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

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jun Zhang ◽  
Tongyuan Wang ◽  
Jianpeng Chang ◽  
Yan Gou
Keyword(s):  
Prediction Accuracy ◽  
Information Quantity ◽  
Emergency Relief ◽  
Earthquake Disaster ◽  
Emergency Rescue ◽  
Form Equation ◽  
Grey Verhulst Model ◽  
Verhulst Model ◽  
Shape Curve ◽  
Short Time

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.

scholarly journals A Multiobjective Optimization Model for Continuous Allocation of Emergency Rescue Materials

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Yonghong Liu ◽  
Yucheng Li ◽  
De Huang
Keyword(s):  
Multiobjective Optimization ◽  
Optimization Model ◽  
Supply And Demand ◽  
Economic Losses ◽  
Allocation Scheme ◽  
Nsga Ii ◽  
Pareto Solution ◽  
Emergency Relief ◽  
Rescue Work ◽  
Emergency Rescue

Emergency rescue operations play a vital role in alleviating human suffering, reducing casualties, and cutting down economic losses. One key aspect in the management of these operations is the rational allocation of emergency relief materials, where the allocation is continuous, dynamic, and concurrent. This allocation should be made not only to minimize the emergency rescue losses, but also to reduce the cost of emergency rescue work. A reasonable and effective allocation scheme for emergency relief materials can be established to adapt to the continuity, dynamics, and concurrency of material distribution. In this work, we propose a multiobjective optimization model of emergency material allocation with continuous time-varying supply and demand constraints, where the objective is to minimize the losses and the economic cost incurred by the emergency rescue operations. The constrained optimization problem is handled through sequential unconstrained minimization techniques, and the multiobjective optimization is carried out by the fast nondominated sorting genetic algorithm (NSGA-II) with an elite strategy to obtain a Pareto solution set with fairness and balance of loss and cost. The loss and cost associated with the Pareto frontier are employed to find an appropriate noninferior solution and its corresponding material allocation scheme. We verify through several simulations the model feasibility and the effectiveness of the proposed method, which can provide decision support for continuous material allocation in emergency rescue operations.

scholarly journals Research on the Grey Verhulst Model Based on Particle Swarm Optimization and Markov Chain to Predict the Settlement of High Fill Subgrade in Xiangli Expressway

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Haiming Liu ◽  
Wei Guo ◽  
Chao Zhang ◽  
Huaihao Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Markov Chain ◽  
Particle Swarm ◽  
Time Interval ◽  
Grey Theory ◽  
Swarm Optimization ◽  
Model Based ◽  
Grey Verhulst Model ◽  
Competing Models ◽  
Verhulst Model

It is of vital significance to accurately forecast the settlement of high fill subgrade, which is the foundation for disaster prevention and treatment of subgrade. According to the monitoring data of high fill subgrade, a novel model, called PSOMGVM model, based on particle swarm optimization (PSO) and Markov chain is proposed. Firstly, the typical characteristics of settlement curve are analyzed from the aspect of geomechanics theory and based on the grey theory, the grey Verhulst model (GVM) with unequal time-interval is proposed. Then, according to the theory of Markov chain, the grey Verhulst model is built to revise the relative residuals of the GVM, in which the effects of volatility characteristics can be considered. Finally, the PSOMGVM model based on PSO algorithm and Markov chain is set up, which whitens the parameters of the grey interval. In order to demonstrate the fitness and the ability of the proposed model, five competing models are introduced to predict the settlement of the high fill subgrade of Xiangli Expressway in Yunnan Province. Through the analysis of APE, MAPE, and RMSE, it states that the accuracy and performance of the PSOMGVM model outperform the other five competing models for simulative and predictive periods.

scholarly journals Fast prediction of quality parameters in whole seeds of oilseed rape (Brassica napus L.)

2011 ◽  
Vol 49 (No. 4) ◽  
pp. 141-145 ◽  
Author(s):  
V. Míka ◽  
P. Tillmann ◽  
R. Koprna ◽  
P. Nerušil ◽  
V. Kučera
Keyword(s):  
Prediction Accuracy ◽  
Near Infrared ◽  
Quality Parameters ◽  
Breeding Cycle ◽  
Brassica Napus L ◽  
Calibration Equation ◽  
Whole Seed ◽  
Fast Prediction ◽  
Short Time ◽  
Non Destructive

