Water Quality Prediction Using Machine Learning Approaches

Water quality prediction has become pivotal for safeguarding aquatic ecosystems, ensuring public health and optimising management of water resources. Machine learning techniques offer a data‐driven alternative to traditional statistical and process‐based models, excelling at capturing nonlinear interactions among multiple water quality parameters. Advances in sensor technologies and the Internet of Things have generated large volumes of real-time environmental data, enabling the training of sophisticated algorithms. Models such as artificial neural networks, recurrent structures and convolutional architectures have been applied to forecast variables including dissolved oxygen, pH, nutrient concentrations and temperature. Hybrid strategies that combine feature extraction methods, signal decomposition or ensemble learning often outperform single-model approaches by reducing noise and improving generalisability. Globally, these developments support early warning systems for harmful algal blooms, adaptive control in wastewater treatment and precision management in aquaculture, illustrating the far-reaching impact of machine learning-based water quality prediction.

Research from Nature Portfolio

One foundational study has demonstrated a hybrid data-fusion approach for predicting dissolved oxygen in outdoor aquaculture ponds. By integrating radial basis function neural networks for sensor data fusion with an optimised support vector machine guided by an improved particle swarm algorithm, the model significantly enhanced prediction accuracy and robustness against sensor uncertainties. This hybrid system provided practitioners with a reliable tool for proactive management of oxygen levels in crab culture, reducing risk and enabling more sustainable operations.

Water Quality Prediction Using Machine Learning Approaches publication trend

The graph below shows the total number of articles in water quality prediction using machine learning approaches across all publications each year (not limited to Nature Index journals).

Technical terms

Artificial neural network (ANN): A computational model inspired by biological neural networks, comprising interconnected layers of nodes that learn complex patterns from data.

Long short-term memory (LSTM): A type of recurrent neural network designed to capture long-range temporal dependencies by using gated mechanisms to control information flow.

Convolutional neural network (CNN): A deep learning architecture that applies convolutional filters to extract spatial or temporal features from input data.

Wavelet transform (WT): A signal processing technique that decomposes time series into components at multiple frequency bands to isolate features and reduce noise.

Ensemble learning: A method of combining multiple predictive models to improve overall performance and robustness by leveraging complementary strengths.

Hybrid model: A predictive framework that integrates two or more methods—such as neural networks, optimisation algorithms and decomposition techniques—to enhance accuracy and generalisability.

References

1. Dissolved oxygen content prediction in crab culture using a hybrid intelligent method. Scientific Reports (2016).
2. A Hybrid Model for Water Quality Prediction Based on an Artificial Neural Network, Wavelet Transform, and Long Short-Term Memory. Water (2022).
3. Water Quality Prediction for Smart Aquaculture Using Hybrid Deep Learning Models. IEEE Access (2022).
4. Hybrid Machine Learning Ensemble Techniques for Modeling Dissolved Oxygen Concentration. IEEE Access (2020).