Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting
article; feasibility study; forecasting; genetic algorithm; radial basis function neural network; river; time series analysis
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
Nature Research
2023
|
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my.uniten.dspace-25143 |
|---|---|
| record_format |
dspace |
| spelling |
my.uniten.dspace-251432023-05-29T16:06:57Z Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting Afan H.A. Allawi M.F. El-Shafie A. Yaseen Z.M. Ahmed A.N. Malek M.A. Koting S.B. Salih S.Q. Mohtar W.H.M.W. Lai S.H. Sefelnasr A. Sherif M. El-Shafie A. 56436626600 57057678400 57207789882 56436206700 57214837520 55636320055 55839645200 57203978808 57215829072 36102664300 6505592467 7005414714 16068189400 article; feasibility study; forecasting; genetic algorithm; radial basis function neural network; river; time series analysis In nature, streamflow pattern is characterized with high non-linearity and non-stationarity. Developing an accurate forecasting model for a streamflow is highly essential for several applications in the field of water resources engineering. One of the main contributors for the modeling reliability is the optimization of the input variables to achieve an accurate forecasting model. The main step of modeling is the selection of the proper input combinations. Hence, developing an algorithm that can determine the optimal input combinations is crucial. This study introduces the Genetic algorithm (GA) for better input combination selection. Radial basis function neural network (RBFNN) is used for monthly streamflow time series forecasting due to its simplicity and effectiveness of integration with the selection algorithm. In this paper, the RBFNN was integrated with the Genetic algorithm (GA) for streamflow forecasting. The RBFNN-GA was applied to forecast streamflow at the High Aswan Dam on the Nile River. The results showed that the proposed model provided high accuracy. The GA algorithm can successfully determine effective input parameters in streamflow time series forecasting. � 2020, The Author(s). Final 2023-05-29T08:06:57Z 2023-05-29T08:06:57Z 2020 Article 10.1038/s41598-020-61355-x 2-s2.0-85082004774 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082004774&doi=10.1038%2fs41598-020-61355-x&partnerID=40&md5=eef3d942bed082e2de92783876a66731 https://irepository.uniten.edu.my/handle/123456789/25143 10 1 4684 All Open Access, Gold, Green Nature Research Scopus |
| institution |
Universiti Tenaga Nasional |
| building |
UNITEN Library |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Tenaga Nasional |
| content_source |
UNITEN Institutional Repository |
| url_provider |
http://dspace.uniten.edu.my/ |
| description |
article; feasibility study; forecasting; genetic algorithm; radial basis function neural network; river; time series analysis |
| author2 |
56436626600 |
| author_facet |
56436626600 Afan H.A. Allawi M.F. El-Shafie A. Yaseen Z.M. Ahmed A.N. Malek M.A. Koting S.B. Salih S.Q. Mohtar W.H.M.W. Lai S.H. Sefelnasr A. Sherif M. El-Shafie A. |
| format |
Article |
| author |
Afan H.A. Allawi M.F. El-Shafie A. Yaseen Z.M. Ahmed A.N. Malek M.A. Koting S.B. Salih S.Q. Mohtar W.H.M.W. Lai S.H. Sefelnasr A. Sherif M. El-Shafie A. |
| spellingShingle |
Afan H.A. Allawi M.F. El-Shafie A. Yaseen Z.M. Ahmed A.N. Malek M.A. Koting S.B. Salih S.Q. Mohtar W.H.M.W. Lai S.H. Sefelnasr A. Sherif M. El-Shafie A. Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting |
| author_sort |
Afan H.A. |
| title |
Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting |
| title_short |
Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting |
| title_full |
Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting |
| title_fullStr |
Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting |
| title_full_unstemmed |
Input attributes optimization using the feasibility of genetic nature inspired algorithm: Application of river flow forecasting |
| title_sort |
input attributes optimization using the feasibility of genetic nature inspired algorithm: application of river flow forecasting |
| publisher |
Nature Research |
| publishDate |
2023 |
| _version_ |
1806428007771930624 |
| score |
13.222552 |