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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 pattern assessment of tropical forest fire danger at Thuan Chau area (Vietnam) using GIS-based advanced machine learning algorithms: A comparative study
Nguyễn Ngọc Thạch, Dang Bao-Toan Ngo, Pham Xuan-Canh, et al.
Ecological Informatics (2018) Vol. 46, pp. 74-85
Open Access | Times Cited: 153

Showing 26-50 of 153 citing articles:

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
Artificial Intelligence Based Fire and Smoke Detection and Security Control System
Harsh Prasad, Akshit Singh, Jatin Thakur, et al.
(2023), pp. 01-06
Closed Access | Times Cited: 26
A Comprehensive Review of Geospatial Technology Applications in Earthquake Preparedness, Emergency Management, and Damage Assessment
Mahyat Shafapour Tehrany, Maryna Batur, Farzin Shabani, et al.
Remote Sensing (2023) Vol. 15, Iss. 7, pp. 1939-1939
Open Access | Times Cited: 24
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
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
Wildfire Susceptibility Mapping in Baikal Natural Territory Using Random Forest
Olga Nikolaychuk, Julia Pestova, Aleksandr Yu. Yurin
Forests (2024) Vol. 15, Iss. 1, pp. 170-170
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
Wildfire Probability Mapping: Bivariate vs. Multivariate Statistics
Abolfazl Jaafari, Davood Mafi-Gholami, Binh Thai Pham, et al.
Remote Sensing (2019) Vol. 11, Iss. 6, pp. 618-618
Open Access | Times Cited: 74
Monitoring Wildfires in the Northeastern Peruvian Amazon Using Landsat-8 and Sentinel-2 Imagery in the GEE Platform
Elgar Barboza, Efraín Y. Turpo Cayo, Cláudia Maria de Almeida, et al.
ISPRS International Journal of Geo-Information (2020) Vol. 9, Iss. 10, pp. 564-564
Open Access | Times Cited: 61
A new approach of deep neural computing for spatial prediction of wildfire danger at tropical climate areas
Van Hung Le, Duc A. Hoang, Chuyen Trung Tran, et al.
Ecological Informatics (2021) Vol. 63, pp. 101300-101300
Closed Access | Times Cited: 55
Predictive model of spatial scale of forest fire driving factors: a case study of Yunnan Province, China
Wenhui Li, Quanli Xu, Junhua Yi, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 31
Artificial neural networks for assessing forest fire susceptibility in Türkiye
Omer Kantarcioglu, Sultan Kocaman, Konrad Schindler
Ecological Informatics (2023) Vol. 75, pp. 102034-102034
Closed Access | Times Cited: 22
A New Approach Based on TensorFlow Deep Neural Networks with ADAM Optimizer and GIS for Spatial Prediction of Forest Fire Danger in Tropical Areas
Tran Xuan Truong, Viet‐Ha Nhu, Doan Thi Nam Phuong, et al.
Remote Sensing (2023) Vol. 15, Iss. 14, pp. 3458-3458
Open Access | Times Cited: 20
A Forest Fire Susceptibility Modeling Approach Based on Integration Machine Learning Algorithm
Changjiang Shi, Fuquan Zhang
Forests (2023) Vol. 14, Iss. 7, pp. 1506-1506
Open Access | Times Cited: 20
Enhancing Land Cover/Land Use (LCLU) classification through a comparative analysis of hyperparameters optimization approaches for deep neural network (DNN)
Ali Azedou, Aouatif Amine, Isaya Kisekka, et al.
Ecological Informatics (2023) Vol. 78, pp. 102333-102333
Open Access | Times Cited: 19
A Forest Fire Recognition Method Based on Modified Deep CNN Model
Shaoxiong Zheng, Xiangjun Zou, Peng Gao, et al.
Forests (2024) Vol. 15, Iss. 1, pp. 111-111
Open Access | Times Cited: 7
Dependable Fire Detection System with Multifunctional Artificial Intelligence Framework
Jun-Hong Park, Seung-Gi Lee, Seongjin Yun, et al.
Sensors (2019) Vol. 19, Iss. 9, pp. 2025-2025
Open Access | Times Cited: 54
A Modeling Comparison of Groundwater Potential Mapping in a Mountain Bedrock Aquifer: QUEST, GARP, and RF Models
Davoud Davoudi Moghaddam, Omid Rahmati, Ali Haghizadeh, et al.
Water (2020) Vol. 12, Iss. 3, pp. 679-679
Open Access | Times Cited: 46
Spatial Assessment of Wildfires Susceptibility in Santa Cruz (Bolivia) Using Random Forest
Marcela Bustillo Sánchez, Marj Tonini, Anna Mapelli, et al.
Geosciences (2021) Vol. 11, Iss. 5, pp. 224-224
Open Access | Times Cited: 41
Analysis of Forest Fire Dynamics, Distribution and Main Drivers in the Atlantic Forest
Minerva Singh, Zhuhua Huang
Sustainability (2022) Vol. 14, Iss. 2, pp. 992-992
Open Access | Times Cited: 28
USING MACHINE LEARNING COUPLED WITH REMOTE SENSING FOR FOREST FIRE SUSCEPTIBILITY MAPPING. CASE STUDY TETOUAN PROVINCE, NORTHERN MOROCCO
M. Seddouki, Mohamed Benayad, Zoya Aamir, et al.
˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences (2023) Vol. XLVIII-4/W6-2022, pp. 333-342
Open Access | Times Cited: 15
Global Wildfire Danger Predictions Based on Deep Learning Taking into Account Static and Dynamic Variables
Yuheng Ji, Dan Wang, Qingliang Li, et al.
Forests (2024) Vol. 15, Iss. 1, pp. 216-216
Open Access | Times Cited: 6
Predicting the pulse of the Amazon: Machine learning insights into deforestation dynamics
Fernanda Dias, Nicolas Suhadolnik, Heloisa A. Camargo, et al.
Journal of Environmental Management (2024) Vol. 362, pp. 121359-121359
Closed Access | Times Cited: 5
RVFR: Random vector forest regression model for integrated & enhanced approach in forest fires predictions
Robin Singh Bhadoria, Manish Kumar Pandey, Pradeep Kundu
Ecological Informatics (2021) Vol. 66, pp. 101471-101471
Closed Access | Times Cited: 30

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