R
Ravinesh C. Deo
Researcher at University of Southern Queensland
Publications - 282
Citations - 11457
Ravinesh C. Deo is an academic researcher from University of Southern Queensland. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 47, co-authored 241 publications receiving 7315 citations. Previous affiliations of Ravinesh C. Deo include University of Adelaide & University of the South Pacific.
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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
TL;DR: The obtained results reveal that the EELM model is a robust expert model and can be embraced practically in real-life water resources management and river sustainability decisions.
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A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset
TL;DR: In this article, a wavelet-coupled support vector machine (W-SVM) model was adopted to forecast global incident solar radiation based on the sunshine hours (St), minimum temperature (Tmax), maximum temperature, Tmax, Tmin, E, P, and precipitation (P) as the predictor variables.
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Predicting compressive strength of lightweight foamed concrete using extreme learning machine model
Zaher Mundher Yaseen,Ravinesh C. Deo,Ameer A. Hilal,Abbas M. Abd,Laura Cornejo Bueno,Sancho Salcedo-Sanz,Moncef L. Nehdi +6 more
TL;DR: The results showed that the proposed ELM model achieved an adequate level of prediction accuracy, improving MARS, M5 Tree and SVR models, and could be employed as a reliable and accurate data intelligent approach for predicting the compressive strength of foamed concrete.
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Impacts of land use/land cover change on climate and future research priorities
Rezaul Mahmood,Roger A. Pielke,Kenneth G. Hubbard,Dev Niyogi,Gordon B. Bonan,Peter Lawrence,Richard T. McNider,Clive McAlpine,Andrés Etter,Samuel Gameda,Budong Qian,Andrew M. Carleton,Adriana Beltrán-Przekurat,Thomas N. Chase,Arturo I. Quintanar,Jimmy O. Adegoke,Sajith Vezhapparambu,Glen Conner,Salvi Asefi,Elif Sertel,David R. Legates,Yuling Wu,Robert Hale,Oliver W. Frauenfeld,Anthony Watts,Marshall Shepherd,Chandana Mitra,Valentine G. Anantharaj,Souleymane Fall,Robert Lund,Anna Treviño,Peter D. Blanken,Jinyang Du,Hsin I. Chang,Ronnie Leeper,Udaysankar S. Nair,Scott Dobler,Ravinesh C. Deo,Jozef Syktus +38 more
TL;DR: In this paper, several recommendations have been proposed for detecting land use and land cover change (LULCC) on the environment from, observed climatic records and to modeling to improve its understanding and its impacts on climate.
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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,Jan Adamowski,John Quilty,Ahmed El-Shafie +6 more
TL;DR: In this article, the potential of a relatively new data-driven method, namely the extreme learning machine (ELM) method, was explored for forecasting monthly stream-flow discharge rates in the Tigris River, Iraq.