Dr Nawin Raj


NameDr Nawin Raj
Email Addressnawin.raj@unisq.edu.au
Job TitleSenior Lecturer (Mathematics)
QualificationsBEd South Pacific, BSc South Pacific, GDipMaths South Pacific, MSc South Pacific, PhD USQ
DepartmentSchool of Mathematics, Physics and Computing
ORCIDhttps://orcid.org/0000-0002-8364-2644
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BEd
South Pacific
2004
BSc
South Pacific
2010
GDipMaths
South Pacific
2006
MSc
South Pacific
2009
PhD
USQ
2015

Current Supervisions

Research TitleSupervisor TypeLevel of StudyCommenced
Investigate the methods for fast incremental learning of machine learning models for misuse and anomaly-based intrusion detection in the cyber-attack domain.Principal SupervisorDoctoral2023
Predicting Australia's Sea Level Rise and its Impacts with Oceanographic Data-Driven Insights Using Artificial Intelligence.Principal SupervisorDoctoral2022
Sustainable energy futures: Modelling energy demand using global climate models and developing interpretable models with Bayesian approachesAssociate SupervisorDoctoral2020
Hybrid Deep Learning Artificial Intelligent Models for wind speed forecasting in wind-rich regions in AustraliaPrincipal SupervisorDoctoral2020

Completed Supervisions

Research TitleSupervisor TypeLevel of StudyCompleted
Advancing Stochastic Wind Speed Forecasting Methods with Novel Hybrid Deep Learning TechniquesAssociate SupervisorDoctoral2024
Evaporation and Soil Moisture Prediction with Artificial Intelligence and Deep Learning MethodsAssociate SupervisorDoctoral2023
ARTIFICIAL INTELLIGENCE AND CLEAN AIR:DEVELOPMENT OF NOVEL ALGORITHMS WITH MACHINE LEARNING AND DEEP LEARNINGAssociate SupervisorDoctoral2022
Development of Deep Learning Predictive Models for Hydrological PredictionsAssociate SupervisorDoctoral2022
Development of flood risk monitoring and forecasting system with artificial intelligence predictive models for community risk management in FijiAssociate SupervisorMasters2021
Development of deep learning predictive models for short-term solar radiation forecasting: Case study in VietnamAssociate SupervisorMasters2020
DEVELOPMENT AND EVALUATION OF DATA-DRIVEN MODELSFOR ELECTRICITY DEMAND FORECASTING IN QUEENSLAND,AUSTRALIAAssociate SupervisorMasters2020
Predictive modelling of global solar radiation with artificial intelligence approaches using MODIS satellites and atmospheric reanalysis data for AustraliaAssociate SupervisorDoctoral2019
An wavelet-coupled artificial neural network-bootstrap model for environmental applications (Solar energy forecasting)Associate SupervisorMasters2018

Assessment and prediction of significant wave height using hybrid CNN-BiLSTM deep learning model for sustainable wave energy in Australia

Raj, Nawin and Prakash, Reema. 2024. "Assessment and prediction of significant wave height using hybrid CNN-BiLSTM deep learning model for sustainable wave energy in Australia." Sustainable Horizons . 11. https://doi.org/10.1016/j.horiz.2024.100098

Assessment and Prediction of Sea Level and Coastal Wetland Changes in Small Islands Using Remote Sensing and Artificial Intelligence

Raj, Nawin and Pasfield-Neofitou, Sarah. 2024. "Assessment and Prediction of Sea Level and Coastal Wetland Changes in Small Islands Using Remote Sensing and Artificial Intelligence." Remote Sensing. 16 (3), p. 551. https://doi.org/https://doi.org/10.3390/rs16030551

Short-term wind speed forecasting using an optimized three-phase convolutional neural network fused with bidirectional long short-term memory network model

Joseph, Lionel P., Deo, Ravinesh C., Casillas-Perez, David, Prasad, Ramendra, Raj, Nawin and Salcedo-sanz, Sancho. 2024. "Short-term wind speed forecasting using an optimized three-phase convolutional neural network fused with bidirectional long short-term memory network model." Applied Energy. 359. https://doi.org/10.1016/j.apenergy.2024.122624

Fostering Enduring Peer Learning Groups for 1st Year Students

Brown, Jason, Kennedy, Joel, Raj, Nawin and Quinton, Matthew. 2023. "Fostering Enduring Peer Learning Groups for 1st Year Students." 34th Annual Conference of the Australasian Association for Engineering Education (AAEE 2023). Gold Coast, Australia 03 - 06 Dec 2023 Australia. Australasian Association for Engineering Education.

