What are the limits of machine learning in environmental research?

What are the limits of machine learning in environmental research?

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

Can you elaborate on the data quality issue?

Certainly! Data quality can be a major hurdle; 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 crucial because stakeholders need to understand how decisions are made. If a model is a 'black box', it can be hard to trust its outputs.

Are there any specific examples of these limitations in action?

Yes, for instance, in predicting climate change impacts, models may fail if they rely on outdated or limited datasets, leading to poor policy decisions.