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.

Chemical incidents can be prevented or mitigated by improving safety performance and implementing the lessons learned from past incidents. Despite some limitations in the range of information they provide, chemical incident databases can be utilized as sources of lessons learned from incidents by evaluating patterns and relationships that exist between the data variables. Much of the previous research focused on studying the causal factors of incidents; hence, this research analyzes the chemical incidents from both the causal and consequence elements of the incidents. A subset of incidents data reported to the Hazardous Substance Emergency Events Surveillance (HSEES) chemical incident database from 2002-2006 was analyzed using data mining and text mining methodologies. Both methodologies were performed with the aid of STATISTICA software. The analysis studied 12,737 chemical process related incidents and extracted descriptions of incidents in free-text data format from 3,316 incident reports. The structured data was analyzed using data mining tools such as classification and regression trees, association rules, and cluster analysis. The unstructured data (textual data) was transformed into structured data using text mining, and subsequently analyzed further using data mining tools such as, feature selections and cluster analysis. The data mining analysis demonstrated that this technique can be used in estimating the incident severity based on input variables of release quantity and distance between victims and source of release. Using the subset data of ammonia release, the classification and regression tree produced 23 final nodes. Each of the final nodes corresponded to a range of release quantity and, of distance between victims and source of release. For each node, the severity of injury was estimated from the observed severity scores' average. The association rule identified the conditional probability for incidents involving piping, chlorine, ammonia, and benzene in the value of 0.19, 0.04, 0.12, and 0.04 respectively. The text mining was utilized successfully to generate elements of incidents that can be used in developing incident scenarios. Also, the research has identified information gaps in the HSEES database that can be improved to enhance future data analysis. The findings from data mining and text mining should then be used to modify or revise design, operation, emergency response planning or other management strategies.