Hybrid Deep Learning Model for Wave Height Prediction in Australia's Wave Energy Region

Ahmed, Abul Abrar Masrur, Jui, S Janifer Jabin, AL-Musaylh, Mohanad S., Raj, Nawin, Saha, Reepa, Deo, Ravinesh C and Saha, Sanjoy Kumar. 2024. "Hybrid Deep Learning Model for Wave Height Prediction in Australia's Wave Energy Region." Applied Soft Computing. 150. https://doi.org/10.1016/j.asoc.2023.111003

An advanced deep learning predictive model for air quality index forecasting with remote satellite-derived hydro-climatological variables

Ahmed, Abul Abrar Masrur, Jui, S. Janifer Jabin, Sharma, Ekta, Ahmed, Mohammad Hafez, Raj, Nawin and Bose, Aditi. 2023. "An advanced deep learning predictive model for air quality index forecasting with remote satellite-derived hydro-climatological variables." Science of the Total Environment. 906. https://doi.org/10.1016/j.scitotenv.2023.167234

The Academic Numeracy Framework: A tool to embed numeracy in tertiary courses, programs and study-support initiatives

Salmeron, Raquel, Galligan, Linda, Howarth, Debi and Raj, Nawin. 2023. "The Academic Numeracy Framework: A tool to embed numeracy in tertiary courses, programs and study-support initiatives." 8th Students Transitions Achievement Retention & Success Conference (STARS 2023). Brisbane, Australia 03 - 05 Jul 2023 Australia.

Prediction of Mean Sea Level with GNSS-VLM Correction Using a Hybrid Deep Learning Model in Australia

Raj, Nawin and Brown, Jason. 2023. "Prediction of Mean Sea Level with GNSS-VLM Correction Using a Hybrid Deep Learning Model in Australia." Remote Sensing. 15 (11).

Downscaling Surface Albedo to Higher Spatial Resolutions With an Image Super-Resolution Approach and PROBA-V Satellite Images

Deo, Ravinesh C., Karalasingham, Sagthitharan, Casillas-Perez, David, Raj, Narwin and Salcedo-sanz, Sancho. 2023. "Downscaling Surface Albedo to Higher Spatial Resolutions With an Image Super-Resolution Approach and PROBA-V Satellite Images." IEEE Access. 11, pp. 5558-5577. https://doi.org/10.1109/ACCESS.2023.3236253

Near real-time wind speed forecast model with bidirectional LSTM networks

Joseph, Lionel P., Deo, Ravinesh C., Prasad, Ramendra, Salcedo-Sanz, Sancho, Raj, Nawin and Soar, Jeffrey. 2023. "Near real-time wind speed forecast model with bidirectional LSTM networks." Renewable Energy. 204, pp. 39-58. https://doi.org/10.1016/j.renene.2022.12.123

CNN Based Image Classification of Malicious UAVs

Brown, Jason, Gharineiat, Zahra and Raj, Nawin. 2023. "CNN Based Image Classification of Malicious UAVs." Applied Sciences. 13 (1), pp. 1-13. https://doi.org/10.3390/app13010240

Prediction of Sea Level with Vertical Land Movement Correction Using Deep Learning

Raj, Nawin. 2022. "Prediction of Sea Level with Vertical Land Movement Correction Using Deep Learning." Mathematics. 10 (23). https://doi.org/10.3390/math10234533

The Playground Shade Index: A New Design Metric for Measuring Shade and Seasonal Ultraviolet Protection Characteristics of Parks and Playgrounds

Downs, Nathan, Raj, Nawin, Vanos, Jennifer, Parisi, Alfio, Butler, Harry, Deo, Ravinesh, Igoe, Damien, Dexter, Benjamin, Beckman-Downs, Melanie, Turner, Joanna and Dekeyser, Stijn. 2023. "The Playground Shade Index: A New Design Metric for Measuring Shade and Seasonal Ultraviolet Protection Characteristics of Parks and Playgrounds." Photochemistry and Photobiology. 99 (4), pp. 1193-1207. https://doi.org/10.1111/php.13745

New double decomposition deep learning methods for river water level forecasting

Ahmed, A. A. Masrur, Deo, Ravinesh C., Ghahramani, Afshin, Feng, Qi, Raj, Nawin, Yin, Zhenliang and Yang, Linshan. 2022. "New double decomposition deep learning methods for river water level forecasting." Science of the Total Environment. 831, pp. 1-21. https://doi.org/10.1016/j.scitotenv.2022.154722

