Prediction of Dye Removal Using Machine Learning Techniques

Dilay Bozdağ Ak; İhsan Hakan Selvi

doi:10.35377/saucis...1697738

EN

Prediction of Dye Removal Using Machine Learning Techniques

Abstract

This study aims to predict the removal efficiency of methylene blue dye using experimental data collected from adsorption processes involving acorn-based biosorbents. A comparative evaluation of four machine learning algorithms (Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Random Forest, and XGBoost) was conducted to determine the most suitable modeling approach. Two ANN architectures, with single and dual hidden layers respectively, achieved the highest predictive accuracy, with R² values of 0.93 and 0.87. While XGBoost demonstrated better performance (R² = 0.64) than Random Forest (R² = 0.61), both ensemble models provided moderately accurate predictions. In contrast, the LSTM model performed poorly (R² = 0.44), likely due to the non-sequential structure of the dataset. These findings underscore the potential of ANN-based models for accurately capturing nonlinear relationships in adsorption systems and also demonstrate the viability of alternative ensemble learning methods for predictive environmental modeling.

Keywords

References

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Details

Primary Language

English

Subjects

Software Engineering (Other)

Journal Section

Research Article

Authors

Dilay Bozdağ Ak ^*
0009-0008-9909-6140
Türkiye

İhsan Hakan Selvi
0000-0002-8837-2137
Türkiye

Early Pub Date

September 26, 2025

Publication Date

September 30, 2025

Submission Date

May 12, 2025

Acceptance Date

August 11, 2025

Published in Issue

Year 2025 Volume: 8 Number: 3

DOI

https://doi.org/10.35377/saucis...1697738

IZ

https://izlik.org/JA82AM82RU

Cite

RIS / Bibtex

APA

Bozdağ Ak, D., & Selvi, İ. H. (2025). Prediction of Dye Removal Using Machine Learning Techniques. Sakarya University Journal of Computer and Information Sciences, 8(3), 496-509. https://doi.org/10.35377/saucis...1697738

AMA

1.Bozdağ Ak D, Selvi İH. Prediction of Dye Removal Using Machine Learning Techniques. SAUCIS. 2025;8(3):496-509. doi:10.35377/saucis.1697738

Chicago

Bozdağ Ak, Dilay, and İhsan Hakan Selvi. 2025. “Prediction of Dye Removal Using Machine Learning Techniques”. Sakarya University Journal of Computer and Information Sciences 8 (3): 496-509. https://doi.org/10.35377/saucis. 1697738.

EndNote

Bozdağ Ak D, Selvi İH (September 1, 2025) Prediction of Dye Removal Using Machine Learning Techniques. Sakarya University Journal of Computer and Information Sciences 8 3 496–509.

IEEE

[1]D. Bozdağ Ak and İ. H. Selvi, “Prediction of Dye Removal Using Machine Learning Techniques”, SAUCIS, vol. 8, no. 3, pp. 496–509, Sept. 2025, doi: 10.35377/saucis...1697738.

ISNAD

Bozdağ Ak, Dilay - Selvi, İhsan Hakan. “Prediction of Dye Removal Using Machine Learning Techniques”. Sakarya University Journal of Computer and Information Sciences 8/3 (September 1, 2025): 496-509. https://doi.org/10.35377/saucis. 1697738.

JAMA

1.Bozdağ Ak D, Selvi İH. Prediction of Dye Removal Using Machine Learning Techniques. SAUCIS. 2025;8:496–509.

MLA

Bozdağ Ak, Dilay, and İhsan Hakan Selvi. “Prediction of Dye Removal Using Machine Learning Techniques”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 3, Sept. 2025, pp. 496-09, doi:10.35377/saucis. 1697738.

Vancouver

1.Dilay Bozdağ Ak, İhsan Hakan Selvi. Prediction of Dye Removal Using Machine Learning Techniques. SAUCIS. 2025 Sep. 1;8(3):496-509. doi:10.35377/saucis. 1697738