World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
72
Citations
15175
World Ranking
936
National Ranking
58

Environmental Sciences

D-Index
71
Citations
15331
World Ranking
1623
National Ranking
75

Overview

What is he best known for?

The fields of study he is best known for:

  • Statistics
  • Ecology
  • Agriculture

His primary scientific interests are in Mean squared error, Extreme learning machine, Statistics, Climatology and Multivariate statistics. The concepts of his Mean squared error study are interwoven with issues in Convolutional neural network, Data mining and Pattern recognition. His Extreme learning machine research is classified as research in Artificial neural network.

His Wavelet research extends to Statistics, which is thematically connected. His Climatology research incorporates themes from Land cover, Climate extremes, Coefficient of determination and Decile. His study focuses on the intersection of Autoregressive integrated moving average and fields such as Linear regression with connections in the field of Meteorology and Algorithm.

His most cited work include:

  • 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 (235 citations)
  • Impacts of land use/land cover change on climate and future research priorities (183 citations)
  • A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset (176 citations)

What are the main themes of his work throughout his whole career to date?

Ravinesh C. Deo mainly investigates Mean squared error, Statistics, Artificial neural network, Artificial intelligence and Climatology. In his research, Solar energy is intimately related to Meteorology, which falls under the overarching field of Mean squared error. His work in the fields of Statistics, such as Correlation coefficient, Regression and Multivariate statistics, overlaps with other areas such as Mars Exploration Program.

His Artificial neural network study combines topics from a wide range of disciplines, such as Algorithm, Wavelet and Linear regression. Ravinesh C. Deo has researched Artificial intelligence in several fields, including Machine learning and Pattern recognition. His Climatology research incorporates elements of Global warming, Climate change and Precipitation.

He most often published in these fields:

  • Mean squared error (20.00%)
  • Statistics (18.43%)
  • Artificial neural network (16.08%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (13.73%)
  • Mean squared error (20.00%)
  • Artificial neural network (16.08%)

In recent papers he was focusing on the following fields of study:

His main research concerns Artificial intelligence, Mean squared error, Artificial neural network, Random forest and Statistics. His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Pattern recognition. The Mean squared error study combines topics in areas such as Tree, Hilbert–Huang transform, Coefficient of determination and Wavelet transform.

His Artificial neural network research includes themes of Wind power, Support vector machine, k-nearest neighbors algorithm, Solar energy and Renewable energy. His Random forest study integrates concerns from other disciplines, such as Wind speed, Streamflow, Computational intelligence and Water resources. The study incorporates disciplines such as Copula and Flood myth in addition to Statistics.

Between 2019 and 2021, his most popular works were:

  • Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia (28 citations)
  • Hybridized neural fuzzy ensembles for dust source modeling and prediction (20 citations)
  • Global Solar Radiation Estimation and Climatic Variability Analysis Using Extreme Learning Machine Based Predictive Model (18 citations)

In his most recent research, the most cited papers focused on:

  • Statistics
  • Ecology
  • Agriculture

Ravinesh C. Deo mainly focuses on Mean squared error, Artificial neural network, Random forest, Artificial intelligence and Hydrology. His Mean squared error study results in a more complete grasp of Statistics. His study in Artificial neural network is interdisciplinary in nature, drawing from both Industrial engineering, Conjugate gradient method and k-nearest neighbors algorithm.

Ravinesh C. Deo focuses mostly in the field of Random forest, narrowing it down to topics relating to Computational intelligence and, in certain cases, Stage, Global warming and Feature selection. His work on Drainage and Stormwater as part of general Hydrology research is frequently linked to Total suspended solids and Total phosphorus, bridging the gap between disciplines. His Machine learning study incorporates themes from Grid and Climate change.

