Forecasting Civil Aviation Incident Rate in China Using a Combined Prediction Model

With the rapid development of air transportation, the complexity, importance, and severity of civil aviation safety have gradually become prominent. It is essential to use various data to analyze and predict the level of aviation safety. This paper used a combined prediction model based on Induced Ordered Weighted Averaging (IOWA) operator to forecast the civil aviation incident rate. We compiled and calculated civil aviation incident data and total flight hours from 2008 to 2019 in China and took the civil aviation incident rate (incident numbers per ten thousand flight hours) as the prediction object. First, this paper used the nonlinear regression model, Grey Verhulst model, and Holt-Winters exponential smoothing model to forecast the civil aviation incident rate individually. Then, it used the smallest sum of squared errors as the principle to use a combined prediction model based on the IOWA operator. It can be seen from the experimental results that the prediction accuracy of the combined model is better than single models. Finally, this paper forecasted the civil aviation incident rate in 2020 and 2021. The results showed that the predicted rates are 0.524 and 0.551. Most notably the incident rate will increase significantly compared with 2019.

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Detection and Prediction of Internal Damage in the Ancient Timber Structure Based on Optimal Combined Model

Advances in Civil Engineering ◽

10.1155/2019/7108262 ◽

2019 ◽

Vol 2019 ◽

pp. 1-18

Author(s):

Ziyi Wang ◽

Donghui Ma ◽

Wei Qian ◽

Wei Wang ◽

Xiaodong Guo ◽

...

Keyword(s):

Stress Wave ◽

Timber Structure ◽

Forecasting Model ◽

Combined Model ◽

Internal Damage ◽

Forecasting Models ◽

Model Based ◽

Combined Forecasting ◽

Drilling Resistance ◽

Iowa Operator

It is currently known that using stress wave and drilling resistance to detect the internal damage in the ancient timber structure is not a highly precise process. To improve the detection precision of this process, a simulation test was used to detect the internal damage of poplar and elm in ancient buildings. In this empirical study, we compared the detection precision of these two detection methods. Based on the idea of variable weight, we introduced three combined forecasting models based on the IOWA operator, IOWGA operator, and IOWHA operator to predict the internal damage in the ancient timber structure. The results show that the combined forecasting model based on the IOWA operator is more effective in predicting compared to a single detection method and other combined forecasting models. To be more specific, the results show that the detection precision of the combined model is increased by 25.8% and 4.7%, respectively, compared to the precision of the stress wave and drilling resistance tests. The error indicators of the combined forecasting model based on the IOWA operator are better than those of the other combined forecasting models. In addition, the analysis results based upon cross-validation theory show the combined forecasting model based on the IOWA operator has the best applicability, which provides a new practical method for evaluating internal damage of timber components in ancient buildings.

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Optimized neural network combined model based on the induced ordered weighted averaging operator for vegetable price forecasting

Expert Systems with Applications ◽

10.1016/j.eswa.2020.114232 ◽

2020 ◽

pp. 114232

Author(s):

Bo Li ◽

Junqi Ding ◽

Zhengqing Yin ◽

Kaiyu Li ◽

Xue Zhao ◽

...

Keyword(s):

Neural Network ◽

Weighted Averaging ◽

Price Forecasting ◽

Combined Model ◽

Ordered Weighted Averaging ◽

Model Based ◽

Averaging Operator ◽

Ordered Weighted Averaging Operator

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A combined model based on feature selection and support vector machine for PM2.5 prediction

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202812 ◽

2021 ◽

pp. 1-15

Author(s):

Xiaocong Lai ◽

Hua Li ◽

Ying Pan

Keyword(s):

Air Pollution ◽

Support Vector Machine ◽

Feature Selection ◽

Air Quality ◽

Prediction Model ◽

Meteorological Factors ◽

Feature Selection Method ◽

Support Vector ