Dr Nawin Raj

Research interests
-Applied Mathematics
-Nonlinear dynamics and general theory of nonlinear oscillations and waves;
-Theoretical and computational fluid dynamics;

Professional memberships
Queensland College of Teachers

Industry affiliations
Member of the Australian Society of Mathematicians

Currently teaching courses/programs
NUR1100; NUR1299 NUR2000 (Numeracy components);
ENM1500 Introductory Engineering Mathematics;
ENM1600 Engineering Mathematics I;
ENM2600 Advanced Engineering Mathematics;
MAT1100 Foundation Mathematics
MAT1101 Discrete Mathematics for Computing;
MAT3103 Mathematical Modelling and Dynamical Systems.

Teaching experience(Tertiary)
8 Years

Research most recent
1. Obregon, Maria and Raj, Nawin and Stepanyants, Yury (2018) Adiabatic decay of internal solitons due to Earth’s rotation
within the framework of the Gardner–Ostrovsky equation. Chaos: An interdisciplinary Journal of Nonlinear Science, 28 (3).
pp. 1-11. ISSN 1054-1500
2. Mouatadid, Soukayna and Raj, Nawin and Deo, Ravinesh C. and Adamowski, Jan F. (2018) Input selection and data-driven
model performance optimization to predict the Standardized Precipitation and Evaporation Index in a drought-prone
region. Atmospheric Research, 212. pp. 130-149. ISSN 0169-8095
3. Ghimire, Sujan and Deo, Ravinesh C. and Downs, Nathan J. and Raj, Nawin (2018) Self-adaptive differential evolutionary
extreme learning machines for long-term solar radiation prediction with remotely-sensed MODIS satellite and Reanalysis
atmospheric products in solar-rich cities. Remote Sensing of Environment, 212. pp. 176-198. ISSN 0034-4257

Research most notable
1. Obregon, Maria and Raj, Nawin and Stepanyants, Yury (2018) Adiabatic decay of internal solitons due to Earth’s rotation
within the framework of the Gardner–Ostrovsky equation. Chaos: An interdisciplinary Journal of Nonlinear Science, 28 (3).
2. Nikitenkova S.P. and Raj N. and Stepanyants Y.A. (2013) Nonlinear vector waves of a flexural mode in a chain model of
atomic particles. Communications in Nonlinear Science and Numerical Simulation, 20 (3). pp. 731-742. ISSN 1007-5704
3. Raj, N., Li, Z. (2008) Creating Streamtubes Based on Mass Conservative Streamlines. International Journal of
Mathematical, Physical and Engineering Sciences, 2(1)(2008), 41-45.

Publications in ePrints

Ahmed, A. A. Masrur and Deo, Ravinesh C.ORCID: https://orcid.org/0000-0002-2290-6749 and Raj, Nawin and Ghahramani, AfshinORCID: https://orcid.org/0000-0002-9648-4606 and Feng, Qi and Yin, Zhenliang and Yang, Linshan (2021) Deep Learning Forecasts of Soil Moisture: Convolutional Neural Network and Gated Recurrent Unit Models Coupled with Satellite-Derived MODIS, Observations and Synoptic-Scale Climate Index Data. Remote Sensing, 13 (4):554. pp. 1-30. ISSN 2072-4292

Neupane, Ananta and Raj, Nawin and Deo, RavineshORCID: https://orcid.org/0000-0002-2290-6749 and Ali, Mumtaz (2021) Development of data-driven models for wind speed forecasting in Australia. In: Predictive modelling for energy management and power systems engineering. Elsevier, Amsterdam, Netherlands, pp. 143-190. ISBN 978-0-12-817772-3

Ahmed, A. A. Masrur and Deo, Ravinesh C.ORCID: https://orcid.org/0000-0002-2290-6749 and Ghahramani, AfshinORCID: https://orcid.org/0000-0002-9648-4606 and Raj, Nawin and Feng, Qi and Yin, Zhenliang and Yang, Linshan (2021) LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenarios. Stochastic Environmental Research and Risk Assessment. pp. 1-31. ISSN 1436-3240

Sharma, Ekta and Deo, Ravinesh C.ORCID: https://orcid.org/0000-0002-2290-6749 and Prasad, Ramendra and Parisi, Alfio and Raj, Nawin (2020) Deep Air Quality Forecasts: Suspended Particulate Matter Modeling With Convolutional Neural and Long Short-Term Memory Networks. IEEE Access, 8. pp. 209503-209516. ISSN 2169-3536

Gunasekara, C. and Lokuge, W. and Keskic, M. and Raj, N. and Law, D. W. and Setunge, S. (2020) Design of Alkali-Activated Slag-Fly Ash Concrete Mixtures Using Machine Learning. Materials Journal, 117 (5). pp. 263-278. ISSN 0889-325X

View full listing in ePrints