An Emergency Decision-Making Method for Coal Spontaneous Combustion Based on Improved Prospect Theory

Abstract

In response to the limited available information during the initial stages of coal spontaneous combustion and the influence of decision makers’ risk preferences on decision-making, this paper proposes an emergency decision-making method for coal spontaneous combustion that integrates grey correlation degree and TOPSIS with an enhanced prospect theory. Firstly, a normalized weighted evaluation matrix is established for the emergency response plan of coal spontaneous combustion, and the entropy method is utilized to determine the weights of various indexes. Then, considering the imperfect rationality of decision makers and their diverse individual risk preferences, they are categorized into three types: risk-seeking type, risk-neutral type, and risk-averse type. The corresponding risk coefficients are determined based on these different types. Positive and negative ideal solutions are taken as reference points, and matrices representing gains and losses are constructed. The grey correlation degree is introduced to calculate both positive and negative prospect values based on these matrices. Moreover, the prospect value for each emergency response plan is calculated, respectively, based on different types of decision makers, and the entropy method is used to assign weights to decision makers according to their respective risk preferences. Consequently, based on these prospect values and the weights, comprehensive prospect values for each emergency response plan are obtained and ranked to identify the optimal one. Finally, in order to validate the effectiveness of our proposed approach, a case study is conducted, and the results obtained from this case study are discussed and compared with those from other methods.

Prediction Model of Spontaneous Combustion of Lignite in Zhalainuoer Mining Area

“…Wang et al 42 proposed a model based on the sparrow search algorithm and convolutional neural network to predict the spontaneous combustion temperature of coal. Zeng et al 43 proposed a coal spontaneous combustion emergency decisionmaking method that integrates Grey Relational Analysis, Technique for Order Preference by Similarity to Ideal Solution, and Enhanced Prospective Theory. Identical gas indicators exhibit significant numerical differences across different coal mines due to variations in geological structure, mining environment, and coal composition, among other factors.…”

“…Wang et al 42 proposed a model based on the sparrow search algorithm and convolutional neural network to predict the spontaneous combustion temperature of coal. Zeng et al 43 proposed a coal spontaneous combustion emergency decision-making method that integrates Grey Relational Analysis, Technique for Order Preference by Similarity to Ideal Solution, and Enhanced Prospective Theory.…”