Ravinesh C. Deo

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Support vector machine model for multistep wind speed forecasting

Shobna Mohini Mala Prasad, Thong Nguyen-Huy, Ravinesh C. Deo, 2021 .

Adaptive boost LS-SVM classification approach for time-series signal classification in epileptic seizure diagnosis applications

Ravinesh C. Deo, Mohammed Diykh, Jonathan H. Green, 2020, Expert Syst. Appl..

Ensemble neural network approach detecting pain intensity from facial expressions

Jeffrey Soar, Xujuan Zhou, Hua Wang, 2020, Artif. Intell. Medicine.

Designing a New Data Intelligence Model for Global Solar Radiation Prediction: Application of Multivariate Modeling Scheme

Nadhir Al-Ansari, Hossein Bonakdari, Isa Ebtehaj, 2019, Energies.

Long‐term modelling of wind speeds using six different heuristic artificial intelligence approaches

Zaher Mundher Yaseen, Ozgur Kisi, Ravinesh C. Deo, 2019, International Journal of Climatology.

Designing an evaluation of a tertiary preparatory program within the university context

Ravinesh C. Deo, R. Deo, 2011 .

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

Ravinesh C. Deo, Sujan Ghimire, Nawin Raj, 2019, Renewable and Sustainable Energy Reviews.

Handbook of Research on Predictive Modeling and Optimization Methods in Science and Engineering

Pijush Samui, Sanjiban Sekhar Roy, Ravinesh C. Deo, 2018 .

Enhanced deep learning algorithm development to detect pain intensity from facial expression images

Jeffrey Soar, Xujuan Zhou, Ravinesh C. Deo, 2020, Expert Syst. Appl..

Sleep EEG signal analysis based on correlation graph similarity coupled with an ensemble extreme machine learning algorithm

Ravinesh C. Deo, Mohammed Diykh, Shahab Abdulla, 2019, Expert Syst. Appl..

The modeling of human facial pain intensity based on Temporal Convolutional Networks trained with video frames in HSV color space

Jeffrey Soar, Xujuan Zhou, Hua Wang, 2020, Appl. Soft Comput..

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

Ravinesh C. Deo, Nawin Raj, Ananta Neupane, 2021 .

Big data in engineering applications

Sanjiban Sekhar Roy, Ravinesh C. Deo, Pijushi Samui, 2018 .

Survey of different data-intelligent modeling strategies for forecasting air temperature using geographic information as model predictors

Özgür Kisi, Pijush Samui, Zaher Mundher Yaseen, 2018, Comput. Electron. Agric..

An enhanced extreme learning machine model for river flow forecasting: State-of-the-art, practical applications in water resource engineering area and future research direction

Zaher Mundher Yaseen, Ravinesh C. Deo, Kwok-wing Chau, 2019, Journal of Hydrology.

Rainfall Pattern Forecasting Using Novel Hybrid Intelligent Model Based ANFIS-FFA

Zaher Mundher Yaseen, Hossein Bonakdari, Isa Ebtehaj, 2017, Water Resources Management.

Weekly soil moisture forecasting with multivariate sequential, ensemble empirical mode decomposition and Boruta-random forest hybridizer algorithm approach

Yan Li, Ravinesh C. Deo, Tek Narayan Maraseni, 2019, CATENA.

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

Ravinesh C. Deo, Sujan Ghimire, Nawin Raj, 2019, Applied Energy.

Global Solar Radiation Estimation and Climatic Variability Analysis Using Extreme Learning Machine Based Predictive Model

Ahmad Sharafati, Nadhir Al-Ansari, Sinan Q. Salih, 2020, IEEE Access.

Short-term electrical energy demand prediction under heat island effects using emotional neural network integrated with genetic algorithm

Ravinesh C. Deo, Sagthitharan Karalasingham, Ramendra Prasad, 2021 .

Shear strength prediction of steel fiber reinforced concrete beam using hybrid intelligence models: A new approach

Zaher Mundher Yaseen, Ravinesh C. Deo, Sungwon Kim, 2018, Engineering Structures.

