Machine learning in wastewater: opportunities and challenges - "not everything is a nail!"

Area of Science:

• Environmental Engineering
• Computer Science
• Data Science

Background:

• Machine learning (ML) presents significant opportunities for advancing wastewater treatment processes.
• The adoption of ML in this field is often hindered by the complexity of models and data challenges.
• There is a need for practical guidelines to effectively implement ML in wastewater management.

Purpose of the Study:

• To explore the potential applications and key considerations of machine learning in wastewater treatment.
• To emphasize the importance of model simplicity, interpretability, and trustworthiness in ML applications.
• To highlight the critical role of data quality and metadata in the successful deployment of ML models.

Main Methods:

• Review of current machine learning applications in wastewater treatment.
• Analysis of the trade-offs between model complexity and interpretability.
• Discussion on data requirements, including metadata collection and management.

Main Results:

• Machine learning models can be highly beneficial for wastewater applications, but their complexity must be carefully managed.
• Overly complex 'black box' models are discouraged, especially when data is limited, due to potential issues with interpretability and trust.
• High-quality data collection and comprehensive metadata are essential for reliable ML model performance.

Conclusions:

• Simplicity and interpretability should be prioritized in machine learning models for wastewater applications to ensure trustworthiness.
• Addressing data scarcity and establishing best practices for data and model management are crucial for broader ML adoption.
• Future research should focus on developing benchmark hybrid models to enhance the skills of environmental engineers in utilizing ML tools effectively.