Artificial Intelligence Applications in Water Quality Prediction

Artificial intelligence has emerged as a transformative tool in water quality prediction, offering data-driven models capable of capturing complex, nonlinear relationships among physicochemical, hydrological and environmental variables. Techniques such as artificial neural networks, support vector machines and adaptive neuro-fuzzy inference systems have been applied to forecast parameters including electrical conductivity, chlorophyll a, dissolved oxygen and turbidity across rivers, lakes and coastal waters. Ensemble learning approaches – combining multiple algorithms via bagging, boosting or random subspace methods – have enhanced robustness and reduced uncertainty in multi-day forecasts. Wavelet decomposition and hybrid metaheuristic optimisers, such as particle swarm and differential evolution algorithms, are increasingly integrated to pre-process time series data and fine-tune model parameters, further improving accuracy and generalisability. These advances enable early warning systems for water pollution events, support resource management in irrigation and drinking supply contexts and inform policy decisions on ecosystem health. Despite challenges in data availability, heterogeneity and interpretability, AI-driven predictions are reshaping monitoring strategies globally and paving the way for real-time, scalable water quality assessment frameworks.

Research from Nature Portfolio

Recent studies have adopted an adaptive neuro-fuzzy inference system coupled with conjoined metaheuristic optimisers to predict electrical conductivity, a key index of water mineralisation. The integration of particle swarm and differential evolution algorithms enhanced both global and local search mechanisms, while wavelet analysis decomposed the original time series into sub-components, increasing prediction certainty. The resulting hybrid model achieved superior correlation and reduced root mean squared error compared to standalone approaches, demonstrating the potential of combined evolutionary and fuzzy logic techniques for reliable water quality forecasting.

Artificial Intelligence Applications in Water Quality Prediction publication trend

The graph below shows the total number of articles in artificial intelligence applications in water quality prediction 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 that uses interconnected nodes to capture complex nonlinear relationships in data.

Adaptive Neuro-Fuzzy Inference System (ANFIS): A hybrid modelling approach combining neural networks and fuzzy logic to infer relationships and handle uncertainty in prediction tasks.

Ensemble Learning: A methodology that combines multiple prediction models, such as bagging or boosting, to improve overall forecast accuracy and stability.

Wavelet Transform: A signal processing technique that decomposes time series data into multi-resolution components for enhanced feature extraction and noise reduction.

Particle Swarm Optimization (PSO): A metaheuristic algorithm that simulates social behaviour to optimise model parameters through collective exploration of the search space.

Least-Squares Boosting (Lsboost): An iterative ensemble technique that sequentially fits weak learners to the residuals of previous models, minimising squared errors.

References

1. Ensemble models with uncertainty analysis for multi-day ahead forecasting of chlorophyll a concentration in coastal waters. Engineering Applications of Computational Fluid Mechanics (2018).
2. Modeling the organic matter of water using the decision tree coupled with bootstrap aggregated and least-squares boosting. Environmental Technology & Innovation (2022).
3. An improved adaptive neuro fuzzy inference system model using conjoined metaheuristic algorithms for electrical conductivity prediction. Scientific Reports (2022).
4. Comparative Assessment of Individual and Ensemble Machine Learning Models for Efficient Analysis of River Water Quality. Sustainability (2022).