Study on the construction and application of a community emergency capacity evaluation model based on a combined weighting-discrete Hopfield neural network

Abstract

To accurately evaluate community emergency capability and solve the problems with the existing index system, which has been declared unreasonable with respect to components, unclear in meaning and unreliable in terms of empowerment results caused by a single algorithm, a triangle model of community safety and resilience is introduced to construct an emergency capability index system suitable for communities with frequent geological disasters in Yunnan Province. Additionally, a combined empowerment model is constructed to improve the accuracy of the empowerment results. First, according to the relevant information and the results of expert consultations and field investigations, the index system is determined. Second, three weighting methods, the G1-entropy weight, G1-CRITIC and G1-coefficient of variation, are used to calculate the index weight and perform a comparative analysis, after which the best weighting method is selected and combined with the discrete Hopfield neural network model to construct a weighting-evaluation model. Finally, the model is applied to evaluate the emergency capability of a community with frequent geological disasters in Yunnan Province. The results show that the index of monitoring and early warning ability of geological disasters is added, which makes the index more targeted, and thus, the result obtained using the G1-CRITIC combination weighting model is more accurate. The evaluation model constructed in this paper accurately evaluates community emergency capacity and is further popularized and applied in communities with frequent geological disasters in Yunnan Province.