What are the limits of machine learning in environmental science?

What are the limits of machine learning in environmental science?

Machine learning has great potential in environmental science, but it faces limitations such as data quality, interpretability, and generalization.

Can you elaborate on the data quality issue?

Certainly! Data quality is crucial; if the data is biased, incomplete, or noisy, it can lead to inaccurate models and predictions.

What about interpretability? Why is it important?

Interpretability is important because stakeholders need to understand how decisions are made. Complex models can be 'black boxes', making it hard to trust their outputs.

Are there any specific examples of these limitations in practice?

Yes, for instance, in predicting species distribution, poor data can lead to incorrect habitat assessments, impacting conservation efforts.