Demand Prediction of Emergency Supplies under Fuzzy and Missing Partial Data

Abstract

An accurate demand prediction of emergency supplies according to disaster information and historical data is an important research subject in emergency rescue. This study aims at improving supplies demand prediction accuracy under partial data fuzziness and missing. The main contributions of this study are summarized as follows. (1) In view that it is difficult for the turning point of the whitenization weight function to determine fuzzy data, two computational formulas solving “core” of fuzzy interval grey numbers were proposed, and the obtained “core” replaced primary fuzzy information so as to reach the goal of transforming uncertain information into certain information. (2) For partial data missing, the improved grey k-nearest neighbor (GKNN) algorithm was put forward based on grey relation degree and K-nearest neighbor (KNN) algorithm. Weights were introduced in the filling and logic test conditions were added after filling so that filling results were of higher truthfulness and accuracy. (3) The preprocessed data are input into the improved algorithm based on the genetic algorithm and BP neural networks (GABP) to obtain the demand prediction model. Finally the calculation presents that the prediction accuracy and its stability are improved at the five-group comparative tests of calculated examples of actual disasters. The experiments indicated that the supplies demand prediction model under data fuzziness and missing proposed in this study was of higher prediction accuracy.

Transportation and Reserve of Emergency Medical Supplies during Public Health Events

“…Hachiya, D., and other scholars studied the path planning for emergency supplies transportation based on drone technology, which takes into account the multi-objective, multi-trip, multi-item, and multi-UAV problems, and the model improves the stability of the emergency supplies supply [10]. In the case of fuzzy or missing data the demand forecasting of emergency supplies will become difficult; Zhang, M., and other scholars proposed two computational formulas solving "core" of fuzzy interval grey numbers and established a demand prediction model for emergency supplies, which has high prediction accuracy [11]. Ren, X., and other scholars proposed a dynamic vehicle path problem for the characteristics of sudden-onset epidemics with the distribution of regional emergency supplies as the research object, and finally solved the problem using the SFSSA algorithm, which provided a solution to the problem [12].…”

“…According to the development of an epidemic situation and the need for emergency medical materials, the urgency function of the emergency medical materials demand is designed. In Equation (11), N denotes the number of the total population at demand point k at time t; I denotes the number of the infected population at demand point k at time t; R denotes the number of the recovered population at demand point k at time t; and D denotes the number of the dead population at demand point k at time t. If the urgency degree of emergency medical material demand in a certain area is Y 2k (t), the formula of emergency medical material demand urgency degree in a certain area is obtained:…”

Road Rescue Demand Prediction for the Improvement of Traffic System Resilience

“…In the previous research studies, the studies about rescue in emergency mainly include rescue methodology [16], rescue architecture [17,18], accessibility of emergency service [19], emergency resource allocation [20][21][22][23][24][25][26][27][28], the determination of the medical rescue demand [29], the prediction of the emergency material demand [9][10][11][12][13][14][15], fre rescue prediction [30], emergency rescue location model [31], emergency rescue service model [32], and rescue performance [33].…”

Forecasting the Number of the Wounded after an Earthquake Disaster Based on the Continuous Interval Grey Discrete Verhulst Model

“…us, the interval grey number sequence with uncertain information is simulated and predicted [23]. Considering that some data are fuzzy or missing after earthquake disasters, which leads to difficulties in material demand prediction, Zhang et al proposed using the fuzzy interval grey number for prediction to improve the prediction accuracy [24]. Li et al proposed a new operation rule of grey interval number multiplication, which improved the accuracy of grey number division.…”