Prediction of Fire Smoke Concentration Based on Grey-Markov Model
Rongrong Wang, Wei Song, Lizhen Liu, Chao Du, Xinlei Shelly Zhao · 2019
Grey theory as a system theory has been widely applied to different disciplines, different areas of research, the theory find the law from the data whose change is not obvious, and through the law to analyze the data's change and development, but for data sequence which is very random, the predicted variation will increase; The Markov chain is based on the transition probability between the different states of the system to predict the development trend of the system. It is related to the current state of the system and has nothing to do with the system's past state. Applicable to the prediction of the larger volatility series. The Grey-Markov prediction model combining the grey theory `advantages with the Markov prediction model' advantages, which not only preserves the overall trend of the data, but also reduces the error caused by the data with large fluctuations, improving the prediction accuracy. The concentration of smoke in the fire is influenced by many factors, and its randomness and volatility are large. The Grey-Markov prediction model is used to predict it, and the data sequence with high fitting degree can be obtained.