G
Gholamabbas Ghanbarian
Researcher at Shiraz University
Publications - 24
Citations - 460
Gholamabbas Ghanbarian is an academic researcher from Shiraz University. The author has contributed to research in topics: Essential oil & Pulegone. The author has an hindex of 6, co-authored 23 publications receiving 270 citations. Previous affiliations of Gholamabbas Ghanbarian include Virginia Tech College of Natural Resources and Environment.
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Assessment of the importance of gully erosion effective factors using Boruta algorithm and its spatial modeling and mapping using three machine learning algorithms
TL;DR: In this article, the importance of effective factors in the occurrence of gully erosion using Boruta algorithm was considered and three factors including land use, distance from river, and clay percent had the most noticeable importance.
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Prioritization of effective factors in the occurrence of land subsidence and its susceptibility mapping using an SVM model and their different kernel functions
TL;DR: In this paper, the authors attempted to map land subsidence susceptibility using a support vector machine (SVM) model and their different kernel functions in Kerman province, Iran and obtained the highest accuracy with AUC values of 0.894 to 0.857.
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Spatial modeling, risk mapping, change detection, and outbreak trend analysis of coronavirus (COVID-19) in Iran (days between February 19 and June 14, 2020).
Hamid Reza Pourghasemi,Soheila Pouyan,Bahram Heidari,Zakariya Farajzadeh,Seyed Rashid Fallah Shamsi,Sedigheh Babaei,Rasoul Khosravi,Mohammad Etemadi,Gholamabbas Ghanbarian,Ahmad Farhadi,Roja Safaeian,Zahra Heidari,Mohammad Hassan Tarazkar,John P. Tiefenbacher,Amir Azmi,Faezeh Sadeghian +15 more
TL;DR: The first comprehensive study of COVID-19 in Iran undertakes spatial modeling, risk mapping, change detection, and outbreak trend analysis of the disease spread, indicating that the risk maps provided by this study is the primary, fundamental step for managing and controlling the epidemic.
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Maxent Data Mining Technique and Its Comparison with a Bivariate Statistical Model for Predicting the Potential Distribution of Astragalus Fasciculifolius Boiss. in Fars, Iran
TL;DR: In this article, the spatial potential distribution of Astragalus fasciculifolius Boiss was mapped using maximum entropy (Maxent) as data mining technique and bivariate statistical model (FR: frequency ratio) in marl soils of southern Zagros, Iran.
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Prediction of habitat suitability of Morina persica L. species using artificial intelligence techniques
TL;DR: In this article, the authors used four data mining models: maximum entropy (MaxEnt), support vector machine (SVM), generalized linear model (GLM), and boosted regression trees (BRT) to predict the spatial distribution and model the habitat suitability for M. persica.