There are several databases collecting information on various types of incidents, and
most analyses performed on these databases usually do not expand past basic trend
analysis or counting occurrences. This research uses the more robust methods of data
mining and text mining to analyze the Hazardous Substances Emergency Events
Surveillance (HSEES) system data by identifying relationships among variables,
predicting the occurrence of injuries, and assessing the value added by the text data. The
benefits of performing a thorough analysis of past incidents include better understanding
of safety performance, better understanding of how to focus efforts to reduce incidents,
and a better understanding of how people are affected by these incidents.
The results of this research showed that visually exploring the data via bar graphs did not
yield any noticeable patterns. Clustering the data identified groupings of categories
across the variable inputs such as manufacturing events resulting from intentional acts
like system startup and shutdown, performing maintenance, and improper dumping.
Text mining the data allowed for clustering the events and further description of the data,
however, these events were not noticeably distinct and drawing conclusions based on
these clusters was limited. Inclusion of the text comments to the overall analysis of
HSEES data greatly improved the predictive power of the models. Interpretation of the
textual data?s contribution was limited, however, the qualitative conclusions drawn were
similar to the model without textual data input. Although HSEES data is collected to
describe the effects hazardous substance releases/threatened releases have on people, a
fairly good predictive model was still obtained from the few variables identified as cause
related.
| Identifer | oai:union.ndltd.org:tamu.edu/oai:repository.tamu.edu:1969.1/ETD-TAMU-2008-08-35 |
| Date | 16 January 2010 |
| Creators | Veltman, Lisa M. |
| Contributors | Mannan, M. |
| Source Sets | Texas A and M University |
| Language | en_US |
| Detected Language | English |
| Type | Book, Thesis, Electronic Thesis |
| Format | application/pdf |
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