Enhancing Surface Water Quality Parameter Prediction Using Deep Learning and Feature Augmentation Methods

Wang, Xianhe; Li, Ying; Qiao, Qian; Tavares, Adriano; Huang, Weidong; Liang, Yanchun

doi:10.3390/w17243523

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Open AccessArticle

Enhancing Surface Water Quality Parameter Prediction Using Deep Learning and Feature Augmentation Methods

by

Xianhe Wang

^1,2

,

Ying Li

^2,3,

Qian Qiao

¹,

Adriano Tavares

²,

Weidong Huang

¹ and

Yanchun Liang

^1,*

¹

School of Computer Science, Zhuhai College of Science and Technology, Zhuhai 519041, China

²

Department of Industrial Electronics, School of Engineering, University of Minho, 4704-553 Braga, Portugal

³

School of Life Sciences, Zhuhai College of Science and Technology, Zhuhai 519041, China

^*

Author to whom correspondence should be addressed.

Water 2025, 17(24), 3523; https://doi.org/10.3390/w17243523

Submission received: 1 November 2025 / Revised: 30 November 2025 / Accepted: 10 December 2025 / Published: 12 December 2025

(This article belongs to the Section New Sensors, New Technologies and Machine Learning in Water Sciences)

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Abstract

Water quality monitoring is crucial for public health and environmental protection, but traditional methods lack real-time accuracy. This study addresses this gap by combining feature augmentation methods (e.g., sliding window) with Principal Component Analysis (PCA) and a tailored Two-Layer Regularized Gated Recurrent Unit (TLR-GRU) model for efficient water quality prediction. Results demonstrate that the proposed framework significantly improves prediction accuracy, with R

^{2}

increased by 7.78%, RMSE decreased by 27.36%, MAE by 36.71%, and MAPE by 45.08%. This approach offers a novel technical pathway for real-time environmental monitoring and water resource management.

Keywords: water quality prediction; feature augmentation; deep learning; GRU

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MDPI and ACS Style

Wang, X.; Li, Y.; Qiao, Q.; Tavares, A.; Huang, W.; Liang, Y. Enhancing Surface Water Quality Parameter Prediction Using Deep Learning and Feature Augmentation Methods. Water 2025, 17, 3523. https://doi.org/10.3390/w17243523

AMA Style

Wang X, Li Y, Qiao Q, Tavares A, Huang W, Liang Y. Enhancing Surface Water Quality Parameter Prediction Using Deep Learning and Feature Augmentation Methods. Water. 2025; 17(24):3523. https://doi.org/10.3390/w17243523

Chicago/Turabian Style

Wang, Xianhe, Ying Li, Qian Qiao, Adriano Tavares, Weidong Huang, and Yanchun Liang. 2025. "Enhancing Surface Water Quality Parameter Prediction Using Deep Learning and Feature Augmentation Methods" Water 17, no. 24: 3523. https://doi.org/10.3390/w17243523

APA Style

Wang, X., Li, Y., Qiao, Q., Tavares, A., Huang, W., & Liang, Y. (2025). Enhancing Surface Water Quality Parameter Prediction Using Deep Learning and Feature Augmentation Methods. Water, 17(24), 3523. https://doi.org/10.3390/w17243523

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Enhancing Surface Water Quality Parameter Prediction Using Deep Learning and Feature Augmentation Methods

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