RT Journal Article
T1 Advancements in water quality prediction: a practical review of machine learning and deep learning approaches
A1 Helaly, Marwah A.
A1 Rady, Sherine
A1 Mabrouk, Mohamed
A1 Aref, Mostafa M.
A1 Villarroya Fernández, Sebastián
A1 Cotos Yáñez, José Manuel
A1 Mera Pérez, David
K1 Water quality prediction
K1 Deep learning
K1 Machine learning
K1 Data imputation
K1 Convolutional neural network
K1 Recurrent neural network
AB Water quality plays a pivotal role in ensuring the safety and sustainability of water resources, with significant implications for environmental protection, public health, and various industrial applications. This paper presents both a review of related state-of-the-art works and an implementation and application of adapted versions of these related works for predicting water quality parameters on a new water dataset from Galicia, Spain. The reviewed studies encompass a range of predictive models applied to diverse water quality parameters, including dissolved oxygen levels, pH levels, and other complex water parameters. These models include various machine learning and deep learning methods such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Bidirectional LSTMs. This research contributes by implementing various models on the dataset and experimentally demonstrating the impact of key factors on model performance. These factors include model sophistication, imputation techniques, recurrent architectures, and customized approaches for water quality prediction using deep learning. Notably, K-Nearest Neighbors (KNN) imputation enhances performance by preserving local data relationships, while noise filtering further improves predictive accuracy. Additionally, we observe that smaller batch sizes and learning rates lead to better generalization in sparse datasets, outperforming traditional approaches. The conclusions are guided by comparing the performance of all models on the Galician dataset using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Determination (R2 ). This paper provides the first DL-based water quality analysis for Galicia, emphasizing the need for regional model adaptation. Our results guide future research directions, including the exploration of Transformer-based architectures for time-series data, more sophisticated feature selection techniques, and neural-network-based imputation strategies to enhance data completeness.
PB Springer
SN 1386-7857
YR 2025
FD 2025-08-30
LK https://hdl.handle.net/10347/43312
UL https://hdl.handle.net/10347/43312
LA eng
NO A. Helaly, M., Rady, S., Mabrouk, M. et al. Advancements in water quality prediction: a practical review of machine learning and deep learning approaches. Cluster Comput 28, 598 (2025). https://doi.org/10.1007/s10586-025-05221-3
NO Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).
DS Minerva
RD 28 abr 2026