Application of classical and novel integrated machine learning models to predict sediment discharge during free-flow flushing

Javadi, Fahime; Qaderi, Kourosh; Ahmadi, Mohammad Mehdi; Rahimpour, Majid; Madadi, Mohamad Reza; Mahdavi‐Meymand, Amin

doi:10.1038/s41598-022-23781-x

Cited by 3 publications

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Javadi et al [135] explored machine-learning models for sediment discharge prediction, including Linear Regression, Neural Network, Neuro-Fuzzy, and hybrid GMDH-SVR models. The models were optimized using Equilibrium Optimizer (EO) and HGSO algorithms.…”

Section: A Engineeringmentioning

confidence: 99%

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

El-Shorbagy,

Bouaouda,

Nabwey

et al. 2024

IEEE Access

View full text Add to dashboard Cite

The Henry Gas Solubility Optimization (HGSO) is a physics-based metaheuristic inspired by Henry's law, which describes the solubility of the gas in a liquid under specific pressure conditions. Since its introduction by Hashim et al. in 2019, HGSO has gained significant attention for its unique features, including minimal adaptive parameters and a balanced exploration-exploitation trade-off, leading to favorable convergence. This study provides an up-to-date survey of HGSO, covering the walk through the historical development of HGSO, its modifications, and hybridizations with other algorithms, showcasing its adaptability and potential for synergy. Recent variants of HGSO are categorized into modified, hybridized, and multi-objective versions, and the review explores its main applications, demonstrating its effectiveness in solving complex problems. The evaluation includes a discussion of the algorithm's strengths and weaknesses. This comprehensive review, featuring graphical and tabular comparisons, not only indicates potential future directions in the field but also serves as a valuable resource for researchers seeking a deep understanding of HGSO and its advanced versions. As physics-based metaheuristic algorithms gain prominence for solving intricate optimization problems, this study provides insights into the adaptability and applications of HGSO across diverse domains.

show abstract

Section: A Engineeringmentioning

confidence: 99%

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

El-Shorbagy,

Bouaouda,

Nabwey

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

Estimating the compressive strength of GGBFS-based concrete employing optimized regression analysis

Xiaozhen,

2023

IFS

View full text Add to dashboard Cite

Carbon dioxide is produced during the manufacture of normal Portland cement; however, this gas may be minimized by utilizing ground granulated blast furnace slag (GGBFS). When planning and constructing concrete buildings, compressive strength (f c ), a crucial component of concrete mixtures, is a need. It is essential to assess this GGBFS-blended concrete property precisely and consistently. The major objective of this research is to provide a practical approach for a comprehensive evaluation of machine learning algorithms in predicting the f c of concrete containing GGBFS. The research used the Equilibrium optimizer (EO) to enhance and accelerate the performance of the radial basis function (RBF) network (REO) and support vector regression (SVR) (SEO) analytical methodologies. The novelty of this work is particularly attributed to the application of the EO, the assessment of f c including GGBFS, the comparison with other studies, and the use of a huge dataset with several input components. The combined SEO and REO systems demonstrated proficient estimation abilities, as evidenced by coefficient of determination (R 2) values of 0.9946 and 0.9952 for the SEO’s training and testing components and 0.9857 and 0.9914 for the REO, respectively. The research identifies the SVR optimized with the EO algorithm as the most successful system for predicting the f c of GGBFS concrete. This finding has practical implications for the construction industry, as it offers a reliable method for estimating concrete properties and optimizing concrete mixtures.

show abstract

A Comparative Analysis of Sediment Concentration Using Artificial Intelligence and Empirical Equations

Khalid,

Ghumman,

Pasha

2024

Hydrology

View full text Add to dashboard Cite

Morphological changes in canals are greatly influenced by sediment load dynamics, whose estimation is a challenging task because of the non-linear behavior of the sediment concentration variables. This study aims to compare different techniques including Artificial Intelligence Models (AIM) and empirical equations for estimating sediment load in Upper Chenab Canal based on 10 years of sediment data from 2012 to 2022. The methodology involves utilization of a newly developed empirical equation, the Ackers and White formula and AIM including 20 neural networks with 10 training functions for both Double and Triple Layers, two Artificial Neuro-Fuzzy Inference System (ANFIS), Particle Swarm Optimization, and Ensemble Learning Random Forest models. Sensitivity analysis of sediment concentration variables has also been performed using various scenarios of input combinations in AIM. A state-of-the-art optimization technique has been used to identify the parameters of the empirical equation, and its performance is tested against AIM and the Ackers and White equation. To compare the performance of various models, four types of errors—correlation coefficient (R), T-Test, Analysis of Variance (ANOVA), and Taylor’s Diagram—have been used. The results of the study show successful application of Artificial Intelligence (AI) and empirical equations to capture the non-linear behavior of sediment concentration variables and indicate that, among all models, the ANFIS outperformed in simulating the total sediment load with a high R-value of 0.958. The performance of various models in simulating sediment concentration was assessed, with notable accuracy achieved by models AIM11 and AIM21. Moreover, the newly developed equation performed better (R = 0.92) compared to the Ackers and White formula (R = 0.88). In conclusion, the study provides valuable insights into sediment concentration dynamics in canals, highlighting the effectiveness of AI models and optimization techniques. It is suggested to incorporate other AI techniques and use multiple canals data in modeling for the future.

show abstract

Application of classical and novel integrated machine learning models to predict sediment discharge during free-flow flushing

Cited by 3 publications

References 28 publications

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

Advances in Henry Gas Solubility Optimization: A Physics-Inspired Metaheuristic Algorithm With Its Variants and Applications

Estimating the compressive strength of GGBFS-based concrete employing optimized regression analysis

A Comparative Analysis of Sediment Concentration Using Artificial Intelligence and Empirical Equations

Contact Info

Product

Resources

About