Design of an Emergency Prediction and Prevention Platform for Societal Security Decision Support Using Neural Networks

Abstract

Disasters, either naturally-occurring or man-made, frequently occur. For example, the recent chemical plant explosion on Nov. 13, 2005 at the Jilin Petrochemical Company caused a major environmental catastrophe in the water system of Songhua River. The explosion produced about 100 tonne of toxic chemicals, including benzene, spilled into the Songhua River and created an 80 km slick. The river contamination forced the shutdown of water supply in Harbin, a city located downstream with 3.8 million residents. The water pollution also brought problems to cities in China and Russia further downstream. Shortly afterwards, on Dec. 17, 2005, the City Central Hospital in Liaoyuan, Jilin Province, caught fire and left 39 people dead. In 2005, several serious coal mine disasters happened in China, which caused great losses in both life and economic assets. The lack of work safety and poor management has led to the high frequency of such coal mine accidents in recent years in China. In addition to the above disasters, there are other emergencies such as the Severe Acute Respiratory Syndrome (SARS), mad-cow disease and bird flu that caused world-wide attention and resulted in huge economic losses. Societal security has been a very important topic for civilians, governments, officials, and researchers as well.