Artificial intelligence approach for the prediction of Robusta coffee yield using soil fertility properties

Ravinesh C. Deo, Louis Kouadio, Shahbaz Mushtaq, 2018, Comput. Electron. Agric..

A general extensible learning approach for multi-disease recommendations in a telehealth environment

Ji Zhang, Liang Chang, Ravinesh C. Deo, 2020, Pattern Recognit. Lett..

Electrical Energy Demand Forecasting Model Development and Evaluation with Maximum Overlap Discrete Wavelet Transform-Online Sequential Extreme Learning Machines Algorithms

Ravinesh C. Deo, Yan Li, Mohanad S. AL-Musaylh, 2020, Energies.

A Joint Deep Neural Network Model for Pain Recognition from Face

Jeffrey Soar, Xujuan Zhou, Ravinesh C. Deo, 2019, 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS).

Multi-stage committee based extreme learning machine model incorporating the influence of climate parameters and seasonality on drought forecasting

Ravinesh C. Deo, Mumtaz Ali, Nathan J. Downs, 2018, Comput. Electron. Agric..

Prediction of SPEI Using MLR and ANN: A Case Study for Wilsons Promontory Station in Victoria

Ravinesh C. Deo, Soukayna Mouatadid, Jan F. Adamowski, 2015, 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA).

Developing reservoir evaporation predictive model for successful dam management

Ahmed El-Shafie, Ravinesh C. Deo, Mohammed Falah Allawi, 2020, Stochastic Environmental Research and Risk Assessment.

Hybridized neural fuzzy ensembles for dust source modeling and prediction

Biswajeet Pradhan, Ravinesh C. Deo, Dieu Tien Bui, 2020 .

Application of the Hybrid Artificial Neural Network Coupled with Rolling Mechanism and Grey Model Algorithms for Streamflow Forecasting Over Multiple Time Horizons

Zaher Mundher Yaseen, Ravinesh C. Deo, Minglei Fu, 2018, Water Resources Management.

Near real-time significant wave height forecasting with hybridized multiple linear regression algorithms

Yong Xiang, Ravinesh C. Deo, Mumtaz Ali, 2020 .

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

Ravinesh C. Deo, Alfio V. Parisi, Nawin Raj, 2020, IEEE Access.

Two-phase extreme learning machines integrated with the complete ensemble empirical mode decomposition with adaptive noise algorithm for multi-scale runoff prediction problems

Min Wu, Ravinesh C. Deo, Vijay P. Singh, 2019, Journal of Hydrology.

Ensemble committee-based data intelligent approach for generating soil moisture forecasts with multivariate hydro-meteorological predictors

Yan Li, Ravinesh C. Deo, Tek Narayan Maraseni, 2018, Soil and Tillage Research.

Computational intelligence approaches for energy load forecasting in smart energy management grids: state of the art, future challenges, and research directionsand Research Directions

Mauro Conti, Shahaboddin Shamshirband, Ravinesh C. Deo, 2018 .

Development and evaluation of the cascade correlation neural network and the random forest models for river stage and river flow prediction in Australia

Sungwon Kim, Mohammad Ali Ghorbani, Ravinesh C. Deo, 2020, Soft Comput..

Reservoir inflow forecasting with a modified coactive neuro-fuzzy inference system: a case study for a semi-arid region

Ahmed El-Shafie, Ravinesh C. Deo, Othman Jaafar, 2018, Theoretical and Applied Climatology.

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

Ravinesh C. Deo, Sujan Ghimire, Nawin Raj, 2019, Energies.

Short-term electricity demand forecasting with MARS, SVR and ARIMA models using aggregated demand data in Queensland, Australia

Yan Li, Ravinesh C. Deo, Jan F. Adamowski, 2018, Adv. Eng. Informatics.

Multi-layer perceptron hybrid model integrated with the firefly optimizer algorithm for windspeed prediction of target site using a limited set of neighboring reference station data

Mohammad Ali Ghorbani, Ravinesh C. Deo, Tek Narayan Maraseni, 2018 .

