Hostname: page-component-cb9f654ff-h4f6x Total loading time: 0 Render date: 2025-09-07T05:23:03.564Z Has data issue: false hasContentIssue false
  • English
  • Français

STREAMFLOW AND SOIL MOISTURE FORECASTING WITH HYBRID DATA INTELLIGENT MACHINE LEARNING APPROACHES: CASE STUDIES IN THE AUSTRALIAN MURRAY–DARLING BASIN

Part of: Artificial intelligence (68Txx)

Published online by Cambridge University Press:  15 August 2019

RAMENDRA PRASAD Open the ORCID record for RAMENDRA PRASAD [Opens in a new window]
RAMENDRA PRASAD*
Affiliation:
Environmental Modelling and Simulation Group, School of Agricultural, Computational and Environmental Sciences, University of Southern Queensland, Queensland, Australia email ramendrap23@gmail.com
Rights & Permissions [Opens in a new window]

Abstract

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'

Keywords

soil moisturestreamflowforecastingmachine learningartificial intelligenceANNrandom forestEEMDCEEMDANnonstationaritystochastic datafeature selection

MSC classification

Primary: 68T99: None of the above, but in this section

Information

Type
Abstracts of Australasian PhD Theses
Information
Bulletin of the Australian Mathematical Society , Volume 100 , Issue 3 , December 2019 , pp. 520 - 522
Copyright
© 2019 Australian Mathematical Publishing Association Inc. 

Footnotes

Thesis submitted to the University of Southern Queensland in July 2018, degree awarded on 14 December 2018; principal supervisor Ravinesh Deo, associate supervisors Yan Li and Tek Moraseni.

References

Prasad, R., Deo, R. C., Li, Y. and Maraseni, T., ‘Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm’, Atmos. Res. 197 (2017), 42–63.Google Scholar
Prasad, R., Deo, R. C., Li, Y. and Maraseni, T., ‘Ensemble committee-based data intelligent approach for generating soil moisture forecasts with multivariate hydro-meteorological predictors’, Soil Tillage Res. 181 (2018), 63–81.Google Scholar
Prasad, R., Deo, R. C., Li, Y. and Maraseni, T., ‘Soil moisture forecasting by a hybrid machine learning technique: ELM integrated with ensemble empirical mode decomposition’, Geoderma 330 (2018), 136–161.Google Scholar
Prasad, R., Deo, R. C., Li, Y. and Maraseni, T., ‘Weekly soil moisture forecasting with multivariate sequential, ensemble empirical mode decomposition and Boruta-random forest hybridizer algorithm approach’, Catena 177 (2019), 149–166.Google Scholar