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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:

A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran
Khabat Khosravi, Binh Thai Pham, Kamran Chapi, et al.
The Science of The Total Environment (2018) Vol. 627, pp. 744-755
Closed Access | Times Cited: 653

Showing 1-25 of 653 citing articles:

Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance
Abdelaziz Merghadi, Ali P. Yunus, Jie Dou, et al.
Earth-Science Reviews (2020) Vol. 207, pp. 103225-103225
Closed Access | Times Cited: 825
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
A survey on river water quality modelling using artificial intelligence models: 2000–2020
Tiyasha Tiyasha, Tran Minh Tung, Zaher Mundher Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬
Journal of Hydrology (2020) Vol. 585, pp. 124670-124670
Closed Access | Times Cited: 531
Flood Prediction Using Machine Learning Models: Literature Review
Amir Mosavi, Pınar Öztürk, Kwok‐wing Chau
Water (2018) Vol. 10, Iss. 11, pp. 1536-1536
Open Access | Times Cited: 476
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: 389
A novel deep learning neural network approach for predicting flash flood susceptibility: A case study at a high frequency tropical storm area
Dieu Tien Bui, Nhat‐Duc Hoang, Francisco Martínez‐Álvarez, et al.
The Science of The Total Environment (2019) Vol. 701, pp. 134413-134413
Closed Access | Times Cited: 331
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
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
Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier
Himan Shahabi, Ataollah Shirzadi, Kayvan Ghaderi, et al.
Remote Sensing (2020) Vol. 12, Iss. 2, pp. 266-266
Open Access | Times Cited: 301
XGBoost-based method for flash flood risk assessment
Meihong Ma, Gang Zhao, Bingshun He, et al.
Journal of Hydrology (2021) Vol. 598, pp. 126382-126382
Closed Access | Times Cited: 297
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
Tackling environmental challenges in pollution controls using artificial intelligence: A review
Zhiping Ye, Jiaqian Yang, Na Zhong, et al.
The Science of The Total Environment (2019) Vol. 699, pp. 134279-134279
Closed Access | Times Cited: 276
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
Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping
Zhice Fang, Yi Wang, Ling Peng, et al.
Computers & Geosciences (2020) Vol. 139, pp. 104470-104470
Closed Access | Times Cited: 268
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: 267
GIS-based MCDM – AHP modeling for flood susceptibility mapping of arid areas, southeastern Tunisia
Dhekra Souissi, Lahcen Zouhri, Salma Hammami, et al.
Geocarto International (2019) Vol. 35, Iss. 9, pp. 991-1017
Closed Access | Times Cited: 264
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
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
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
Predictive models for concrete properties using machine learning and deep learning approaches: A review
Mohammad Mohtasham Moein, Ashkan Saradar, Komeil Rahmati, et al.
Journal of Building Engineering (2022) Vol. 63, pp. 105444-105444
Open Access | Times Cited: 246
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
Flood Susceptibility Assessment in Bangladesh Using Machine Learning and Multi-criteria Decision Analysis
Mahfuzur Rahman, Chen Ningsheng, Md Monirul Islam, et al.
Earth Systems and Environment (2019) Vol. 3, Iss. 3, pp. 585-601
Closed Access | Times Cited: 235
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
Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques
Jian Zhou, Yingui Qiu, Danial Jahed Armaghani, et al.
Geoscience Frontiers (2020) Vol. 12, Iss. 3, pp. 101091-101091
Open Access | Times Cited: 231
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