Theo dơi
Amir Naghibi
Tiêu đề
Trích dẫn bởi
Trích dẫn bởi
GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran
SA Naghibi, HR Pourghasemi, B Dixon
Environmental Monitoring and Assessment 188 (1), 44, 2016
Assessment of the effects of training data selection on the landslide susceptibility mapping: a comparison between support vector machine (SVM), logistic regression (LR) and …
B Kalantar, B Pradhan, SA Naghibi, A Motevalli, S Mansor
Geomatics, Natural Hazards and Risk 9 (1), 49-69, 2018
Application of support vector machine, random forest, and genetic algorithm optimized random forest models in groundwater potential mapping
SA Naghibi, K Ahmadi, A Daneshi
Water Resources Management 31, 2761-2775, 2017
Groundwater qanat potential mapping using frequency ratio and Shannon’s entropy models in the Moghan watershed, Iran
SA Naghibi, HR Pourghasemi, ZS Pourtaghi, A Rezaei
Earth Science Informatics 8, 171-186, 2015
A comparative assessment between three machine learning models and their performance comparison by bivariate and multivariate statistical methods in groundwater potential mapping
SA Naghibi, HR Pourghasemi
Water Resources Management 29 (14), 5217-5236, 2015
A comparative study of landslide susceptibility maps produced using support vector machine with different kernel functions and entropy data mining models in China
W Chen, HR Pourghasemi, SA Naghibi
Bulletin of Engineering Geology and the Environment, 1-18, 2017
Groundwater potential mapping using C5. 0, random forest, and multivariate adaptive regression spline models in GIS
A Golkarian, SA Naghibi, B Kalantar, B Pradhan
Environmental monitoring and assessment 190, 1-16, 2018
A comparative assessment of GIS-based data mining models and a novel ensemble model in groundwater well potential mapping
SA Naghibi, DD Moghaddam, B Kalantar, B Pradhan, O Kisi
Journal of Hydrology 548, 471-483, 2017
A comparison between ten advanced and soft computing models for groundwater qanat potential assessment in Iran using R and GIS
SA Naghibi, HR Pourghasemi, K Abbaspour
Theoretical and applied climatology 131, 967-984, 2018
Groundwater potential mapping using a novel data-mining ensemble model
MD Kordestani, SA Naghibi, H Hashemi, K Ahmadi, B Kalantar, ...
Hydrogeology journal, 2019
Groundwater spring potential modelling: Comprising the capability and robustness of three different modeling approaches
O Rahmati, SA Naghibi, H Shahabi, DT Bui, B Pradhan, A Azareh, ...
Journal of hydrology 565, 248-261, 2018
GIS-based landslide spatial modeling in Ganzhou City, China
H Hong, SA Naghibi, HR Pourghasemi, B Pradhan
Arabian Journal of Geosciences 9, 1-26, 2016
Prioritization of landslide conditioning factors and its spatial modeling in Shangnan County, China using GIS-based data mining algorithms
W Chen, HR Pourghasemi, SA Naghibi
Bulletin of Engineering Geology and the Environment, 1-19, 2017
Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia
O Rahmati, F Falah, KS Dayal, RC Deo, F Mohammadi, T Biggs, ...
Science of the Total Environment 699, 134230, 2020
A comparative assessment between linear and quadratic discriminant analyses (LDA-QDA) with frequency ratio and weights-of-evidence models for forest fire susceptibility mapping …
H Hong, SA Naghibi, M Moradi Dashtpagerdi, HR Pourghasemi, W Chen
Arabian Journal of Geosciences 10, 1-14, 2017
Land subsidence modelling using tree-based machine learning algorithms
O Rahmati, F Falah, SA Naghibi, T Biggs, M Soltani, RC Deo, A Cerdà, ...
Science of the total environment 672, 239-252, 2019
Evaluation of four supervised learning methods for groundwater spring potential mapping in Khalkhal region (Iran) using GIS-based features
SA Naghibi, MM Dashtpagerdi
Hydrogeology journal 25 (1), 169, 2017
Inverse method using boosted regression tree and k-nearest neighbor to quantify effects of point and non-point source nitrate pollution in groundwater
A Motevalli, SA Naghibi, H Hashemi, R Berndtsson, B Pradhan, ...
Journal of cleaner production, 2019
Application of extreme gradient boosting and parallel random forest algorithms for assessing groundwater spring potential using DEM-derived factors
SA Naghibi, H Hashemi, R Berndtsson, S Lee
Journal of Hydrology 589, 125197, 2020
Development of novel hybridized models for urban flood susceptibility mapping
O Rahmati, H Darabi, M Panahi, Z Kalantari, SA Naghibi, CSS Ferreira, ...
Scientific reports 10 (1), 12937, 2020
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