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.

中文翻译：

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基于连续间隔灰色离散Verhulst模型的地震灾后伤亡人数预测

地震灾害造成重大人员伤亡，因此对人员伤亡的预测有利于应急物资的合理有效分配，在应急救援中发挥着重要作用。在本文中，基于核和度量的连续间隔灰色离散Verhulst模型（CGDVM-KM）与以前的预测方法不同，可以帮助我们在很短的时间内有效地预测受伤人数。患病人数的“ S形”曲线。也就是说，通过区间白化法将连续的区间序列转换为核和具有相等信息量的测度序列，并与经典的灰色离散Verhulst模型相结合，然后与核和测度序列的灰色离散Verhulst模型相结合被介绍，分别。最后，开发了CGDVM-KM。它可以有效克服经典灰色Verhulst模型中参数估计的离散形式方程和仿真预测的连续形式方程所引起的系统误差，从而提高了预测精度。同时，通过实例验证了模型的合理性和有效性。与其他预测模型的比较表明，该模型在预测大规模地震灾害中的伤员方面具有较高的预测精度和较好的模拟效果。同时，通过实例验证了模型的合理性和有效性。与其他预测模型的比较表明，该模型在预测大规模地震灾害中的伤员方面具有较高的预测精度和较好的模拟效果。同时，通过实例验证了模型的合理性和有效性。与其他预测模型的比较表明，该模型在预测大规模地震灾害中的伤员方面具有较高的预测精度和较好的模拟效果。