Hybrid multilayer perceptron-firefly optimizer algorithm for modelling photosynthetic active solar radiation for biofuel energy exploration

Zaher Mundher Yaseen, Ravinesh C. Deo, Harshna Goundar, 2021 .

Daily Flood Forecasts with Intelligent Data Analytic Models: Multivariate Empirical Mode Decomposition-Based Modeling Methods

Thong Nguyen-Huy, Ravinesh C. Deo, Sanjay Kumar Singh, 2020 .

Multi-stage hybridized online sequential extreme learning machine integrated with Markov Chain Monte Carlo copula-Bat algorithm for rainfall forecasting

Ravinesh C. Deo, Tek Narayan Maraseni, Nathan Downs, 2018, Atmospheric Research.

Soil moisture forecasting by a hybrid machine learning technique: ELM integrated with ensemble empirical mode decomposition

Yan Li, Ravinesh C. Deo, Tek Narayan Maraseni, 2018, Geoderma.

Wind speed forecasting in Nepal using self-organizing map-based online sequential extreme learning machine

Ravinesh C. Deo, Neelesh Sharma, 2021 .

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

Ravinesh C. Deo, Ananta Neupane, Mumtaz Ali, 2020 .

Hybrid Data Intelligent Models and Applications for Water Level Prediction

Zaher Mundher Yaseen, Hossein Bonakdari, Isa Ebtehaj, 2018 .

Predicting compressive strength of lightweight foamed concrete using extreme learning machine model

Zaher Mundher Yaseen, Sancho Salcedo-Sanz, Ravinesh C. Deo, 2018, Adv. Eng. Softw..

Optimization of Windspeed Prediction Using an Artificial Neural Network Compared With a Genetic Programming Model

Ravinesh C. Deo, Nathan Downs, Sujan Ghimire, 2018, Research Anthology on Multi-Industry Uses of Genetic Programming and Algorithms.

Modeling wheat yield with data-intelligent algorithms

Ravinesh C. Deo, Mumtaz Ali, R. Deo, 2020 .

Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network

Ravinesh C. Deo, Shahab Abdulla, Duc-Anh An-Vo, 2020, Energies.

Handbook of probabilistic models

Pijush Samui, Ravinesh C. Deo, Dieu Tien Bui, 2020 .

An efficient neuro-evolutionary hybrid modelling mechanism for the estimation of daily global solar radiation in the Sunshine State of Australia

Sancho Salcedo-Sanz, Ravinesh C. Deo, L. Cornejo-Bueno, 2018 .

Fractal dimension undirected correlation graph-based support vector machine model for identification of focal and non-focal electroencephalography signals

Ravinesh C. Deo, Mohammed Diykh, Shahab Abdulla, 2019, Biomed. Signal Process. Control..

Selection of representative feature training sets with self-organized maps for optimized time series modeling and prediction: application to forecasting daily drought conditions with ARIMA and neural network models

Yan Li, Ravinesh C. Deo, Tek Narayan Maraseni, 2018 .

Modern Artificial Intelligence Model Development for Undergraduate Student Performance Prediction: An Investigation on Engineering Mathematics Courses

Zaher Mundher Yaseen, Linda Galligan, Thong Nguyen-Huy, 2020, IEEE Access.

Designing Deep-Based Learning Flood Forecast Model With ConvLSTM Hybrid Algorithm

Ravinesh C. Deo, Shahab Abdulla, Nawin Raj, 2021, IEEE Access.

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

Ravinesh C. Deo, Shahab Abdulla, Nawin Raj, 2021 .

Modeling soil temperature using air temperature features in diverse climatic conditions with complementary machine learning models

Özgür Kisi, Zaher Mundher Yaseen, Ravinesh C. Deo, 2021, Comput. Electron. Agric..

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

Ravinesh C. Deo, Nawin Raj, Afshin Ghahramani, 2021, Remote. Sens..

A new framework for classification of multi-category hand grasps using EMG signals

Ravinesh C. Deo, Siuly Siuly, Firas Sabar Miften, 2021, Artif. Intell. Medicine.

