Surface water sodium (Na+) concentration prediction using hybrid weighted exponential regression model with gradient-based optimization

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 deci...

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Veröffentlicht in:Environmental science and pollution research international Jg. 29; H. 35; S. 53456 - 53481
Hauptverfasser: Ahmadianfar, Iman, Shirvani-Hosseini, Seyedehelham, Samadi-Koucheksaraee, Arvin, Yaseen, Zaher Mundher
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.07.2022
Springer Nature B.V
Schlagworte:
Adaptive systems
Algorithms
Aquatic Pollution
Artificial neural networks
Atmospheric Protection/Air Quality Control/Air Pollution
Bayesian analysis
Bayesian theory
Concentration gradient
Decision making
Earth and Environmental Science
Economic development
Ecotoxicology
Environment
Environmental Chemistry
Environmental Health
Environmental science
Fuzzy logic
Intelligence
Iran
Mathematical models
Optimization
Performance enhancement
Performance evaluation
prediction
Predictions
regression analysis
Regression models
Research Article
Resource management
rivers
Sodium
Statistical analysis
Support vector machines
Surface water
time series analysis
Waste Water Technology
Water Management
Water pollution
Water Pollution Control
Water quality
Water resources
Water resources management
Iran
Bayesian linear regression
Wavelet transform
Weighted exponential regression
Gradient-based optimization
Water quality
ISSN:0944-1344, 1614-7499, 1614-7499
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Zusammenfassung: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.
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ISSN:0944-1344
1614-7499
1614-7499
DOI:10.1007/s11356-022-19300-0