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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 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

Showing 1-25 of 113 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
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: 208
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
Landslide susceptibility mapping using multiscale sampling strategy and convolutional neural network: A case study in Jiuzhaigou region
Yaning Yi, Zhijie Zhang, Wanchang Zhang, et al.
CATENA (2020) Vol. 195, pp. 104851-104851
Closed 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
Assessment of long and short-term flood risk using the multi-criteria analysis model with the AHP-Entropy method in Poyang Lake basin
Jinru Wu, Xiaoling Chen, Jianzhong Lu
International Journal of Disaster Risk Reduction (2022) Vol. 75, pp. 102968-102968
Closed Access | Times Cited: 129
Comprehensive Overview of Flood Modeling Approaches: A Review of Recent Advances
Vijendra Kumar, Kul Vaibhav Sharma, Tommaso Caloiero, et al.
Hydrology (2023) Vol. 10, Iss. 7, pp. 141-141
Open Access | Times Cited: 110
Multi-hazard susceptibility mapping based on Convolutional Neural Networks
Kashif Ullah, Yi Wang, Zhice Fang, et al.
Geoscience Frontiers (2022) Vol. 13, Iss. 5, pp. 101425-101425
Open Access | Times Cited: 91
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
Spatial characteristics and driving factors of urban flooding in Chinese megacities
Yongheng Wang, Chunlin Li, Miao Liu, et al.
Journal of Hydrology (2022) Vol. 613, pp. 128464-128464
Closed Access | Times Cited: 77
A comparative assessment of flood susceptibility modelling of GIS-based TOPSIS, VIKOR, and EDAS techniques in the Sub-Himalayan foothills region of Eastern India
Rajib Mitra, Jayanta Das
Environmental Science and Pollution Research (2022) Vol. 30, Iss. 6, pp. 16036-16067
Closed Access | Times Cited: 77
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
A novel framework for urban flood risk assessment: Multiple perspectives and causal analysis
Yongheng Wang, Qingtao Zhang, Kairong Lin, et al.
Water Research (2024) Vol. 256, pp. 121591-121591
Closed Access | Times Cited: 18
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
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
Examining LightGBM and CatBoost models for wadi flash flood susceptibility prediction
Mohamed Saber, Tayeb Boulmaiz, Mawloud Guermoui, et al.
Geocarto International (2021) Vol. 37, Iss. 25, pp. 7462-7487
Open Access | Times Cited: 102
Exploring machine learning potential for climate change risk assessment
Federica Zennaro, Elisa Furlan, Christian Simeoni, et al.
Earth-Science Reviews (2021) Vol. 220, pp. 103752-103752
Closed Access | Times Cited: 82
Spatial Prediction of Future Flood Risk: An Approach to the Effects of Climate Change
Mohammadtaghi Avand, Hamid Reza Moradi, Mehdi Ramazanzadeh lasboyee
Geosciences (2021) Vol. 11, Iss. 1, pp. 25-25
Open Access | Times Cited: 65
Machine Learning Algorithms for Modeling and Mapping of Groundwater Pollution Risk: A Study to Reach Water Security and Sustainable Development (Sdg) Goals in a Mediterranean Aquifer System
Safae Ijlil, Ali Essahlaoui, Meriame Mohajane, et al.
Remote Sensing (2022) Vol. 14, Iss. 10, pp. 2379-2379
Open Access | Times Cited: 41
A novel approach for assessing flood risk with machine learning and multi-criteria decision-making methods
Sharareh Rashidi Shikhteymour, Moslem Borji, Mehdi Bagheri‐Gavkosh, et al.
Applied Geography (2023) Vol. 158, pp. 103035-103035
Closed Access | Times Cited: 29
Novel integrated modelling based on multiplicative long short-term memory (mLSTM) deep learning model and ensemble multi-criteria decision making (MCDM) models for mapping flood risk
Aliakbar Mohammadifar, Hamid Gholami, Shahram Golzari
Journal of Environmental Management (2023) Vol. 345, pp. 118838-118838
Closed Access | Times Cited: 28
Application of bivariate approaches for flood susceptibility mapping: a district level study in Eastern India
Madhurima Dutta, Sunil Saha, Nur Islam Saikh, et al.
HydroResearch (2023) Vol. 6, pp. 108-121
Open Access | Times Cited: 27
Nitrate concentrations tracking from multi-aquifer groundwater vulnerability zones: Insight from machine learning and spatial mapping
Sani I. Abba, Mohamed A. Yassin, Mahmud M. Jibril, et al.
Process Safety and Environmental Protection (2024) Vol. 184, pp. 1143-1157
Closed Access | Times Cited: 16
Urban flood susceptibility mapping using remote sensing, social sensing and an ensemble machine learning model
Xiaotong Zhu, Hongwei Guo, Jinhui Jeanne Huang‬‬‬‬
Sustainable Cities and Society (2024) Vol. 108, pp. 105508-105508
Closed Access | Times Cited: 16
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