Assessing Lightning and Wild Fire Hazard by Land Properties and Cloud to Ground Lightning Data with Association Rule Mining over Alberta, Canada
Abstract
Characteristics of Cloud to Ground (CG) lightning over Alberta, Canada were investigated by using 2010-2016 lightning data with data mining methods. The hotspot analysis was implemented to find the regions with high frequency CG lightning strikes clustered together. Generally, hotspot regions are located in central, central east and south central regions of the study regions. About 94% of annual lightning occurred in warm months (June to August) and the daily lightning frequency was influenced by diurnal heating cycle. The CG lightning frequency associated with land properties was investigated by measuring preference index (PI). The association rule mining technique was used to investigate frequent CG lightning patterns, which were verified by similarity measurement to check the patterns’ consistency. The verification of CG lightning hazard map generated with 2010-2014 data was carried out by comparing it to unprocessed raw CG lightning data from 2015-2016. The similarity coefficient values indicated that there were high correlations throughout the entire study period. The actual CG lightning generally occurred more frequently in higher risky regions in the lightning hazard map. Most wild fire (around 93%) in Alberta occurred in forests, wetland forests and wetland shrub areas. It was also found that lightning and wild fire occur in two distinct areas: frequent wild fire region with a high frequency of lightning, and frequent wild fire region with a low frequency of lightning. Further, preference index (PI) revealed locations where the wild fires occurred more frequently than in other class regions. As one of the potential applications of this research, the wild fire hazard area was estimated with the CG lightning hazard map and specific land use types.
Description
Keywords
Engineering--Environmental
Citation
Cha, D. (2017). Assessing Lightning and Wild Fire Hazard by Land Properties and Cloud to Ground Lightning Data with Association Rule Mining over Alberta, Canada (Master's thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/25018