Surface water sodium (Na+) concentration prediction using hybrid weighted exponential regression model with gradient-based optimization
Article
Article Title | Surface water sodium (Na+) concentration prediction using hybrid weighted exponential regression model with gradient-based optimization |
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ERA Journal ID | 5827 |
Article Category | Article |
Authors | Ahmadianfar, Iman, Shirvani-Hosseini, Seyedehelham, Samadi-Koucheksaraee, Arvin and Yaseen, Zaher Mundher |
Journal Title | Environmental Science and Pollution Research |
Journal Citation | 29 (35), pp. 53456-53481 |
Number of Pages | 26 |
Year | 2022 |
Publisher | Springer |
Place of Publication | Germany |
ISSN | 0944-1344 |
1614-7499 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s11356-022-19300-0 |
Web Address (URL) | https://link.springer.com/article/10.1007/s11356-022-19300-0 |
Abstract | Undeniably, there is a link between water resources and people’s lives and, consequently, economic development, which makes them vital in health and the environment. Proper water quality forecasting time series has a crucial role in giving on-time warnings for water pollution and supporting the decision-making of water resource management. The principal aim of this study is to develop a novel and cutting-edge ensemble data intelligence model named the weighted exponential regression and hybridized by gradient-based optimization (WER-GBO). Indeed, this is to reach more meticulous sodium (Na+) prediction monthly at Maroon River in the southwest of Iran. This developed model has advantages over other previous methodologies thanks to the following merits: (i) it can improve the performance and ability by mixing the outputs of four distinct data intelligence (DI) models, i.e., adaptive neuro-fuzzy inference system (ANFIS), least square support vector regression (LSSVM), Bayesian linear regression (BLR), and response surface regression (RSR); (ii) the proposed model can employ a Cauchy weighted function combined with an exponential-based regression model being optimized by GBO algorithm. To evaluate the performance of these models, diverse statistical indices and graphical assessment including error distributions, box plots, scatter-plots with confidence bounds and Taylor diagrams were conducted. According to obtained statistical metrics and verified validation procedures, the proposed WER-GBO resulted in promising accuracy compared to other models. Furthermore, the outcomes revealed the WER-GBO (R = 0.9712, RMSE = 0.639, and KGE = 0.948) reached more accurate and reliable results than other methods such as the ANFIS, LSSVM, BLR, and RSR for Na prediction in this study. Hence, the WER-GBO model can be considered a constructive technique to forecast the water quality parameters. |
Keywords | Water quality ; Wavelet transform ; Weighted exponential regression ; Gradient-based optimization ; Bayesian linear regression |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | Behbahan Khatam Alanbia University of Technology, Iran |
Islamic Azad University, Iran | |
School of Mathematics, Physics and Computing | |
Al-Ayen University, Iraq | |
MARA University of Technology, Malaysia |
https://research.usq.edu.au/item/z0182/surface-water-sodium-na-concentration-prediction-using-hybrid-weighted-exponential-regression-model-with-gradient-based-optimization
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