University of Natural Resources and Life Sciences, Vienna (BOKU) - Research portal
Publications
Search Items: neural network, . hits: 42
** = Publications listed in SCI/SSCI - Journals (published in Web of Science)
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2023
** Marimuthu, S; J, SMP; Rajendran, K; Artificial neural network modeling and statistical optimization of medium components to enhance production of exopolysaccharide by .
Prep Biochem Biotechnol. 2023; 53(2):136-147 WoS PubMed FullText FullText_BOKU** Medl, M; Rajamanickam, V; Striedner, G; Newton, J Development and Validation of an Artificial Neural-Network-Based Optical Density Soft Sensor for a High-Throughput Fermentation System.
PROCESSES. 2023; 11(1), 297
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** Zeitfogel, H; Feigl, M; Schulz, K Soil information on a regional scale: Two machine learning based approaches for predicting saturated hydraulic conductivity.
GEODERMA. 2023; 433, 116418 WoS FullText FullText_BOKU2022
** Avand, M; Kuriqi, A; Khazaei, M; Ghorbanzadeh, O DEM resolution effects on machine learning performance for flood probability mapping.
J HYDRO-ENVIRON RES. 2022; 40: 1-16. WoS FullText FullText_BOKU** Ghorbanzadeh, O; Shahabi, H; Crivellari, A; Homayouni, S; Blaschke, T; Ghamisi, P Landslide detection using deep learning and object-based image analysis.
LANDSLIDES. 2022; 19(4): 929-939. WoS FullText FullText_BOKU** Pinto, J; Mestre, M; Ramos, J; Costa, RS; Striedner, G; Oliveira, R A general deep hybrid model for bioreactor systems: Combining first principles with deep neural networks.
COMPUT CHEM ENG. 2022; 165, 107952 WoS FullText FullText_BOKU2021
** Ghorbanzadeh, O; Meena, SR; Abadi, HSS; Piralilou, ST; Lv, ZY; Blaschke, T Landslide Mapping Using Two Main Deep-Learning Convolution Neural Network Streams Combined by the Dempster-Shafer Model.
IEEE J-STARS. 2021; 14: 452-463.
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** Ghorbanzadeh, O; Tiede, D; Wendt, L; Sudmanns, M; Lang, SF Transferable instance segmentation of dwellings in a refugee camp-integrating CNN and OBIA.
EUR J REMOTE SENS. 2021; 54: 127-140.
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** Krippl, M; Kargl, T; Duerkop, M; Durauer, A Hybrid modeling reduces experimental effort to predict performance of serial and parallel single-pass tangential flow filtration.
SEP PURIF TECHNOL. 2021; 276, 119277
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** Meena, SR; Ghorbanzadeh, O; van Westen, CJ; Nachappa, TG; Blaschke, T; Singh, RP; Sarkar, R Rapid mapping of landslides in the Western Ghats (India) triggered by 2018 extreme monsoon rainfall using a deep learning approach.
LANDSLIDES. 2021; 18(5): 1937-1950. WoS FullText FullText_BOKU** Wei, XJ; Wang, X; Wei, G; Zhu, CW; Shi, Y Prediction of Jacking Force in Vertical Tunneling Projects Based on Neuro-Genetic Models.
J MAR SCI ENG. 2021; 9(1), 71
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2020
** Ariza, MS; Zambon, I; Sousa, HS; Matos, JACE; Strauss, A Comparison of forecasting models to predict concrete bridge decks performance.
STRUCT CONCRETE. 2020; 21(4): 1240-1253. WoS FullText FullText_BOKU** Gholamnia, K; Nachappa, TG; Ghorbanzadeh, O; Blaschke, T Comparisons of Diverse Machine Learning Approaches for Wildfire Susceptibility Mapping.
SYMMETRY-BASEL. 2020; 12(4), 604
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** Ghorbanzadeh, O; Shahabi, H; Mirchooli, F; Kamran, KV; Lim, S; Aryal, J; Jarihani, B; Blaschke, T Gully erosion susceptibility mapping (GESM) using machine learning methods optimized by the multi-collinearity analysis and K-fold cross-validation.
