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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:

Machine learning based wildfire susceptibility mapping using remotely sensed fire data and GIS: A case study of Adana and Mersin provinces, Turkey
Muzaffer Can İban, Aliihsan Şekertekin
Ecological Informatics (2022) Vol. 69, pp. 101647-101647
Closed Access | Times Cited: 112

Showing 1-25 of 112 citing articles:

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
Explainable artificial intelligence (XAI) for interpreting the contributing factors feed into the wildfire susceptibility prediction model
Abolfazl Abdollahi, Biswajeet Pradhan
The Science of The Total Environment (2023) Vol. 879, pp. 163004-163004
Open Access | Times Cited: 87
A Brief Review of Machine Learning Algorithms in Forest Fires Science
Ramez Alkhatib, Wahib Sahwan, Anas Alkhatieb, et al.
Applied Sciences (2023) Vol. 13, Iss. 14, pp. 8275-8275
Open Access | Times Cited: 65
Explainable artificial intelligence in disaster risk management: Achievements and prospective futures
Saman Ghaffarian, Firouzeh Taghikhah, Holger R. Maier
International Journal of Disaster Risk Reduction (2023) Vol. 98, pp. 104123-104123
Open Access | Times Cited: 61
Machine learning based forest fire susceptibility assessment of Manavgat district (Antalya), Turkey
Hazan Alkan Akıncı, Halil Akıncı
Earth Science Informatics (2023) Vol. 16, Iss. 1, pp. 397-414
Closed Access | Times Cited: 53
Assessing Chilgoza Pine (Pinus gerardiana) forest fire severity: Remote sensing analysis, correlations, and predictive modeling for enhanced management strategies
Kaleem Mehmood, Shoaib Ahmad Anees, Mi Luo, et al.
Trees Forests and People (2024) Vol. 16, pp. 100521-100521
Open Access | Times Cited: 38
Integrating geospatial, remote sensing, and machine learning for climate-induced forest fire susceptibility mapping in Similipal Tiger Reserve, India
Chiranjit Singha, Kishore Chandra Swain, Armin Moghimi, et al.
Forest Ecology and Management (2024) Vol. 555, pp. 121729-121729
Open Access | Times Cited: 27
Spatial analysis and machine learning prediction of forest fire susceptibility: a comprehensive approach for effective management and mitigation
Manoranjan Mishra, Rajkumar Guria, Biswaranjan Baraj, et al.
The Science of The Total Environment (2024) Vol. 926, pp. 171713-171713
Closed Access | Times Cited: 26
Exploring forest fire susceptibility and management strategies in Western Himalaya: Integrating ensemble machine learning and explainable AI for accurate prediction and comprehensive analysis
Hoang Thi Hang, Javed Mallick, Saeed Alqadhi, et al.
Environmental Technology & Innovation (2024) Vol. 35, pp. 103655-103655
Open Access | Times Cited: 20
Assessing habitat selection parameters of Arabica coffee using BWM and BCM methods based on GIS
Xiaogang Liu, Yuting Tan, Jianhua Dong, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access | Times Cited: 2
An explainable model for the mass appraisal of residences: The application of tree-based Machine Learning algorithms and interpretation of value determinants
Muzaffer Can İban
Habitat International (2022) Vol. 128, pp. 102660-102660
Closed Access | Times Cited: 55
Seasonal differences in the spatial patterns of wildfire drivers and susceptibility in the southwest mountains of China
Wang Wen-quan, Fengjun Zhao, Yanxia Wang, et al.
The Science of The Total Environment (2023) Vol. 869, pp. 161782-161782
Closed Access | Times Cited: 35
