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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 susceptibility using random-forest and boosted-tree models in Seoul metropolitan city, Korea
Sunmin Lee, Jeong-Cheol Kim, Hyung-Sup Jung, et al.
Geomatics Natural Hazards and Risk (2017) Vol. 8, Iss. 2, pp. 1185-1203
Open Access | Times Cited: 337

Showing 1-25 of 337 citing articles:

An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines
Bahram Choubin, Ehsan Moradi, Mohammad Golshan, et al.
The Science of The Total Environment (2018) Vol. 651, pp. 2087-2096
Open Access | Times Cited: 663
Ensemble machine learning paradigms in hydrology: A review
Mohammad Zounemat‐Kermani, Okke Batelaan, Marzieh Fadaee, et al.
Journal of Hydrology (2021) Vol. 598, pp. 126266-126266
Open Access | Times Cited: 440
Application of fuzzy weight of evidence and data mining techniques in construction of flood susceptibility map of Poyang County, China
Haoyuan Hong, Paraskevas Tsangaratos, Ioanna Ilia, et al.
The Science of The Total Environment (2017) Vol. 625, pp. 575-588
Closed Access | Times Cited: 374
Flash-flood hazard assessment using ensembles and Bayesian-based machine learning models: Application of the simulated annealing feature selection method
Farzaneh Sajedi Hosseini, Bahram Choubin, Amir Mosavi, et al.
The Science of The Total Environment (2019) Vol. 711, pp. 135161-135161
Open Access | Times Cited: 304
Identifying the essential flood conditioning factors for flood prone area mapping using machine learning techniques
Mahyat Shafapour Tehrany, Simon Jones, Farzin Shabani
CATENA (2018) Vol. 175, pp. 174-192
Closed Access | Times Cited: 299
A novel hybrid approach based on a swarm intelligence optimized extreme learning machine for flash flood susceptibility mapping
Dieu Tien Bui, Phuong Thao Thi Ngo, Tien Dat Pham, et al.
CATENA (2019) Vol. 179, pp. 184-196
Closed Access | Times Cited: 270
Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods
Dieu Tien Bui, Paraskevas Tsangaratos, Phuong Thao Thi Ngo, et al.
The Science of The Total Environment (2019) Vol. 668, pp. 1038-1054
Closed Access | Times Cited: 248
Integrated machine learning methods with resampling algorithms for flood susceptibility prediction
Esmaeel Dodangeh, Bahram Choubin, Ahmad Najafi Eigdir, et al.
The Science of The Total Environment (2019) Vol. 705, pp. 135983-135983
Closed Access | Times Cited: 223
Evaluating urban flood risk using hybrid method of TOPSIS and machine learning
Elham Rafiei-Sardooi, Ali Azareh, Bahram Choubin, et al.
International Journal of Disaster Risk Reduction (2021) Vol. 66, pp. 102614-102614
Open Access | Times Cited: 215
GIS Based Hybrid Computational Approaches for Flash Flood Susceptibility Assessment
Binh Thai Pham, Mohammadtaghi Avand, Saeid Janizadeh, et al.
Water (2020) Vol. 12, Iss. 3, pp. 683-683
Open Access | Times Cited: 207
Flash-flood susceptibility mapping based on XGBoost, random forest and boosted regression trees
Rahebeh Abedi, Romulus Costache, Hossein Shafizadeh‐Moghadam, et al.
Geocarto International (2021) Vol. 37, Iss. 19, pp. 5479-5496
Closed Access | Times Cited: 204
GIS-based groundwater potential mapping in Shahroud plain, Iran. A comparison among statistical (bivariate and multivariate), data mining and MCDM approaches
Alireza Arabameri, Khalil Rezaei, Artemi Cerdà, et al.
The Science of The Total Environment (2018) Vol. 658, pp. 160-177
Open Access | Times Cited: 195
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
Explainable heat-related mortality with random forest and SHapley Additive exPlanations (SHAP) models
Yesuel Kim, Young‐Chul Kim
Sustainable Cities and Society (2022) Vol. 79, pp. 103677-103677
Closed Access | Times Cited: 175
Hazard and vulnerability in urban flood risk mapping: Machine learning techniques and considering the role of urban districts
Mohammad Reza Eini, Hesam Seyed Kaboli, Mohsen Rashidian, et al.
International Journal of Disaster Risk Reduction (2020) Vol. 50, pp. 101687-101687
Closed Access | Times Cited: 161
Urban flood modeling using deep-learning approaches in Seoul, South Korea
Xinxiang Lei, Wei Chen, Mahdi Panahi, et al.
Journal of Hydrology (2021) Vol. 601, pp. 126684-126684
Open Access | Times Cited: 160
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
Improvement of Best First Decision Trees Using Bagging and Dagging Ensembles for Flood Probability Mapping
Peyman Yariyan, Saeid Janizadeh, Tran Van Phong, et al.
Water Resources Management (2020) Vol. 34, Iss. 9, pp. 3037-3053
Closed Access | Times Cited: 148
Evaluation of multi-hazard map produced using MaxEnt machine learning technique
Narges Javidan, Ataollah Kavian, Hamid Reza Pourghasemi, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 114
Spatial flood susceptibility mapping using an explainable artificial intelligence (XAI) model
Biswajeet Pradhan, Saro Lee, Abhirup Dikshit, et al.
Geoscience Frontiers (2023) Vol. 14, Iss. 6, pp. 101625-101625
Open Access | Times Cited: 105
A novel flood risk mapping approach with machine learning considering geomorphic and socio-economic vulnerability dimensions
Prakhar Deroliya, Mousumi Ghosh, Mohit Prakash Mohanty, et al.
The Science of The Total Environment (2022) Vol. 851, pp. 158002-158002
Closed Access | Times Cited: 72
Flood susceptibility mapping using machine learning boosting algorithms techniques in Idukki district of Kerala India
Subbarayan Saravanan, Devanantham Abijith, Nagireddy Masthan Reddy, et al.
Urban Climate (2023) Vol. 49, pp. 101503-101503
Closed Access | Times Cited: 59
Flood susceptible prediction through the use of geospatial variables and machine learning methods
Navid Mahdizadeh Gharakhanlou, Liliana Pérez
Journal of Hydrology (2023) Vol. 617, pp. 129121-129121
Closed Access | Times Cited: 54
Enhancing flood risk assessment through integration of ensemble learning approaches and physical-based hydrological modeling
Mohamed Saber, Tayeb Boulmaiz, Mawloud Guermoui, et al.
Geomatics Natural Hazards and Risk (2023) Vol. 14, Iss. 1
Open Access | Times Cited: 52
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