GEOMAT NAT HAZ RISK. 2020; 11(1): 1653-1678.
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** Krippl, M; Bofarull-Manzano, I; Duerkop, M; Durauer, A Hybrid Modeling for Simultaneous Prediction of Flux, Rejection Factor and Concentration in Two-Component Crossflow Ultrafiltration.
PROCESSES. 2020; 8(12), 1625
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** Krippl, M; Durauer, A; Duerkop, M Hybrid modeling of cross-flow filtration: Predicting the flux evolution and duration of ultrafiltration processes.
SEP PURIF TECHNOL. 2020; 248, 117064
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2019
** Adede, C; Oboko, R; Wagacha, PW; Atzberger, C Model Ensembles of Artificial Neural Networks and Support Vector Regression for Improved Accuracy in the Prediction of Vegetation Conditions and Droughts in Four Northern Kenya Counties.
ISPRS INT GEO-INF. 2019; 8(12), 562
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** Ghorbanzadeh, O; Blaschke, T; Gholamnia, K; Aryal, J Forest Fire Susceptibility and Risk Mapping Using Social/Infrastructural Vulnerability and Environmental Variables.
FIRE-BASEL. 2019; 2(3), 50
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** Ghorbanzadeh, O; Blaschke, T; Gholamnia, K; Meena, SR; Tiede, D; Aryal, J Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection.
REMOTE SENS-BASEL. 2019; 11(2), 196
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** Ghorbanzadeh, O; Kamran, KV; Blaschke, T; Aryal, J; Naboureh, A; Einali, J; Bian, JH Spatial Prediction of Wildfire Susceptibility Using Field Survey GPS Data and Machine Learning Approaches.
FIRE-BASEL. 2019; 2(3), 43
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** Rammer, W; Seidl, R A scalable model of vegetation transitions using deep neural networks.
METHODS ECOL EVOL. 2019; 10(6): 879-890.
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** Rammer, W; Seidl, R Harnessing Deep Learning in Ecology: An Example Predicting Bark Beetle Outbreaks.
FRONT PLANT SCI. 2019; 10, 1327
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** Salimi, AH; Samakosh, JM; Sharifi, E; Hassanvand, MR; Noori, A; von Rautenkranz, H Optimized Artificial Neural Networks-Based Methods for Statistical Downscaling of Gridded Precipitation Data.
WATER-SUI. 2019; 11(8), 1653
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2016
** Cipri, A; Schulz, C; Ludwig, R; Gorton, L; del Valle, M A novel bio-electronic tongue using different cellobiose dehydrogenases to resolve mixtures of various sugars and interfering analytes.
BIOSENS BIOELECTRON. 2016; 79: 515-521. WoS PubMed FullText FullText_BOKU2015
** Kinzner, MC; Wagner, HC; Peskoller, A; Moder, K; Dowell, FE; Arthofer, W; Schlick-Steiner, BC; Steiner, FM A near-infrared spectroscopy routine for unambiguous identification of cryptic ant species.
PEERJ. 2015; 3: WoS FullText FullText_BOKU** Melcher, M; Scharl, T; Spangl, B; Luchner, M; Cserjan, M; Bayer, K; Leisch, F; Striedner, G; The potential of random forest and neural networks for biomass and recombinant protein modeling in Escherichia coli fed-batch fermentations..
Biotechnol J. 2015; 10(11):1770-1782 WoS PubMed FullText FullText_BOKU2013
** Atzberger, C; Rembold, F Mapping the Spatial Distribution of Winter Crops at Sub-Pixel Level Using AVHRR NDVI Time Series and Neural Nets.
REMOTE SENS-BASEL. 2013; 5(3): 1335-1354. WoS FullText FullText_BOKU** Savic, IM; Nikolic, VD; Savic, IM; Nikolic, LB; Stankovic, MZ; Moder, K Optimization of total flavonoid compound extraction from Camellia sinensis using the artificial neural network and response surface methodology.
