-of-the-art fuzzy-metaheuristic ANFIS-based machine learning models for flood susceptibility prediction mapping in the Middle Ganga Plain, India
Aman Arora, Alireza Arabameri, Manish Pandey, et al.
The Science of The Total Environment (2020) Vol. 750, pp. 141565-141565
Open Access | Times Cited: 180
Toward an Integrated Disaster Management Approach: How Artificial Intelligence Can Boost Disaster Management
Sheikh Kamran Abid, Noralfishah Sulaiman, Shiau Wei Chan, et al.
Sustainability (2021) Vol. 13, Iss. 22, pp. 12560-12560
Open Access | Times Cited: 116
Novel ensemble machine learning models in flood susceptibility mapping
Pankaj Prasad, Victor J. Loveson, Bappa Das, et al.
Geocarto International (2021) Vol. 37, Iss. 16, pp. 4571-4593
Closed Access | Times Cited: 106
Predicting and analyzing flood susceptibility using boosting-based ensemble machine learning algorithms with SHapley Additive exPlanations
Halit Enes Aydin, Muzaffer Can İban
Natural Hazards (2022) Vol. 116, Iss. 3, pp. 2957-2991
Closed Access | Times Cited: 89
Application of genetic algorithm in optimization parallel ensemble-based machine learning algorithms to flood susceptibility mapping using radar satellite imagery
Seyed Vahid Razavi-Termeh, Abolghasem Sadeghi‐Niaraki, Myoung-Bae Seo, et al.
The Science of The Total Environment (2023) Vol. 873, pp. 162285-162285
Closed Access | Times Cited: 49
GIS-based machine learning algorithm for flood susceptibility analysis in the Pagla river basin, Eastern India
Nur Islam Saikh, Prolay Mondal
Natural Hazards Research (2023) Vol. 3, Iss. 3, pp. 420-436
Open Access | Times Cited: 47
A 100 m gridded population dataset of China's seventh census using ensemble learning and big geospatial data
Yuehong Chen, Congcong Xu, Yong Ge, et al.
Earth system science data (2024) Vol. 16, Iss. 8, pp. 3705-3718
Open Access | Times Cited: 23
Flood susceptibility assessment of the Agartala Urban Watershed, India, using Machine Learning Algorithm
Jatan Debnath, Jimmi Debbarma, Amal Debnath, et al.
Environmental Monitoring and Assessment (2024) Vol. 196, Iss. 2
Closed Access | Times Cited: 20
A Systematic Review of Urban Flood Susceptibility Mapping: Remote Sensing, Machine Learning, and Other Modeling Approaches
Tania Islam, Ethiopia Bisrat Zeleke, Mahmud Afroz, et al.
Remote Sensing (2025) Vol. 17, Iss. 3, pp. 524-524
Open Access | Times Cited: 2
Identification of areas prone to flash-flood phenomena using multiple-criteria decision-making, bivariate statistics, machine learning and their ensembles
Romulus Costache, Dieu Tien Bui
The Science of The Total Environment (2020) Vol. 712, pp. 136492-136492
Closed Access | Times Cited: 134
Using machine learning models, remote sensing, and GIS to investigate the effects of changing climates and land uses on flood probability
Mohammadtaghi Avand, Hamidreza Moradi, Mehdi Ramazanzadeh lasboyee
Journal of Hydrology (2020) Vol. 595, pp. 125663-125663
Closed Access | Times Cited: 130
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
Flood susceptibility mapping and assessment using a novel deep learning model combining multilayer perceptron and autoencoder neural networks
Mohammad Ahmadlou, A’kif Al-Fugara, Abdel Rahman Al‐Shabeeb, et al.
Journal of Flood Risk Management (2020) Vol. 14, Iss. 1
Open Access | Times Cited: 114
Spatial predicting of flood potential areas using novel hybridizations of fuzzy decision-making, bivariate statistics, and machine learning
Romulus Costache, Mihnea Cristian Popa, Dieu Tien Bui, et al.
Journal of Hydrology (2020) Vol. 585, pp. 124808-124808
Closed Access | Times Cited: 113
Novel hybrid models between bivariate statistics, artificial neural networks and boosting algorithms for flood susceptibility assessment
Romulus Costache, Quoc Bao Pham, Mohammadtaghi Avand, et al.
Journal of Environmental Management (2020) Vol. 265, pp. 110485-110485
Closed Access | Times Cited: 111
DEM resolution effects on machine learning performance for flood probability mapping
Mohammadtaghi Avand, Alban Kuriqi, Majid Khazaei, et al.
Journal of Hydro-environment Research (2021) Vol. 40, pp. 1-16
Open Access | Times Cited: 104
Improved flood susceptibility mapping using a best first decision tree integrated with ensemble learning techniques
Binh Thai Pham, Abolfazl Jaafari, Tran Van Phong, et al.
Geoscience Frontiers (2020) Vol. 12, Iss. 3, pp. 101105-101105
Open Access | Times Cited: 95
GIS-Based Site Selection for Check Dams in Watersheds: Considering Geomorphometric and Topo-Hydrological Factors
Omid Rahmati, Zahra Kalantari, Mahmood Samadi, et al.
Sustainability (2019) Vol. 11, Iss. 20, pp. 5639-5639
Open Access | Times Cited: 83
GIS-based spatial modeling of snow avalanches using four novel ensemble models
Peyman Yariyan, Mohammadtaghi Avand, Rahim Ali Abbaspour, et al.
The Science of The Total Environment (2020) Vol. 745, pp. 141008-141008
Closed Access | Times Cited: 78
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