CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Non-linear input variable selection approach integrated with non-tuned data intelligence model for streamflow pattern simulation
Department of the Real Estate Development and Management, Ankara University, Ankara, Turkey.
Department of Physical Planning Development, Maitama Sule University Kano, Nigeria.
Department of Civil Engineering, College of Engineering, University of Diyala, Diyala Governorate, Iraq.
Institute of Research and Development, Duy Tan University, Da Nang, Vietnam.
Show others and affiliations
2019 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 141533-141548Article in journal (Refereed) Published
Abstract [en]

Streamflow modeling is considered as an essential component for water resources planning and management. There are numerous challenges related to streamflow prediction that are facing water resources engineers. These challenges due to the complex processes associated with several natural variables such as non-stationarity, non-linearity, and randomness. In this study, a new model is proposed to predict long-term streamflow. Several lags that cover several years are abstracted using the potential of Extreme Gradient Boosting (XGB) then after the selected inputs variables are imposed into the predictive model (i.e., Extreme Learning Machine (ELM)). The proposed model is compared with the stand-alone schema in which the optimum lags of the variables are supplied into the XGB and ELM models. Hydrological variables including rainfall, temperature and evapotranspiration are used to build the model and predict the streamflow at Goksu-Himmeti basin in Turkey. The results showed that XGB model performed an excellent result in which can be used for predicting the streamflow pattern. Also, it is clear from the attained results that the accuracy of the streamflow prediction using XGB technique could be improved when the high number of lags was used. However, the implementation of the XGB is tree-based technique in which several issues could be raised such as overfitting problem. The proposed schema XGBELM in which XGB approach is selected the correlated inputs and ranking them according to their importance; then after, the selected inputs are supplied into the ELM model for the prediction process. The XGBELM model outperformed the stand-alone schema of both XGB and ELM models and the high-lagged schema of the XGB. It is important to indicate that the XGBELM model found to improve the prediction ability with minimum variables number.

Place, publisher, year, edition, pages
USA: IEEE, 2019. Vol. 7, p. 141533-141548
Keywords [en]
Correlated variables, non-linear XGB approach, extreme learning machine, streamflow
National Category
Geotechnical Engineering
Research subject
Soil Mechanics
Identifiers
URN: urn:nbn:se:ltu:diva-76215DOI: 10.1109/ACCESS.2019.2943515OAI: oai:DiVA.org:ltu-76215DiVA, id: diva2:1357088
Note

Validerad;2019;Nivå 2;2019-10-09 (johcin)

Available from: 2019-10-02 Created: 2019-10-02 Last updated: 2019-10-09Bibliographically approved

Open Access in DiVA

fulltext(1956 kB)6 downloads
File information
File name FULLTEXT01.pdfFile size 1956 kBChecksum SHA-512
8f810ffbee755575cc23b3c7ec344b0f639fa78f37e2a95f0d926b443c96cf122735b936382c46db098828a1edbe22d0b5270d368f541a638da9ed5776872a46
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Authority records BETA

Al-Ansari, Nadhir

Search in DiVA

By author/editor
Al-Ansari, Nadhir
By organisation
Mining and Geotechnical Engineering
In the same journal
IEEE Access
Geotechnical Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 6 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 34 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf