Modeling of the cascading impacts of drought and forest fire based on a Bayesian network

Abstract

The cascading impact of disastrous events has become an important focus of disaster research. In recent years, forest fires have occurred frequently in China, causing huge economic losses. Studying the cascading impacts of drought and forest fire is of great significance for reducing disaster risks. Taking Yunnan Province of China as an example, meteorological data and forest fire point data from 2005 to 2018 were collected and statistically analyzed. A Bayesian network model of the cascading impacts of drought and forest fire was established, enabling the prior probability and conditional probability of nodes to be determined. Based on this information, a probability prediction was established using causal reasoning. Finally, in the case test, the Brier score was used to test the accuracy of the model. The Brier test value was 0.305, which was less than the qualified threshold of 0.6. The results indicated that the Bayesian network model established in this study had a good prediction performance, which was basically consistent with the facts. The results provide an insight into the mechanism by which drought induced forest fires occur and will be of use in forest fire prevention work.