Best Publications

  • 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;Sadeq Oleiwi Sulaiman;Ravinesh C. Deo;Kwok Wing Chau

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

    Zaher Mundher Yaseen;Ravinesh C. Deo;Ameer Hilal;Abbas M. Abd

  • Impacts of land use/land cover change on climate and future research priorities

    Rezaul Mahmood;Roger A. Pielke;Kenneth G. Hubbard;Dev Niyogi

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

    Sujan Ghimire;Ravinesh C. Deo;Nawin Raj;Jianchun Mi

  • A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset

    Ravinesh C. Deo;Xiaohu Wen;Feng Qi

  • Application of the extreme learning machine algorithm for the prediction of monthly Effective Drought Index in eastern Australia

    Ravinesh C. Deo;Mehmet Şahin

  • 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;Mehmet Şahin

  • Stream-flow forecasting using extreme learning machines: a case study in a semi-arid region in Iraq

    Zaher Mundher Yaseen;Othman Jaafar;Ravinesh C. Deo;Ozgur Kisi

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

    Mohanad S. Al-Musaylh;Ravinesh C. Deo;Ravinesh C. Deo;Jan Franklin Adamowski;Yan Li

  • Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model

    Ravinesh C Deo;Ozgur Kisi;Vijay P Singh

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

    Zaher Mundher Yaseen;Zaher Mundher Yaseen;Isa Ebtehaj;Hossein Bonakdari;Ravinesh C. Deo

  • Computational Intelligence Approaches for Energy Load Forecasting in Smart Energy Management Grids: State of the Art, Future Challenges, and Research Directions

    Seyedeh Narjes Fallah;Ravinesh Chand Deo;Mohammad Shojafar;Mauro Conti

  • Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks.

    Sujan Ghimire;Zaher Mundher Yaseen;Zaher Mundher Yaseen;Aitazaz A. Farooque;Ravinesh C. Deo

  • Forecasting effective drought index using a wavelet extreme learning machine (W-ELM) model

    Ravinesh C. Deo;Mukesh K. Tiwari;Jan F. Adamowski;John M. Quilty

  • Computational intelligence approach for modeling hydrogen production: a review

    Sina Faizollahzadeh Ardabili;Bahman Najafi;Shahaboddin Shamshirband;Behrouz Minaei Bidgoli

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

    Ramendra Prasad;Ravinesh C. Deo;Yan Li;Tek Maraseni

  • Forecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in Queensland

    Ravinesh C. Deo;Ravinesh C. Deo;Mehmet Şahin

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

    Louis Kouadio;Ravinesh C. Deo;Vivekananda Byrareddy;Jan Franklin Adamowski

  • Design and implementation of a hybrid model based on two-layer decomposition method coupled with extreme learning machines to support real-time environmental monitoring of water quality parameters.

    Elham Fijani;Rahim Barzegar;Rahim Barzegar;Ravinesh Deo;Evangelos Tziritis

  • Machine learning approaches for spatial modeling of agricultural droughts in the south-east region of Queensland Australia

    Omid Rahmati;Fatemeh Falah;Kavina Shaanu Dayal;Ravinesh C. Deo

  • Pan evaporation prediction using a hybrid multilayer perceptron-firefly algorithm (MLP-FFA) model: case study in North Iran

    M. A. Ghorbani;M. A. Ghorbani;Ravinesh C. Deo;Zaher Mundher Yaseen;Zaher Mundher Yaseen;Mahsa H. Kashani

  • A continent under stress: interactions, feedbacks and risks associated with impact of modified land cover on Australia's climate

    C. A. McAlpine;J. I. Syktus;J. G. Ryan;R. C. Deo

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

    Seyedeh Narjes Fallah;Ravinesh Chand Deo;Mohammad Shojafar;Mauro Conti

Frequent Co-Authors

Jan Adamowski
Jan Adamowski McGill University
Zaher Mundher Yaseen
Zaher Mundher Yaseen King Fahd University of Petroleum and Minerals
Jianchun Mi
Jianchun Mi Peking University
Tek Narayan Maraseni
Tek Narayan Maraseni University of Southern Queensland
Mumtaz Ali
Mumtaz Ali University of Southern Queensland
Graham J. Nathan
Graham J. Nathan University of Adelaide
Clive McAlpine
Clive McAlpine University of Queensland
Ahmed El-Shafie
Ahmed El-Shafie United Arab Emirates University
Ozgur Kisi
Ozgur Kisi Technical University of Applied Sciences Lübeck
Hamish A. McGowan
Hamish A. McGowan University of Queensland

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