Short-term flood forecasting using artificial neural networks, extreme learning machines, and M5 model tree

Ravinesh C. Deo, Mukesh K. Tiwari, Jan F. Adamowski, 2021, Advances in Streamflow Forecasting.

Deep multi-stage reference evapotranspiration forecasting model: Multivariate empirical mode decomposition integrated with Boruta-random forest algorithm

Ravinesh C. Deo, Nawin Raj, Afshin Ghahramani, 2021, IEEE Access.

Advanced extreme learning machines vs. deep learning models for peak wave energy period forecasting: A case study in Queensland, Australia

Fuyuan Xiao, Ravinesh C. Deo, Ramendra Prasad, 2021 .

Design and performance of two decomposition paradigms in forecasting daily solar radiation with evolutionary polynomial regression: wavelet transform versus ensemble empirical mode decomposition

Ravinesh C. Deo, Mohammad Rezaie-Balf, Sungwon Kim, 2021 .

Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River

Mohammad Ali Ghorbani, Ahmed El-Shafie, Ravinesh C. Deo, 2017, Environmental Earth Sciences.

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

Ravinesh C. Deo, Nathan Downs, Nawin Raj, 2018, Remote Sensing of Environment.

Experimental investigations of the effect of Reynolds number on a plane jet

Ravinesh C. Deo, Jianchun Mi, Graham J. Nathan, 2007 .

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

Ravinesh C. Deo, Nathan Downs, Nawin Raj, 2019, Journal of Cleaner Production.

Integrative stochastic model standardization with genetic algorithm for rainfall pattern forecasting in tropical and semi-arid environments

Ahmad Sharafati, Sinan Q. Salih, Zaher Mundher Yaseen, 2020 .

Improving SPI-derived drought forecasts incorporating synoptic-scale climate indices in multi-phase multivariate empirical mode decomposition model hybridized with simulated annealing and kernel ridge regression algorithms

Ravinesh C. Deo, Nathan J. Downs, Tek Maraseni, 2019, Journal of Hydrology.

Comparison of turbulent jets issuing from rectangular nozzles with and without sidewalls

Ravinesh C. Deo, Jianchun Mi, R. Deo, 2007 .

Adaptive Neuro-Fuzzy Inference System integrated with solar zenith angle for forecasting sub-tropical Photosynthetically Active Radiation

Ravinesh C. Deo, Alfio V. Parisi, Nathan J. Downs, 2018, Food and Energy Security.

Characterization of turbulent jets from high-aspect-ratio rectangular nozzles

Ravinesh C. Deo, Jianchun Mi, Graham J. Nathan, 2005 .

Drought prediction till 2100 under RCP 8.5 climate change scenarios for Korea

Ravinesh C. Deo, Bo-Ra Lee, R. Deo, 2015 .

A hybrid air quality early-warning framework: An hourly forecasting model with online sequential extreme learning machines and empirical mode decomposition algorithms.

Ravinesh C. Deo, Alfio V. Parisi, Ramendra Prasad, 2019, The Science of the total environment.

Land subsidence modelling using tree-based machine learning algorithms.

Ravinesh C. Deo, Omid Rahmati, Artemi Cerdà, 2019, The Science of the total environment.

Experimental investigations of the influence of Reynolds number and boundary conditions on a plane air jet

Ravinesh C. Deo, 2005 .

Copula-statistical precipitation forecasting model in Australia’s agro-ecological zones

Thong Nguyen-Huy, Ravinesh C. Deo, Shahjahan Khan, 2017 .

Application of the Artificial Neural Network model for prediction of monthly Standardized Precipitation and Evapotranspiration Index using hydrometeorological parameters and climate indices in eastern Australia

Ravinesh C. Deo, R. Deo, M. Şahin, 2015 .

A New Approach to Predict Daily pH in Rivers Based on the “à trous” Redundant Wavelet Transform Algorithm

Ravinesh C. Deo, Jan Adamowski, Taher Rajaee, 2018, Water, Air, & Soil Pollution.

Soil organic carbon in semiarid alpine regions: the spatial distribution, stock estimation, and environmental controls

Ravinesh C. Deo, Chengqi Zhang, Meng Zhu, 2019, Journal of Soils and Sediments.

