Lightning discharges in the atmosphere owe their existence to a combination of complex dynamic and microphysical processes. Knowledge discovery and data mining methods can be used to search for data characteristics and their teleconnections in complex data sets. Researchers have used machine learning techniques to successfully avoid lightning hazards near and far by looking at observations of meteorological parameters at a single location. The resulting alerts are validated using data from the lightning location system.
In addition to discussing the predictive ability of the model, data mining techniques are also used to compare data distribution patterns, both spatially and temporally between stations. The results prompt further analysis of how mining techniques can contribute to a better understanding of lightning dependencies on atmospheric parameters.
Source papers on this research were published in the journal: Nowcasting lightning occurrence from commonly available meteorological parameters using machine learning techniques