A Comparison of Different Soft Computing Techniques for the Estimation of Suspended Sediment Load in Rivers
Research output: Contribution to journal › Conference article › Contributed › peer-review
Contributors
Abstract
Accurate assessment of suspended sediment load (SSL) in rivers plays a vital role in the planning and management of water resource structures. This study focused on the assessment of different techniques for the estimation of SSL in rivers. This comprises sediment rating curves (SRC) and soft computing techniques such as artificial neural networks (ANN), hybrid wavelet-coupled artificial neural networks (WANN), and local linear regression (LLR) models. These techniques were employed to estimate the daily SSL at Azad Pattan station of the Jhelum River in Pakistan. Further, the Gamma and M-test were performed to select the best-input variables and appropriate data length for smooth model development. By evaluating the outcomes of all the leading models, it can be concluded that the performance of soft computing models is superior to the SRC approach for the SSL estimation. This is because the soft computing models employed a non-linear approach for the data reconstruction. Additionally, the WANN was the most precise model to predict the SSL. Thus, WANN models are a powerful technique to reconstruct the SSL time series because they reveal the salient characteristics enclosed in the SSL time series.
Details
Original language | English |
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Pages (from-to) | 557-564 |
Number of pages | 8 |
Journal | Proceedings of the IAHR World Congress |
Volume | 2022 |
Publication status | Published - 2022 |
Peer-reviewed | Yes |
Conference
Title | 39th IAHR World Congress 2022 |
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Subtitle | From Snow to Sea |
Abbreviated title | IAHR 2022 |
Conference number | 39 |
Duration | 19 - 24 June 2022 |
Website | |
Degree of recognition | International event |
Location | Palacio de Congresos de Granada |
City | Granada |
Country | Spain |
External IDs
ORCID | /0000-0002-3729-0166/work/149081986 |
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ORCID | /0000-0001-6913-0354/work/151435729 |
Keywords
ASJC Scopus subject areas
Keywords
- Artificial neural networks, Gamma test, Local linear regression, Sediment rating curves, Wavelet analysis