Mapping heatwave vulnerability in Korea

Ravinesh C. Deo, Jong-Min Yeom, R. Deo, 2017, Natural Hazards.

A real-time hourly water index for flood risk monitoring: Pilot studies in Brisbane, Australia, and Dobong Observatory, South Korea

Ravinesh C. Deo, Jan F. Adamowski, R. Deo, 2018, Environmental Monitoring and Assessment.

Design and implementation of a hybrid MLP-GSA model with multi-layer perceptron-gravitational search algorithm for monthly lake water level forecasting

Mohammad Ali Ghorbani, Ravinesh C. Deo, Vahid Karimi, 2018, Stochastic Environmental Research and Risk Assessment.

Application of effective drought index for quantification of meteorological drought events: a case study in Australia

Ravinesh C. Deo, Jan Adamowski, R. Deo, 2017, Theoretical and Applied Climatology.

Forecasting Evaporative Loss by Least-Square Support-Vector Regression and Evaluation with Genetic Programming, Gaussian Process, and Minimax Probability Machine Regression: Case Study of Brisbane City

Pijush Samui, Ravinesh C. Deo, R. Deo, 2017 .

An ensemble-ANFIS based uncertainty assessment model for forecasting multi-scalar standardized precipitation index

Ravinesh C. Deo, Nathan Downs, Tek Narayan Maraseni, 2018, Atmospheric Research.

Non-tuned data intelligent model for soil temperature estimation: A new approach

Ozgur Kisi, Ravinesh C. Deo, Hadi Sanikhani, 2018, Geoderma.

Incorporating synoptic-scale climate signals for streamflow modelling over the Mediterranean region using machine learning models

Zaher Mundher Yaseen, Ozgur Kisi, Ravinesh C. Deo, 2019, Hydrological Sciences Journal.

Drought Modelling Based on Artificial Intelligence and Neural Network Algorithms: A Case Study in Queensland, Australia

Ravinesh C. Deo, Armando Apan, R. Deo, 2017 .

Novel approach for streamflow forecasting using a hybrid ANFIS-FFA model

Zaher Mundher Yaseen, Hossein Bonakdari, Isa Ebtehaj, 2017 .

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

Ravinesh C. Deo, Soukayna Mouatadid, Nawin Raj, 2018, Atmospheric Research.

Short-term electricity demand forecasting using machine learning methods enriched with ground-based climate and ECMWF Reanalysis atmospheric predictors in southeast Queensland, Australia

Ravinesh C. Deo, Jan Adamowski, Yan Li, 2019, Renewable and Sustainable Energy Reviews.

Future projection with an extreme-learning machine and support vector regression of reference evapotranspiration in a mountainous inland watershed in north-west China

Ravinesh C. Deo, Qi Feng, Jianhua Si, 2017 .

New Approach for Sediment Yield Forecasting with a Two-Phase Feedforward Neuron Network-Particle Swarm Optimization Model Integrated with the Gravitational Search Algorithm

Mohammad Ali Ghorbani, Ravinesh C. Deo, Sarita Gajbhiye Meshram, 2019, Water Resources Management.

Drought prediction with standardized precipitation and evapotranspiration index and support vector regression models

Sancho Salcedo-Sanz, Ravinesh C. Deo, B. Saavedra-Moreno, 2018 .

Exploring solar and wind energy resources in North Korea with COMS MI geostationary satellite data coupled with numerical weather prediction reanalysis variables

Kyung-Soo Han, Ravinesh C. Deo, Jong-Min Yeom, 2020 .

Development and Evaluation of Hybrid Artificial Neural Network Architectures for Modeling Spatio-Temporal Groundwater Fluctuations in a Complex Aquifer System

Ravinesh C. Deo, Madan K. Jha, Thendiyath Roshni, 2019, Water Resources Management.

Comparative Study of Hybrid-Wavelet Artificial Intelligence Models for Monthly Groundwater Depth Forecasting in Extreme Arid Regions, Northwest China

Ravinesh C. Deo, Haijiao Yu, Qi Feng, 2017, Water Resources Management.