Machine Learning: New Ideas and Tools in Environmental Science and Engineering.

Area of Science:

• Environmental Science and Engineering (ESE)
• Data Science
• Machine Learning (ML)

Background:

• Exponential growth in ESE data necessitates advanced analytical techniques.
• Conventional methods struggle with the complexity and volume of modern ESE datasets.
• Machine learning (ML) presents a promising alternative for uncovering hidden patterns and correlations.

Purpose of the Study:

• To explore the transformative potential of ML in ESE data analysis and modeling.
• To provide essential knowledge for researchers applying ML in the ESE field.
• To identify current challenges and future opportunities for ML adoption in ESE.

Main Methods:

• Illustrative examples demonstrating ML's application to complex ESE problems.
• Categorization of ML applications in ESE into four key areas: prediction, feature importance, anomaly detection, and material/chemical discovery.
• Discussion of essential knowledge, including model development, interpretation, and applicability analysis.

Main Results:

• ML effectively addresses complex ESE challenges, surpassing conventional analytical limitations.
• Key ML applications identified: predictive modeling, feature importance extraction, anomaly detection, and discovery of new materials/chemicals.
• Critical components for successful ML implementation in ESE include correct model development, proper interpretation, and sound applicability analysis.

Conclusions:

• ML is an indispensable tool for advancing data analysis and modeling in environmental science and engineering.
• Addressing current shortcomings in ML application, particularly in model development, interpretation, and applicability, is crucial.
• Significant future opportunities exist for leveraging ML to tackle pressing ESE issues.