Through our study of state-of-the-art methods, we now know better how to construct these to get the most accurate results with the least computational resources. This is not only true for pollution but for many different types of data, making our discovery very widely applicable.
Our work on including uncertainty in AI methods is also very general and can be used in many different situations. When it comes to pollution, this means that methods will have a much better understanding of how confident they should be about their predictions. We can, therefore, avoid having methods that claim that there is only very little pollution, when in fact there is a lot, just because they did not consider how precisely we knew their inputs.
Finally, we developed a new method that can not only model many different types of pollution at once, it can also find much more complicated correlations between these than previous methods, and take advantage of these correlations. For example, we can train the method to find correlations between previously measured pollution and new types of pollution. We can then predict how much of the new pollution there is anywhere we have measurements of the old type without having to go out and obtain new, expensive samples.
We also discovered, unfortunately, that it is not possible to use AI or any other statistical method to predict soil pollution based on the samples we are collecting today. We are simply taking soil samples too far apart, which has large societal implications, as we may miss hotspots of pollution. By drafting new guidelines for soil sampling based on the findings in this project, we hope to turn the policymakers' attention to this problem.
It may not be possible to use AI to predict soil pollution because of the current regulations, but the methods we developed are completely general and can be used for many other types of data. For instance, they can be used to predict air pollution or to help in modelling climate change.