Snow avalanche susceptibility mapping using novel tree-based machine learning algorithms (XGBoost, NGBoost, and LightGBM) with eXplainable Artificial Intelligence (XAI) approach
Muzaffer Can İban, Süleyman Sefa Bilgilioğlu
Stochastic Environmental Research and Risk Assessment (2023) Vol. 37, Iss. 6, pp. 2243-2270
Closed Access | Times Cited: 34
Assessment of Wildfire Susceptibility and Wildfire Threats to Ecological Environment and Urban Development Based on GIS and Multi-Source Data: A Case Study of Guilin, China
Weiting Yue, Chao Ren, Yueji Liang, et al.
Remote Sensing (2023) Vol. 15, Iss. 10, pp. 2659-2659
Open Access | Times Cited: 27
Forest-Fire-Risk Prediction Based on Random Forest and Backpropagation Neural Network of Heihe Area in Heilongjiang Province, China
Chao Gao, Honglei Lin, Haiqing Hu
Forests (2023) Vol. 14, Iss. 2, pp. 170-170
Open Access | Times Cited: 25
FirePred: A hybrid multi-temporal convolutional neural network model for wildfire spread prediction
Mohammad Marjani, Seyed Ali Ahmadi, Masoud Mahdianpari
Ecological Informatics (2023) Vol. 78, pp. 102282-102282
Closed Access | Times Cited: 25
Forest fire risk mapping with Landsat 8 OLI images: Evaluation of the potential use of vegetation indices
Fatih Sivrikaya, Alkan Günlü, Ömer Küçük, et al.
Ecological Informatics (2024) Vol. 79, pp. 102461-102461
Open Access | Times Cited: 16
Comparison of tree-based ensemble learning algorithms for landslide susceptibility mapping in Murgul (Artvin), Turkey
Ziya Usta, Halil Akıncı, Alper Tunga Akın
Earth Science Informatics (2024) Vol. 17, Iss. 2, pp. 1459-1481
Open Access | Times Cited: 16
Ensembling machine learning models to identify forest fire-susceptible zones in Northeast India
Mriganka Shekhar Sarkar, Bishal Kumar Majhi, Bhawna Pathak, et al.
Ecological Informatics (2024) Vol. 81, pp. 102598-102598
Open Access | Times Cited: 16
SegNet: A segmented deep learning based Convolutional Neural Network approach for drones wildfire detection
Aditya Jonnalagadda, Hashim A. Hashim
Remote Sensing Applications Society and Environment (2024) Vol. 34, pp. 101181-101181
Open Access | Times Cited: 12
Enhancing Flood Risk Analysis in Harris County: Integrating Flood Susceptibility and Social Vulnerability Mapping
Hemal Dey, Wanyun Shao, Md. Munjurul Haque, et al.
Journal of Geovisualization and Spatial Analysis (2024) Vol. 8, Iss. 1
Closed Access | Times Cited: 11
Comparison of diverse machine learning algorithms for forest fire susceptibility mapping in Antalya, Türkiye
Hazan Alkan Akıncı, Halil Akıncı, Mustafa Zeybek
Advances in Space Research (2024) Vol. 74, Iss. 2, pp. 647-667
Closed Access | Times Cited: 10
A SHAP-Enhanced XGBoost Model for Interpretable Prediction of Coseismic Landslides
Haijia Wen, Bo Liu, Mingrui Di, et al.
Advances in Space Research (2024) Vol. 74, Iss. 8, pp. 3826-3854
Closed Access | Times Cited: 9
SHAP-Driven Explainable Artificial Intelligence Framework for Wildfire Susceptibility Mapping Using MODIS Active Fire Pixels: An In-Depth Interpretation of Contributing Factors in Izmir, Türkiye
Muzaffer Can İban, Oktay Aksu
Remote Sensing (2024) Vol. 16, Iss. 15, pp. 2842-2842
Open Access | Times Cited: 9
Modeling of Forest Fire Risk Areas of Amazonas Department, Peru: Comparative Evaluation of Three Machine Learning Methods
Alex J. Vergara, Sivmny V. Valqui-Reina, Dennis Cieza-Tarrillo, et al.
Forests (2025) Vol. 16, Iss. 2, pp. 273-273
Open Access | Times Cited: 1
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