Items where Author is "Aryal, J"

Feizizadeh, B, Roodposhti, MS, Blaschke, T and Aryal, J

2017 , 'Comparing GIS-based support vector machine kernel functions for landslide susceptibility mapping' , Arabian Journal of Geosciences, vol. 10, no. 5 , pp. 1-13 , doi: 10.1007/s12517-017-2918-z.

Garg, S

, Aryal, J

, Wang, H, Shah, T, Kecskemeti, G and Ranjan, R 2018 , 'Cloud computing based bushfire prediction for cyber-physical emergency applications' , Future Generation Computer Systems, vol. 79 , pp. 354-363 , doi: 10.1016/j.future.2017.02.009.

Ghorbanzadeh, O, Blaschke, T, Aryal, J

and Gholaminia, K 2018 , 'A new GIS-based technique using an adaptive neuro-fuzzy inference system for land subsidence susceptibility mapping' , Journal of Spatial Science , pp. 1-18 , doi: 10.1080/14498596.2018.1505564.

Ghorbanzadeh, O, Blaschke, T, Gholamnia, K and Aryal, J

2019 , 'Forest fire susceptibility and risk mapping using social/infrastructural vulnerability and environmental variables' , Fire, vol. 2, no. 3 , pp. 1-27 , doi: 10.3390/fire2030050.

Ghorbanzadeh, O, Blaschke, T, Gholamnia, K, Meena, SR, Tiede, D and Aryal, J

2019 , 'Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection' , Remote Sensing, vol. 11, no. 2 , pp. 1-21 , doi: 10.3390/rs11020196.

Ghorbanzadeh, O, Meena, SR, Blaschke, T and Aryal, J

2019 , 'UAV-based slope failure detection using deep-learning convolutional neural networks' , Remote Sensing, vol. 11, no. 17 , pp. 1-24 , doi: 10.3390/rs11172046.

Ghorbanzadeh, O, Rostamzadeh, H, Blaschke, T, Gholaminia, K and Aryal, J

2018 , 'A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping' , Natural Hazards , pp. 1-21 , doi: 10.1007/s11069-018-3449-y.

Ghorbanzadeh, O, Valizadeh Kamran, K, Blaschke, T, Aryal, J

, Naboureh, A, Einali, J and Bian, J 2019 , 'Spatial prediction of wildfire susceptibility using field survey GPS data and machine learning approaches' , Fire, vol. 2, no. 3 , pp. 1-23 , doi: 10.3390/fire2030043.

Hardy, A

, Aryal, J

and Wells, MP 2019 , 'Comparing techniques for tracking: the case of Tourism Tracer in Tasmania, Australia' , E-review of Tourism Research, vol. 16, no. 2/3 , pp. 84-94 .

Hardy, A

, Hyslop, S, Booth, K

, Robards, B

, Aryal, J

, Gretzel, U and Eccleston, R

2017 , 'Tracking tourists’ travel with smartphone-based GPS technology: a methodological discussion' , Information Technology & Tourism, vol. 17, no. 3 , pp. 255-274 , doi: 10.1007/s40558-017-0086-3.

Lay, SU, Pradhan, B, Yusoff, ZBM, Abdallah, AFB, Aryal, J

and Park, H-J 2019 , 'Data mining and statistical approaches in debris-flow susceptibility modelling using airborne LiDAR data' , Sensors, vol. 19, no. 16 , pp. 1-32 , doi: 10.3390/s19163451.

McKercher, B, Hardy, A

and Aryal, J

2019 , 'Using tracking technology to improve marketing: insights from a historic town in Tasmania, Australia' , Journal of Travel and Tourism Marketing, no. 22 Feb , pp. 1-12 , doi: 10.1080/10548408.2019.1580243.

Melville, B

, Lucieer, A

and Aryal, J

2018 , 'Assessing the impact of spectral resolution on classification of lowland native grassland communities based on field spectroscopy in Tasmania, Australia' , Remote Sensing, vol. 10, no. 2 , pp. 1-19 , doi: 10.3390/rs10020308.

Melville, B

, Lucieer, A

and Aryal, J

2019 , 'Classification of lowland native grassland communities using hyperspectral Unmanned Aircraft System (UAS) Imagery in the Tasmanian midlands' , Drones, vol. 3, no. 1 , pp. 1-12 , doi: 10.3390/drones3010005.

Melville, B

, Lucieer, A

and Aryal, J

2018 , 'Object-based random forest classification of Landsat ETM+ and WorldView2 satellite imagery for mapping lowland native grassland communities in Tasmania, Australia' , International Journal of Applied Earth Observation and Geoinformation, vol. 66 , pp. 46-55 , doi: 10.1016/j.jag.2017.11.006 Rece.

Oloo, F, Safi, K and Aryal, J

2018 , 'Predicting migratory corridors of white storks, Ciconia ciconia, to enhance sustainable wind energy planning: a data-driven agent-based model' , Sustainability, vol. 10, no. 5 , pp. 1-22 , doi: 10.3390/su10051470.

Ozaki, M, Aryal, J

and Fox-Hughes, P 2019 , 'Dynamic wildfire navigation system' , International Journal of Geo-Information, vol. 8, no. 4 , pp. 1-21 , doi: 10.3390/ijgi8040194.

Rajbhandari, S, Aryal, J

, Osborn, J

, Lucieer, A

and Musk, R 2019 , 'Leveraging machine learning to extend Ontology-Driven Geographic Object-Based Image Analysis (O-GEOBIA): a case study in forest-type mapping' , Remote Sensing, vol. 11, no. 5 , pp. 1-25 , doi: 10.3390/rs11050503.

Rajbhandari, S, Aryal, J

, Osborn, J

, Musk, R and Lucieer, A

2017 , 'Benchmarking the applicability of ontology in geographic object-based image analysis' , ISPRS International Journal of Geo-Information, vol. 6 , pp. 1-24 , doi: 10.3390/ijgi6120386.

Roodposhti, MS, Aryal, J

and Bryan, BA 2018 , 'A novel algorithm for calculating transition potential in cellular automata models of land-use/cover change' , Environmental Modelling and Software , doi: 10.1016/j.envsoft.2018.10.006.

Roodposhti, MS, Aryal, J

, Lucieer, A

and Bryan, BA 2019 , 'Uncertainty assessment of hyperspectral image classification: Deep learning vs. random forest' , Entropy, vol. 21, no. 1 , pp. 1-15 , doi: 10.3390/e21010078.

Roodposhti, MS, Aryal, J

and Pradhan, B 2019 , 'A novel rule-based approach in mapping landslide susceptibility' , Sensors, vol. 19, no. 10 , pp. 1-20 , doi: 10.3390/s19102274.

Rajbhandari, S, Aryal, J

, Osborn, J

, Lucieer, A

and Musk, R 2018 , 'Employing ontology to capture expert intelligence within GEOBIA: automation of the interpretation process', in RA White and A Coltekin and RR Hoffman (eds.), Remote Sensing and Cognition: Human Factors in Image Interpretation , Taylor and Francis Group, United States, pp. 151-175.

Minh-Thai, TN, Aryal, J

, Samarasinghe, J and Levin, M 2018 , 'A computational framework for autonomous self-repair systems', in T Mitrovic and B Xue and X Li (eds.), Proceedings of the 31st Australasian Joint Conference on Artificial Intelligence (AI 2018) , Springer, Switzerland, pp. 1-6 , doi: 10.1007/978-3-030-03991-2.

University of Tasmania

Open Access Repository

Article

Book Section

Conference Publication

Export as	Atom RSS 1.0 RSS 2.0