A Tutorial on Best Practices and Pitfalls in Applying Machine Learning to Environmental Research

This research examines the integration of machine learning (ML) methods in environmental engineering and its environmental data analysis challenges and opportunities. It stresses the importance of incorporating the relevant domain knowledge at all steps of the ML processes to improve the scientific understanding of complicated environmental data. The project provides a detailed procedural guide for all major steps related to data cleaning, model building, model validation, and post hoc interpretability, aimed at ensuring that ML results in the environmental sciences are valid and trustworthy. The study also identifies and discusses common patterns that can complicate (or even sabotage) the data-driven model and its interpretation, specifically model qualitative issues and poor

methodological choices. The primary focus is on outlining the fundamental principles for creating ML models for environmental data analysis that are scientifically valid, methodologically sound, and environmentally relevant. The model provides a building block for environmental engineers on the effective application of ML to provide valid, relevant, and insightful predictive and analytical models that address environmental issues such as pollution and contaminant transport.

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