Novel hybrid deep learning model for satellite based PM10 forecasting in the most polluted Australian hotspots

Sharma, Ekta, Deo, Ravinesh C., Soar, Jeffrey, Prasad, Ramendra, Parisi, Alfio V. and Raj, Nawin. 2022. "Novel hybrid deep learning model for satellite based PM10 forecasting in the most polluted Australian hotspots." Atmospheric Environment. 279, pp. 1-13. https://doi.org/10.1016/j.atmosenv.2022.119111

Hybrid deep learning method for a week-ahead evapotranspiration forecasting

Ahmed, A. A. Masrur, Deo, Ravinesh C., Feng, Qi, Ghahramani, Afshin, Raj, Nawin, Yin, Zhenliang and Yang, Linshan. 2022. "Hybrid deep learning method for a week-ahead evapotranspiration forecasting." Stochastic Environmental Research and Risk Assessment. 36 (3), pp. 831-849. https://doi.org/10.1007/s00477-021-02078-x

Assessment and Prediction of Sea Level Trend in the South Pacific Region

Raj, Nawin, Gharineiat, Zahra, Ahmed, Abul Abrar Masrur and Stepanyants, Yury. 2022. "Assessment and Prediction of Sea Level Trend in the South Pacific Region." Remote Sensing. 14 (4), pp. 1-25. https://doi.org/10.3390/rs14040986

Spatiotemporal Hybrid Random Forest Model for Tea Yield Prediction Using Satellite-Derived Variables

Jui, S. Janifer Jabin, Ahmed, A. A. Masrur, Bose, Aditi, Raj, Nawin, Sharma, Ekta, Soar, Jeffrey and Chowdhury, Md Wasique Islam. 2022. "Spatiotemporal Hybrid Random Forest Model for Tea Yield Prediction Using Satellite-Derived Variables." Remote Sensing. 14 (3), pp. 1-18. https://doi.org/10.3390/rs14030805

Development and evaluation of hybrid deep learning long short-term memory network model for pan evaporation estimation trained with satellite and ground-based data

Jayasinghe, W. J. M. Lakmini Prarthana, Deo, Ravinesh C., Ghahramani, Afshin, Ghimire, Sujan and Raj, Nawin. 2022. "Development and evaluation of hybrid deep learning long short-term memory network model for pan evaporation estimation trained with satellite and ground-based data." Journal of Hydrology. 607, pp. 1-19. https://doi.org/10.1016/j.jhydrol.2022.127534

Deep Multi-Stage Reference Evapotranspiration Forecasting Model: Multivariate Empirical Mode Decomposition Integrated With the Boruta-Random Forest Algorithm

Jayasinghe, W. J. M. Lakmini Prarthana, Deo, Ravinesh C., Ghahramani, Afshin, Ghimire, Sujan and Raj, Nawin. 2021. "Deep Multi-Stage Reference Evapotranspiration Forecasting Model: Multivariate Empirical Mode Decomposition Integrated With the Boruta-Random Forest Algorithm." IEEE Access. 9, pp. 166695-166708. https://doi.org/10.1109/ACCESS.2021.3135362

Evaluation of multivariate adaptive regression splines and artificial neural network for prediction of mean sea level trend around northern Australian coastlines

Raj, Nawin and Gharineiat, zahra. 2021. "Evaluation of multivariate adaptive regression splines and artificial neural network for prediction of mean sea level trend around northern Australian coastlines." Mathematics. 9 (21), pp. 1-20. https://doi.org/10.3390/math9212696

Novel short-term solar radiation hybrid model: long short-term memory network integrated with robust local mean decomposition

Huynh, Anh Ngoc-Lan, Deo, Ravinesh C., Ali, Mumtaz, Abdulla, Shahab and Raj, Nawin. 2021. "Novel short-term solar radiation hybrid model: long short-term memory network integrated with robust local mean decomposition." Applied Energy. 298, pp. 1-19. https://doi.org/10.1016/j.apenergy.2021.117193

Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity

Ahmed, A. A. Masrur, Deo, Ravinesh C., Feng, Qi, Ghahramani, Afshin, Raj, Nawin, Yin, Zhenliang and Yang, Linshan. 2021. "Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity." Journal of Hydrology. 599, pp. 1-23. https://doi.org/10.1016/j.jhydrol.2021.126350

The Impact of Initial Swarm Formation for Tracking of a High Capability Malicious UAV

