Cikkek nyilvánosan hozzáférhető megbízással - Omid GhorbanzadehTovábbi információ
Sehol sem hozzáférhető: 1
Flood susceptibility mapping with machine learning, multi-criteria decision analysis and ensemble using Dempster Shafer Theory
TG Nachappa, ST Piralilou, K Gholamnia, O Ghorbanzadeh, O Rahmati, ...
Journal of hydrology 590, 125275, 2020
Megbízások: Austrian Science Fund
Valahol hozzáférhető: 41
Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection
O Ghorbanzadeh, T Blaschke, K Gholamnia, SR Meena, D Tiede, J Aryal
Remote Sensing 11 (2), 196, 2019
Megbízások: Austrian Science Fund
Forest fire susceptibility and risk mapping using social/infrastructural vulnerability and environmental variables
O Ghorbanzadeh, T Blaschke, K Gholamnia, J Aryal
Fire 2 (3), 50, 2019
Megbízások: Austrian Science Fund
Landslide detection using multi-scale image segmentation and different machine learning models in the higher himalayas
S Tavakkoli Piralilou, H Shahabi, B Jarihani, O Ghorbanzadeh, ...
Remote Sensing 11 (21), 2575, 2019
Megbízások: Austrian Science Fund
Sustainable urban transport planning considering different stakeholder groups by an interval-AHP decision support model
O Ghorbanzadeh, S Moslem, T Blaschke, S Duleba
Sustainability 11 (1), 9, 2018
Megbízások: Austrian Science Fund
Analysing stakeholder consensus for a sustainable transport development decision by the fuzzy AHP and interval AHP
S Moslem, O Ghorbanzadeh, T Blaschke, S Duleba
Sustainability 11 (12), 3271, 2019
Megbízások: Austrian Science Fund
Spatial prediction of wildfire susceptibility using field survey GPS data and machine learning approaches
O Ghorbanzadeh, K Valizadeh Kamran, T Blaschke, J Aryal, A Naboureh, ...
Fire 2 (3), 43, 2019
Megbízások: Austrian Science Fund
Flood susceptibility mapping using an improved analytic network process with statistical models
P Yariyan, M Avand, RA Abbaspour, A Torabi Haghighi, R Costache, ...
Geomatics, Natural Hazards and Risk 11 (1), 2282-2314, 2020
Megbízások: Austrian Science Fund
A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold crossvalidation approach for land subsidence susceptibility mapping
O Ghorbanzadeh, H Rostamzadeh, T Blaschke, K Gholaminia, J Aryal
Natural Hazards, 2018
Megbízások: Austrian Science Fund
A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping
O Ghorbanzadeh, T Blaschke, J Aryal, K Gholaminia
Journal of Spatial Science 65 (3), 401-418, 2020
Megbízások: Austrian Science Fund
Comparisons of diverse machine learning approaches for wildfire susceptibility mapping
K Gholamnia, T Gudiyangada Nachappa, O Ghorbanzadeh, T Blaschke
Symmetry 12 (4), 604, 2020
Megbízások: Austrian Science Fund
UAV-based slope failure detection using deep-learning convolutional neural networks
O Ghorbanzadeh, SR Meena, T Blaschke, J Aryal
Remote Sensing 11 (17), 2046, 2019
Megbízások: Austrian Science Fund
A comprehensive transferability evaluation of U-Net and ResU-Net for landslide detection from Sentinel-2 data (case study areas from Taiwan, China, and Japan)
O Ghorbanzadeh, A Crivellari, P Ghamisi, H Shahabi, T Blaschke
Scientific Reports 11 (1), 14629, 2021
Megbízások: Austrian Science Fund
An integrated approach of best-worst method (BWM) and triangular fuzzy sets for evaluating driver behavior factors related to road safety
S Moslem, M Gul, D Farooq, E Celik, O Ghorbanzadeh, T Blaschke
Mathematics 8 (3), 414, 2020
Megbízások: Austrian Science Fund
Application of the AHP-BWM model for evaluating driver behavior factors related to road safety: A case study for Budapest
S Moslem, D Farooq, O Ghorbanzadeh, T Blaschke
Symmetry 12 (2), 243, 2020
Megbízások: Austrian Science Fund
A comparative study of statistics-based landslide susceptibility models: A case study of the region affected by the gorkha earthquake in nepal
SR Meena, O Ghorbanzadeh, T Blaschke
ISPRS international journal of geo-information 8 (2), 94, 2019
Megbízások: Austrian Science Fund
An interval matrix method used to optimize the decision matrix in AHP technique for land subsidence susceptibility mapping
O Ghorbanzadeh, B Feizizadeh, T Blaschke
Environmental Earth Sciences 77 (16), 584, 2018
Megbízások: Austrian Science Fund
A semi-automated object-based gully networks detection using different machine learning models: a case study of Bowen catchment, Queensland, Australia
H Shahabi, B Jarihani, S Tavakkoli Piralilou, D Chittleborough, M Avand, ...
Sensors 19 (22), 4893, 2019
Megbízások: Austrian Science Fund
Rapid mapping of landslides in the Western Ghats (India) triggered by 2018 extreme monsoon rainfall using a deep learning approach
SR Meena, O Ghorbanzadeh, CJ van Westen, TG Nachappa, T Blaschke, ...
Landslides 18, 1937-1950, 2021
Megbízások: Austrian Science Fund
Multi-hazard exposure mapping using machine learning for the State of Salzburg, Austria
TG Nachappa, O Ghorbanzadeh, K Gholamnia, T Blaschke
Remote Sensing 12 (17), 2757, 2020
Megbízások: Austrian Science Fund
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