Abstract
Earthquake disaster causes serious casualties, so the prediction of casualties is conducive to the reasonable and efficient allocation of emergency relief materials, which plays a significant role in emergency rescue. In this paper, a continuous interval grey discrete Verhulst model based on kernels and measures (CGDVM-KM), different from the previous forecasting methods, can help us to efficiently predict the number of the wounded in a very short time, that is, an “S-shape” curve for the numbers of the sick and wounded. That is, the continuous interval sequence is converted into the kernel and measure sequences with equal information quantity by the interval whitening method, and it is combined with the classical grey discrete Verhulst model, and then the grey discrete Verhulst models of the kernel and measure sequences are presented, respectively. Finally, CGDVM-KM is developed. It can effectively overcome the systematic errors caused by the discrete form equation for parameter estimation and continuous form equation for simulation and prediction in classical grey Verhulst model, so as to improve the prediction accuracy. At the same time, the rationality and validity of the model are verified by examples. A comparison with other forecasting models shows that the model has higher prediction accuracy and better simulation effect in forecasting the wounded in massive earthquake disasters.
Highlights
In recent years, frequent earthquake disasters have occurred frequently all over the world
According to the characteristics of data, interval grey number whitening method and grey discrete Verhulst method are selected to construct continuous interval grey number discrete Verhulst prediction model based on kernels and measures (CGDVM-KM), which is used to simulate and forecast the number of the wounded in Lushan earthquake of Sichuan Province in China, and good results are obtained
In view of the characteristics of saturated “S-shape” and continuous change interval in the number of the wounded in the massive earthquake disaster, this paper analyses the selection of prediction methods and interval whitening method and proposes the continuous interval grey discrete Verhulst model based on kernels and measures (CGDVM-KM) to simulate and predict the number of the wounded in Lushan earthquake, Sichuan Province, in China
Summary
Received 19 November 2020; Revised 30 January 2021; Accepted 22 March 2021; Published 5 April 2021. Earthquake disaster causes serious casualties, so the prediction of casualties is conducive to the reasonable and efficient allocation of emergency relief materials, which plays a significant role in emergency rescue. A continuous interval grey discrete Verhulst model based on kernels and measures (CGDVM-KM), different from the previous forecasting methods, can help us to efficiently predict the number of the wounded in a very short time, that is, an “S-shape” curve for the numbers of the sick and wounded. It can effectively overcome the systematic errors caused by the discrete form equation for parameter estimation and continuous form equation for simulation and prediction in classical grey Verhulst model, so as to improve the prediction accuracy. A comparison with other forecasting models shows that the model has higher prediction accuracy and better simulation effect in forecasting the wounded in massive earthquake disasters
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