Brown, Jason and Raj, Nawin. 2021. "The Impact of Initial Swarm Formation for Tracking of a High Capability Malicious UAV." International IOT, Electronics and Mechatronics Conference (IEMTRONICS 2021). Toronto, Canada 21 - 24 Apr 2021 Piscataway, United States. https://doi.org/10.1109/IEMTRONICS52119.2021.9422506

Guidance law for a surveillance UAV swarm tracking a high capability malicious UAV

Brown, Jason and Raj, Nawin. 2021. "Guidance law for a surveillance UAV swarm tracking a high capability malicious UAV." 2021 IEEE Asia Pacific Conference on Wireless and Mobile (APWiMob 2021). Bandung, Indonesia 08 - 10 Apr 2021 United States. https://doi.org/10.1109/APWiMob51111.2021.9435240

An EEMD-BiLSTM algorithm integrated with Boruta random forest optimiser for significant wave height forecasting along coastal areas of Queensland, Australia

Raj, Nawin and Brown, Jason. 2021. "An EEMD-BiLSTM algorithm integrated with Boruta random forest optimiser for significant wave height forecasting along coastal areas of Queensland, Australia." Remote Sensing. 13 (8), pp. 1-20. https://doi.org/10.3390/rs13081456

Predictive Tracking of a High Capability Malicious UAV

Brown, Jason and Raj, Nawin. 2021. "Predictive Tracking of a High Capability Malicious UAV." IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC 2021). Las Vegas, United States 27 - 30 Jan 2021 Piscataway, United States. https://doi.org/10.1109/CCWC51732.2021.9376137

Designing Deep-based Learning Flood Forecast Model with ConvLSTM Hybrid Algorithm

Moishin, Mohammed, Deo, Ravinesh C., Prasad, Ramendra, Raj, Nawin and Abdulla, Shahab. 2021. "Designing Deep-based Learning Flood Forecast Model with ConvLSTM Hybrid Algorithm." IEEE Access. 9, pp. 50982-50993. https://doi.org/10.1109/ACCESS.2021.3065939

LSTM integrated with Boruta-random forest optimiser for soil moisture estimation under RCP4.5 and RCP8.5 global warming scenarios

Ahmed, A. A. Masrur, Deo, Ravinesh C., Ghahramani, Afshin, Raj, Nawin, Feng, Qi, 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. 35, pp. 1851-1881. https://doi.org/10.1007/s00477-021-01969-3

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

Ahmed, A. A. Masrur, Deo, Ravinesh C., Raj, Nawin, Ghahramani, Afshin, Feng, Qi, 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), pp. 1-30. https://doi.org/10.3390/rs13040554

Development of Flood Monitoring Index for daily flood risk evaluation: case studies in Fiji

Moishin, Mohammed, Deo, Ravinesh C., Prasad, Ramendra, Raj, Nawin and Abdulla, Shahab. 2021. "Development of Flood Monitoring Index for daily flood risk evaluation: case studies in Fiji." Stochastic Environmental Research and Risk Assessment. 35 (7), pp. 1387-1402. https://doi.org/10.1007/s00477-020-01899-6

Deep Air Quality Forecasts: Suspended Particulate Matter Modeling With Convolutional Neural and Long Short-Term Memory Networks

Sharma, Ekta, Deo, Ravinesh C., Prasad, Ramendra, 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. https://doi.org/10.1109/ACCESS.2020.3039002

Development of data-driven models for wind speed forecasting in Australia

Neupane, Ananta, Raj, Nawin, Deo, Ravinesh and Ali, Mumtaz. 2021. "Development of data-driven models for wind speed forecasting in Australia." Deo, R., Samui, Pijush and Roy, Sanjiban Sekhar (ed.) Predictive modelling for energy management and power systems engineering. Netherlands. Elsevier. pp. 143-190

Design of Alkali-Activated Slag-Fly Ash Concrete Mixtures Using Machine Learning

Gunasekara, C., Lokuge, W., Keskic, M., Raj, N., Law, D. W. and Setunge, S.. 2020. "Design of Alkali-Activated Slag-Fly Ash Concrete Mixtures Using Machine Learning." ACI Materials Journal. 117 (5), pp. 263-278. https://doi.org/10.14359/51727019

Near real-time global solar radiation forecasting at multiple time-step horizons using the long short-term memory network

Huynh, Anh Ngoc‐Lan, Deo, Ravinesh C., An-Vo, Duc-Anh, Ali, Mumtaz, Raj, Nawin and Abdulla, Shahab. 2020. "Near real-time global solar radiation forecasting at multiple time-step horizons using the long short-term memory network." Energies. 13 (14). https://doi.org/10.3390/en13143517

Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms

Ghimire, Sujan, Deo, Ravinesh C., Raj, Nawin and Mi, Jianchun. 2019. "Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms." Applied Energy. 253, pp. 1-20. https://doi.org/10.1016/j.apenergy.2019.113541

Wavelet-based 3-phase hybrid SVR model trained with satellite-derived predictors, particle swarm optimization and maximum overlap discrete wavelet transform for solar radiation prediction

Ghimire, Sujan, Deo, Ravinesh C., Raj, Nawin and Mi, Jianchun. 2019. "Wavelet-based 3-phase hybrid SVR model trained with satellite-derived predictors, particle swarm optimization and maximum overlap discrete wavelet transform for solar radiation prediction." Renewable and Sustainable Energy Reviews. 113, pp. 1-19. https://doi.org/10.1016/j.rser.2019.109247

Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction

Ghimire, Sujan, Deo, Ravinesh C., Raj, Nawin and Mi, Jianchun. 2019. "Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction." Energies. 12 (12), pp. 1-42. https://doi.org/10.3390/en12122407

Global solar radiation prediction by ANN integrated with European Centre for medium range weather forecast fields in solar rich cities of Queensland Australia

Ghimire, Sujan, Deo, Ravinesh C., Downs, Nathan J. and Raj, Nawin. 2019. "Global solar radiation prediction by ANN integrated with European Centre for medium range weather forecast fields in solar rich cities of Queensland Australia." Journal of Cleaner Production. 216, pp. 288-310. https://doi.org/10.1016/j.jclepro.2019.01.158

Optimization of windspeed prediction using an artificial neural network compared with a genetic programming model

Deo, Ravinesh C., Ghimire, Sujan, Downs, Nathan J. and Raj, Nawin. 2018. "Optimization of windspeed prediction using an artificial neural network compared with a genetic programming model." Kim, Dookie, Roy, Sanjiban Sekhar, Lansivaara, Tim, Deo, Ravinesh C. and Samui, Pijush (ed.) Handbook of research on predictive modeling and optimization methods in science and engineering. Hershey, United States. IGI Global. pp. 328-359

Input selection and data-driven model performance optimization to predict the Standardized Precipitation and Evaporation Index in a drought-prone region

Mouatadid, Soukayna, Raj, Nawin, 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. https://doi.org/10.1016/j.atmosres.2018.05.012

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

Ghimire, Sujan, Deo, Ravinesh C., 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: an interdisciplinary journal. 212, pp. 176-198. https://doi.org/10.1016/j.rse.2018.05.003

Adiabatic decay of internal solitons due to Earth’s rotation within the framework of the Gardner–Ostrovsky equation

Obregon, Maria, 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. https://doi.org/10.1063/1.5021864

Adiabatic decay of internal solitons in a rotating ocean

Obregon, M. A., Raj, N. and Stepanyants, Y. A.. 2016. "Adiabatic decay of internal solitons in a rotating ocean." 20th Australasian Fluid Mechanics Conference (AFMC 2016). Perth, Australia 05 - 08 Dec 2016 Australia.

Nonlinear vector waves of a flexural mode in a chain model of atomic particles

Nikitenkova, S. P., Raj, N. and Stepanyants, Y. A.. 2015. "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. https://doi.org/10.1016/j.cnsns.2014.05.031

Particle and particle-like solitary wave dynamics in fluid media

Raj, Nawin. 2015. Particle and particle-like solitary wave dynamics in fluid media. PhD Thesis Doctor of Philosophy. University of Southern Queensland.

Nonlinear spectra of shallow water waves

Giovanangeli, J. -P., Kharif, C., Raj, N. and Stepanyants, Y.. 2013. "Nonlinear spectra of shallow water waves." Oceans - San Diego, 2013. San Diego, United States 23 - 26 Sep 2013 United States. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.23919/OCEANS.2013.6741132

Numerical study of nonlinear wave processes by means of discrete chain models

Obregon, M., Raj, N. and Stepanyants, Y.. 2012. "Numerical study of nonlinear wave processes by means of discrete chain models." Gu, Y. T. and Saha, Suvash C. (ed.) 4th International Conference on Computational Methods (ICCM 2012). Gold Coast, Australia 25 - 28 Nov 2012 Brisbane, Australia.

Creating streamtubes based on mass conservative streamlines

Raj, Nawin and Li, Zhenquan. 2008. "Creating streamtubes based on mass conservative streamlines." International Journal of Mathematical, Physical and Engineering Sciences. 2 (1), pp. 41-45. https://doi.org/10.5281/zenodo.1081061