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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:

Flood susceptibility mapping using novel ensembles of adaptive neuro fuzzy inference system and metaheuristic algorithms
Seyed Vahid Razavi Termeh, Aiding Kornejady, Hamid Reza Pourghasemi, et al.
The Science of The Total Environment (2017) Vol. 615, pp. 438-451
Closed Access | Times Cited: 428

Showing 1-25 of 428 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
A comparative assessment of flood susceptibility modeling using Multi-Criteria Decision-Making Analysis and Machine Learning Methods
Khabat Khosravi, Himan Shahabi, Binh Thai Pham, et al.
Journal of Hydrology (2019) Vol. 573, pp. 311-323
Closed Access | Times Cited: 562
Flood susceptibility modelling using advanced ensemble machine learning models
Abu Reza Md. Towfiqul Islam, Swapan Talukdar, Susanta Mahato, et al.
Geoscience Frontiers (2020) Vol. 12, Iss. 3, pp. 101075-101075
Open Access | Times Cited: 427
Modeling flood susceptibility using data-driven approaches of naïve Bayes tree, alternating decision tree, and random forest methods
Wei Chen, Yang Li, Weifeng Xue, et al.
The Science of The Total Environment (2019) Vol. 701, pp. 134979-134979
Closed Access | Times Cited: 390
Novel forecasting approaches using combination of machine learning and statistical models for flood susceptibility mapping
Hossein Shafizadeh‐Moghadam, Roozbeh Valavi, Himan Shahabi, et al.
Journal of Environmental Management (2018) Vol. 217, pp. 1-11
Open Access | Times Cited: 327
Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory
Thimmaiah Gudiyangada Nachappa, Sepideh Tavakkoli Piralilou, Khalil Gholamnia, et al.
Journal of Hydrology (2020) Vol. 590, pp. 125275-125275
Closed Access | Times Cited: 313
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
GIS-based groundwater potential analysis using novel ensemble weights-of-evidence with logistic regression and functional tree models
Wei Chen, Hui Li, Enke Hou, et al.
The Science of The Total Environment (2018) Vol. 634, pp. 853-867
Open Access | Times Cited: 292
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
Flood susceptibility modelling using novel hybrid approach of reduced-error pruning trees with bagging and random subspace ensembles
Wei Chen, Haoyuan Hong, Shaojun Li, et al.
Journal of Hydrology (2019) Vol. 575, pp. 864-873
Closed Access | Times Cited: 268
Hybrid artificial intelligence models based on a neuro-fuzzy system and metaheuristic optimization algorithms for spatial prediction of wildfire probability
Abolfazl Jaafari, Eric K. Zenner, Mahdi Panahi, et al.
Agricultural and Forest Meteorology (2019) Vol. 266-267, pp. 198-207
Closed Access | Times Cited: 265
Flood Susceptibility Mapping on a National Scale in Slovakia Using the Analytical Hierarchy Process
Matej Vojtek, Jana Vojteková
Water (2019) Vol. 11, Iss. 2, pp. 364-364
Open Access | Times Cited: 261
Flood susceptibility mapping using convolutional neural network frameworks
Yi Wang, Zhice Fang, Haoyuan Hong, et al.
Journal of Hydrology (2019) Vol. 582, pp. 124482-124482
Closed Access | Times Cited: 260
A comparison of statistical methods and multi-criteria decision making to map flood hazard susceptibility in Northern Iran
Alireza Arabameri, Khalil Rezaei, Artemi Cerdà, et al.
The Science of The Total Environment (2019) Vol. 660, pp. 443-458
Closed Access | Times Cited: 259
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
Flood susceptibility assessment using integration of adaptive network-based fuzzy inference system (ANFIS) and biogeography-based optimization (BBO) and BAT algorithms (BA)
Mohammad Ahmadlou, Mohammad Karimi, Somayeh Alizadeh, et al.
Geocarto International (2018) Vol. 34, Iss. 11, pp. 1252-1272
Closed Access | Times Cited: 243
Meta optimization of an adaptive neuro-fuzzy inference system with grey wolf optimizer and biogeography-based optimization algorithms for spatial prediction of landslide susceptibility
Abolfazl Jaafari, Mahdi Panahi, Binh Thai Pham, et al.
CATENA (2019) Vol. 175, pp. 430-445
Closed Access | Times Cited: 236
Assessment of urban flood susceptibility using semi-supervised machine learning model
Gang Zhao, Bo Pang, Zongxue Xu, et al.
The Science of The Total Environment (2018) Vol. 659, pp. 940-949
Closed 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
Recent advances in neuro-fuzzy system: A survey
K.V. Shihabudheen, G. N. Pillai
Knowledge-Based Systems (2018) Vol. 152, pp. 136-162
Closed Access | Times Cited: 224
Assessment of the importance of gully erosion effective factors using Boruta algorithm and its spatial modeling and mapping using three machine learning algorithms
Mahdis Amiri, Hamid Reza Pourghasemi, Gholamabbas Ghanbarian, et al.
Geoderma (2019) Vol. 340, pp. 55-69
Closed Access | Times Cited: 222
Flood susceptibility mapping in Dingnan County (China) using adaptive neuro-fuzzy inference system with biogeography based optimization and imperialistic competitive algorithm
Yi Wang, Haoyuan Hong, Wei Chen, et al.
Journal of Environmental Management (2019) Vol. 247, pp. 712-729
Closed Access | Times Cited: 222
New Hybrids of ANFIS with Several Optimization Algorithms for Flood Susceptibility Modeling
Dieu Tien Bui, Khabat Khosravi, Shaojun Li, et al.
Water (2018) Vol. 10, Iss. 9, pp. 1210-1210
Open Access | Times Cited: 215
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