A calibration equation for NIRSystems 6500 instrument was derived at VSTE Jevíčko using the measurement of broad collection of Czech samples of winter rape, allowing sufficiently accurate prediction of content of dry matter (DM), crude protein (XP), crude fat (XL), glucosinolates (GSL), oleic and linoleic acids in an extremely short time. The prediction accuracy was verified on a validation file (n = 60). The coefficients of determinance (R2) were 0.83 for XP, 0.71 for XL, and 0.84 for GSL. The prediction accuracy according to the VSTE equation was compared to the prediction accuracy according to the VDLUFA calibration equation (Kassel, FRG) used in EU near infrared spectroscopy network. It was stated that the former was not distinctly worse. Non-destructive NIR-analysis of the whole seed also allows sowing selected seeds in the year of harvest and thus accelerates the breeding cycle.

scholarly journals Demand Prediction of Emergency Supplies under Fuzzy and Missing Partial Data

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Ming Zhang ◽  
Hanlin Wu ◽  
Zhifeng Qiu ◽  
Yifan Zhang ◽  
Boquan Li
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Nearest Neighbor ◽  
Uncertain Information ◽  
K Nearest Neighbor ◽  
Emergency Rescue ◽  
Demand Prediction ◽  
Partial Data ◽  
Grey Relation ◽  
Fuzzy Interval

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.

scholarly journals Grey power models based on optimization of initial condition and model parameters

2014 ◽  
Vol 4 (2) ◽  
pp. 370-382 ◽  
Author(s):  
Yunchol Jong ◽  
Sifeng Liu
Keyword(s):  
Least Square Method ◽  
Least Square ◽  
Model Parameters ◽  
Initial Condition ◽  
Content Type ◽  
Novel Approach ◽  
New Information ◽  
Power Models ◽  
Grey Verhulst Model ◽  
Verhulst Model

Purpose – The purpose of this paper is to propose a novel approach to improve prediction accuracy of grey power models including GM(1, 1) and grey Verhulst model. Design/methodology/approach – The modified new models are proposed by optimizing the initial condition and model parameters. The new initial condition consists of the first item and the last item of a sequence generated by applying the first-order accumulative generation operator on the sequence of raw data. Findings – It is shown that the newly modified grey power model is an extension of the previous optimized GM(1, 1) and grey Verhulst model. And the optimized initial condition reflected the principle of new information priority. Practical implications – The result of a numerical example indicates that the modified grey model presented in this paper with better prediction performance. Originality/value – The new initial condition are derived by weighted combination of the first item and the last item. The coefficients of weight obtained by the least square method.

Measurement of shock effect following change of one-child policy based on grey forecasting approach

Kybernetes ◽  
2018 ◽  
Vol 47 (3) ◽  
pp. 559-586 ◽  
Author(s):  
Naiming Xie ◽  
Ruizhi Wang ◽  
Nanlei Chen
Keyword(s):  
Sex Ratio ◽  
Total Population ◽  
Development Trend ◽  
General Development ◽  
Content Type ◽  
One Child Policy ◽  
Child Policy ◽  
Grey Verhulst Model ◽  
Verhulst Model ◽  
The One

Purpose This paper aims to analyze general development trend of China’s population and to forecast China’s total population under the change of China’s family planning policy so as to measure shock disturbance effects on China’s population development. Design/methodology/approach China has been the most populous country for hundreds of years. And this state will be sustained in the forthcoming decade. Obviously, China is confronted with greater pressure on controlling total scale of population than any other country. Meanwhile, controlling population will be beneficial for not only China but also the whole world. This paper first analyzes general development trend of China’s population total amount, sex ratio and aging ratio. The mechanism for measurement of the impact effect of a policy shock disturbance is proposed. Linear regression model, exponential curve model and grey Verhulst model are adopted to test accuracy of simulation of China’s total population. Then considering the policy shock disturbance on population, discrete grey model, DGM (1, 1), and grey Verhulst model were adopted to measure how China’s one-child policy affected its total population between 1978 and 2015. And similarly, the grey Verhulst model and scenario analysis of economic developing level were further used to forecast the effect of adjustment from China’s one-child policy to two-child policy. Findings Results show that China has made an outstanding contribution toward controlling population; it was estimated that China prevented nearly 470 million births since the late 1970s to 2015. However, according to the forecast, with the adjustment of the one-child policy, the birth rate will be a little higher, China’s total population was estimated to reach 1,485.59 million in 2025. Although the scale of population will keep increasing, but it is tolerable for China and sex ratio and trend of aging will be relieved obviously. Practical implications The approach constructed in the paper can be used to measure the effect of population change under the policy shock disturbance. It can be used for other policy effect measurement problems under shock events’ disturbance. Originality/value The paper succeeded in studying the mechanism for the measurement of the post-impact effect of a policy and the effect of changes in China’s population following the revision of the one-child policy. The mechanism is useful for solving system forecasting problems and can contribute toward improving the grey decision-making models.

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