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:

Artificial Neural Network Models: An Alternative Approach for Reliable Aboveground Pine Tree Biomass Prediction
Ramazan Özçelík, Maria J. Diamantopoulou, Mehmet Eker, et al.
Forest Science (2017) Vol. 63, Iss. 3, pp. 291-302
Open Access | Times Cited: 33

Showing 1-25 of 33 citing articles:

An Evaluation of Eight Machine Learning Regression Algorithms for Forest Aboveground Biomass Estimation from Multiple Satellite Data Products
Yuzhen Zhang, Jun Ma, Shunlin Liang, et al.
Remote Sensing (2020) Vol. 12, Iss. 24, pp. 4015-4015
Open Access | Times Cited: 107
Artificial Intelligence for Biomass Detection, Production and Energy Usage in Rural Areas: A review of Technologies and Applications
Zhan Shi, Giovanni Ferrari, Ping Ai, et al.
Sustainable Energy Technologies and Assessments (2023) Vol. 60, pp. 103548-103548
Open Access | Times Cited: 17
Removal of methylene blue (aq) using untreated and acid‐treated eucalyptus leaves and GA‐ANN modelling
Koushik Ghosh, Nirjhar Bar, Asit Baran Biswas, et al.
The Canadian Journal of Chemical Engineering (2019) Vol. 97, Iss. 11, pp. 2883-2898
Closed Access | Times Cited: 49
Innovative deep learning artificial intelligence applications for predicting relationships between individual tree height and diameter at breast height
İlker Ercanlı
Forest Ecosystems (2020) Vol. 7, Iss. 1
Open Access | Times Cited: 41
Prediction of tree crown width in natural mixed forests using deep learning algorithm
Yangping Qin, Biyun Wu, Xiangdong Lei, et al.
Forest Ecosystems (2023) Vol. 10, pp. 100109-100109
Open Access | Times Cited: 16
Modelling height-diameter relationships in complex tropical rain forest ecosystems using deep learning algorithm
Friday Nwabueze Ogana, İlker Ercanlı
Journal of Forestry Research (2021) Vol. 33, Iss. 3, pp. 883-898
Open Access | Times Cited: 32
Development of Estimation Models for Individual Tree Aboveground Biomass Based on TLS-Derived Parameters
Fan Wang, Yuman Sun, Weiwei Jia, et al.
Forests (2023) Vol. 14, Iss. 2, pp. 351-351
Open Access | Times Cited: 12
Employing artificial neural network for effective biomass prediction: An alternative approach
Şükrü Teoman Güner, Maria J. Diamantopoulou, Krishna P. Poudel, et al.
Computers and Electronics in Agriculture (2021) Vol. 192, pp. 106596-106596
Closed Access | Times Cited: 25
Evaluation of statistical and machine learning models using satellite data to estimate aboveground biomass: A study in Vietnam Tropical Forests
Thuy Phuong Nguyen, Phuc Khoa Nguyen, Huu Ngu Nguyen, et al.
Forest Science and Technology (2024), pp. 1-13
Open Access | Times Cited: 3
Artificial neural network models predicting the leaf area index: a case study in pure even-aged Crimean pine forests from Turkey
İlker Ercanlı, Alkan Günlü, Muammer Şenyurt, et al.
Forest Ecosystems (2018) Vol. 5, Iss. 1
Open Access | Times Cited: 30
Artificial intelligence as an alternative modelling strategy for reliable height-diameter predictions of mixed-oaks species
Maria J. Diamantopoulou, RAMAZAN ÖZÇELİK, BURAK KOPARAN, et al.
TURKISH JOURNAL OF AGRICULTURE AND FORESTRY (2023) Vol. 47, Iss. 2, pp. 228-241
Open Access | Times Cited: 9
Evaluation of potential modeling approaches for Scots pine stem diameter prediction in north-eastern Turkey
Ramazan Özçelík, Maria J. Diamantopoulou, Guillermo Trincado
Computers and Electronics in Agriculture (2019) Vol. 162, pp. 773-782
Closed Access | Times Cited: 22
A novel method for approaching the compatibility of tree biomass estimation by multi-task neural networks
Qigang Xu, Xiangdong Lei, Huiru Zhang
Forest Ecology and Management (2022) Vol. 508, pp. 120011-120011
Closed Access | Times Cited: 10
Aboveground Biomass Equations for Small Trees of Brutian Pine in Turkey to Facilitate Harvesting and Management
Mehmet Eker, Krishna P. Poudel, Ramazan Özçelík
Forests (2017) Vol. 8, Iss. 12, pp. 477-477
Open Access | Times Cited: 18
Estimating aboveground stand carbon by combining Sentinel-1 and Sentinel-2 satellite data: a case study from Turkey
Sedat Keleş, Alkan Günlü, İlker Ercanlı
Elsevier eBooks (2021), pp. 117-126
Closed Access | Times Cited: 13
A hybrid of response surface methodology and artificial neural network in optimization of culture conditions of mycelia growth of Antrodia cinnamomea
Meng-Hsin Lee, Wei-Bin Lu, Mei‐Kuang Lu, et al.
Biomass and Bioenergy (2022) Vol. 158, pp. 106349-106349
Closed Access | Times Cited: 9
Recovering energy biomass from sustainable forestry using local labor resources
Mehmet Eker, Raffaele Spinelli, Nevzat Gürlevık
Journal of Cleaner Production (2017) Vol. 157, pp. 57-64
Closed Access | Times Cited: 16
Artificial intelligence with deep learning algorithms to model relationships between total tree height and diameter at breast height
İlker Ercanlı
Forest Systems (2020) Vol. 29, Iss. 2, pp. e013-e013
Open Access | Times Cited: 11
Artificial neural network models for predicting relationships between diameter at breast height and stump diameter: Crimean pine stands at ÇAKÜ Forest
Muammer Şenyurt, İlker Ercanlı, Alkan Günlü, et al.
Bosque (Valdivia) (2020) Vol. 41, Iss. 1, pp. 25-34
Open Access | Times Cited: 6
Tree Biomass Modeling Based on the Exploration of Regression and Artificial Neural Networks Approaches
Şerife KALKANLI, Maria J. Diamantopoulou, Ramazan Özçelík
Forests (2023) Vol. 14, Iss. 12, pp. 2429-2429
Open Access | Times Cited: 2
Alternatives to Growth and Yield Prognosis for Pinus caribaea var. caribaea Barrett & Golfari
Ouorou Ganni Mariel Guera, José Antônio Aleixo da Silva, Rinaldo Luíz Caraciolo Ferreira, et al.
Floresta e Ambiente (2019) Vol. 26, Iss. 4
Open Access | Times Cited: 4
Estimation of Aboveground Stand Carbon using Landsat 8 OLI Satellite Image: A Case Study from Turkey
Alkan Günlü, Sedat Keleş, İlker Ercanlı, et al.
Environmental science and engineering (2020), pp. 385-403
Closed Access | Times Cited: 4
A novel perspective for parameter estimation of seemingly unrelated nonlinear regression
Özlem Türkşen
Journal of Applied Statistics (2021) Vol. 48, Iss. 13-15, pp. 2326-2347
Open Access | Times Cited: 4
Total tree height predictions via parametric and artificial neural network modeling approaches
Yasin Karatepe, MJ Diamantopoulou, Ramazan Özçelík, et al.
iForest - Biogeosciences and Forestry (2022) Vol. 15, Iss. 2, pp. 95-105
Open Access | Times Cited: 3
Trunk volume prediction of individual Populus euphratica trees based on point clouds analysis
Yunmei Huang, Songlin Hou, Hongbo Ling, et al.
Ecological Indicators (2018) Vol. 95, pp. 964-971
Closed Access | Times Cited: 3
Page 1 - Next Page

Scroll to top