Lightning strike trend prediction with GBM in R

August 28, 2015

(This article was first published on R – SNAP Tech, and kindly contributed to R-bloggers)

Lightning activity is projected to increase with climate change. Lightning activity is interesting to model with stochastic gradient boosting (GBM: generalized boosted regression models/gradient boosting machine) in R. One use I have for this at SNAP is in the context of landscape fire modeling with SNAP’s ALFRESCO model. The simulations from the model can be enhanced by incorporating information about lightning strike activity over Alaska which varies both spatially and temporally.


The plot above reveals the upward projected trend in lightning strike frequency over Alaska, predominantly interior boreal forest in this case. A preliminary model suggests a 17% increase in lightning strikes per decade on average. More will be shared in a future update.

To leave a comment for the author, please follow the link and comment on their blog: R – SNAP Tech. offers daily e-mail updates about R news and tutorials on topics such as: Data science, Big Data, R jobs, visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics (regression, PCA, time series, trading) and more...

If you got this far, why not subscribe for updates from the site? Choose your flavor: e-mail, twitter, RSS, or facebook...

Comments are closed.


Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)