Re: More training data needed then
ML/AI is like curve fitting. If you have 2 data points, it's pointless fitting an order-100 polynomial to it. In contrast, linear fitting *can* work for 1000 data points, but you are possibly over-simplifying your solution too much.
The data has to be sufficiently modellable with the features offered to the AI (even if that model is unknown). This team are attempting to predict the size of the fire from only two variables (which is slightly at odds with the article statement of "..built a decision tree algorithm to predict how big a forest fire will grow given the time of day, weather conditions, and local vegetation"), which are quite possibly the only two that can be reliably extracted from historic data, but given their accuracy of only 50%, there is clearly more at play in the underlying model (and even then, the chaotic nature of the process may prove limiting)
Overall their model boils down to:
(a): if VPD < 0.66 kPa, goto (b) else goto (c)
(b): if VPD < 0.56 kPa, it's "small", else "medium"
(c): if spruce fraction < 0.04 it's "small", else "large"
Interestingly, the abstract of the paper says that they tried more complicated models and they were less accurate, and rather interestingly just using the Vapour Pressure Deficit was 49.2% accurate, and just using Relative Humidity was 47.2% accurate. They probably know a boat-load more about forest fires than I do.