HEM IND. 2013; 67(2): 249-259. WoS FullText FullText_BOKU2012
** Atzberger, C; Rembold, F Portability of neural nets modelling regional winter crop acreages using AVHRR time series.
EUR J REMOTE SENS. 2012; 45(2): 371-392. WoS FullText FullText_BOKU** Richter, K; Hank, TB; Vuolo, F; Mauser, W; DxxxUrso, G Optimal Exploitation of the Sentinel-2 Spectral Capabilities for Crop Leaf Area Index Mapping.
REMOTE SENS-BASEL. 2012; 4(3): 561-582. WoS FullText FullText_BOKU** Savic, IM; Stojiljkovic, ST; Savic, IM; Stojanovic, SB; Moder, K Modeling and Optimization of Fe(III) Adsorption from Water using Bentonite Clay: Comparison of Central Composite Design and Artificial Neural Network.
CHEM ENG TECHNOL. 2012; 35(11): 2007-2014. WoS FullText FullText_BOKU2011
** Lopez, JA; Verdiguier, EI; Chova, LG; Mari, JM; Barreiro, JZR; Valls, GC; Maravilla, JC Land cover classification of VHR airborne images for citrus grove identification.
ISPRS J PHOTOGRAMM. 2011; 66(1): 115-123. WoS FullText FullText_BOKU2007
** Farifteh, J; Van der Meer, FD; Atzberger, C; Carranza, EJM Quantitative analysis of salt-affected soil reflectance spectra: A comparison of two adaptive methods (PLSR and ANN).
REMOTE SENS ENVIRON. 2007; 110(1): 59-78. WoS FullText FullText_BOKU2006
** Schlerf, M; Atzberger, C Inversion of a forest reflectance model to estimate structural canopy variables from hyperspectral remote sensing data.
REMOTE SENS ENVIRON. 2006; 100(3): 281-294. WoS FullText FullText_BOKU2005
** Strik, D., Domnanovich, A., Zani, L., Braun, R., Holubar, P. Prediction of trace compounds in biogas from anaerobic digestion using the MATLAB Neural Network Toolbox..
Environmental Modelling & Software, 20, 6, 803-810; ISSN 1364-8152 WoS FullText FullText_BOKU2003
** Holubar, P., Zani, L., Hager, M., Fröschl, W., Radak, Z., Braun, R. Start-up and Recovery of a Biogas-Reactor using a Hierarchical Neural Network-based Control Tool.
Journal of Chemical Technology and Biotechnology, 78, 9, 847-855 WoS FullText FullText_BOKU2002
** Holubar, P; Zani, L; Hager, M; Fröschl, W; Radak, Z; Braun, R; Advanced controlling of anaerobic digestion by means of hierarchical neural networks..
Water Res. 2002; 36(10):2582-2588 WoS PubMed2001
** Hasenauer, H., Merkl, D., Weingartner, M. Estimating tree mortality of Norway spruce stands with neural networks..
Advances in Environmental Research, 5, 4, 405-414 WoS** Wotawa, F; Wotawa, G Deriving qualitative rules from neural networks - a case study for ozone forecasting.
AI COMMUNICATIONS. AI COMMUNICATIONS; 14: 23-33. WoS2000
** Hasenauer, H., Kindermann, G., Merkl, D. Zur Schätzung der Verjüngungssituation in Mischbeständen mit Hilfe Neuraler Netze..
Forstw. Cbl., 119, 350-366 WoS** Holubar, P., Zani, L., Hager, M., Fröschl, W., Radak, Z., Braun, R. Modelling of anaerobic digestion using self-organizing maps and artificial neural networks..
Wat. Sci. Tech., 41, 12, 149-156 WoS1999
** Soja, G; Soja, AM Ozone indices based on simple meteorological parameters: potentials and limitations of regression and neural network models.
ATMOS ENVIRON. 1999; 33(26): 4299-4307. WoS FullText FullText_BOKU