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:

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: 243

Showing 1-25 of 243 citing articles:

Selecting critical features for data classification based on machine learning methods
Rung-Ching Chen, Christine Dewi, Su-Wen Huang, et al.
Journal Of Big Data (2020) Vol. 7, Iss. 1
Open Access | Times Cited: 699
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: 422
Digital Transformation and Environmental Sustainability: A Review and Research Agenda
A. Karim Feroz, Hangjung Zo, Ananth Chiravuri
Sustainability (2021) Vol. 13, Iss. 3, pp. 1530-1530
Open Access | Times Cited: 421
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: 419
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: 385
Comparing the prediction performance of a Deep Learning Neural Network model with conventional machine learning models in landslide susceptibility assessment
Dieu Tien Bui, Paraskevas Tsangaratos, Viet-Tien Nguyen, et al.
CATENA (2020) Vol. 188, pp. 104426-104426
Closed Access | Times Cited: 379
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: 297
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: 265
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: 263
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: 253
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: 232
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: 220
Machine Learning in Environmental Research: Common Pitfalls and Best Practices
Jun‐Jie Zhu, Meiqi Yang, Zhiyong Jason Ren
Environmental Science & Technology (2023) Vol. 57, Iss. 46, pp. 17671-17689
Closed Access | Times Cited: 219
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: 217
Shallow Landslide Susceptibility Mapping: A Comparison between Logistic Model Tree, Logistic Regression, Naïve Bayes Tree, Artificial Neural Network, and Support Vector Machine Algorithms
Viet‐Ha Nhu, Ataollah Shirzadi, Himan Shahabi, et al.
International Journal of Environmental Research and Public Health (2020) Vol. 17, Iss. 8, pp. 2749-2749
Open Access | Times Cited: 217
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: 203
Predicting flood susceptibility using LSTM neural networks
Zhice Fang, Yi Wang, Ling Peng, et al.
Journal of Hydrology (2020) Vol. 594, pp. 125734-125734
Open Access | Times Cited: 200
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: 195
Development of advanced artificial intelligence models for daily rainfall prediction
Binh Thai Pham, Lei Lü, Tien-Thinh Le, et al.
Atmospheric Research (2020) Vol. 237, pp. 104845-104845
Closed Access | Times Cited: 194
Flash Flood Susceptibility Modeling Using New Approaches of Hybrid and Ensemble Tree-Based Machine Learning Algorithms
Shahab S. Band, Saeid Janizadeh, Subodh Chandra Pal, et al.
Remote Sensing (2020) Vol. 12, Iss. 21, pp. 3568-3568
Open Access | Times Cited: 188
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: 176
Soft Computing Ensemble Models Based on Logistic Regression for Groundwater Potential Mapping
Phong Tung Nguyen, Duong Hai Ha, Mohammadtaghi Avand, et al.
Applied Sciences (2020) Vol. 10, Iss. 7, pp. 2469-2469
Open Access | Times Cited: 154
Development of artificial intelligence models for the prediction of Compression Coefficient of soil: An application of Monte Carlo sensitivity analysis
Binh Thai Pham, Manh Duc Nguyen, Dong Van Dao, et al.
The Science of The Total Environment (2019) Vol. 679, pp. 172-184
Closed Access | Times Cited: 147
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: 146
Is digitalization a driver to enhance environmental performance? An empirical investigation of European countries
Lê Thanh Hà, Trần Thị Lan Hương, To Trung Thanh
Sustainable Production and Consumption (2022) Vol. 32, pp. 230-247
Closed Access | Times Cited: 125
Page 1 - Next Page

Scroll to top