Data driven multi-stage transformer based framework for intelligent water quality monitoring

Area of Science:

• Environmental Science
• Computer Science
• Data Science

Background:

• Access to clean water is a critical global challenge, with Sustainable Development Goal 6 (SDG 6) aiming for universal access to water and sanitation.
• Wastewater Treatment Plants (WWTPs) require advanced tools for monitoring and predicting water quality to ensure effective management and compliance.
• Limited training data is a significant hurdle in developing robust predictive models for WWTPs.

Purpose of the Study:

• To develop and evaluate a deep learning framework based on Transformer architectures for accurate wastewater quality forecasting.
• To address data limitations in WWTPs using generative models and enhance data interpretability through anomaly detection.
• To compare the performance of various Transformer-based models and ensemble methods for predicting water quality parameters.

Main Methods:

• Introduced TransGAN, a Transformer-driven Generative Adversarial Network, for synthetic tabular data generation in WWTPs.
• Proposed TransAuto, a Transformer Autoencoder, for anomaly detection and feature identification in multivariate time series data.
• Evaluated Transformer-based models including Time Series Transformer (TST), TimeGPT, and an ensemble of Informer, Autoformer, and FEDformer for wastewater quality prediction.

Main Results:

• The Time Series Transformer (TST) achieved a Mean Squared Error (MSE) of 0.0028 and an R-squared (R²) of 0.9643.
• An ensemble model combining Informer, Autoformer, and FEDformer demonstrated strong performance with MSE of 0.0036, Root Mean Squared Error (RMSE) of 0.0582, Mean Absolute Error (MAE) of 0.0438, and R² of 0.9646.
• Transformer-based models proved effective in capturing complex temporal dynamics and accurately forecasting wastewater quality parameters.

Conclusions:

• Transformer-based deep learning models offer a powerful and accurate solution for wastewater quality forecasting.
• The developed framework, including data augmentation and anomaly detection, supports improved water resource management and compliance with SDG 6.
• These findings encourage the adoption of advanced AI techniques in real-world applications for sustainable water management.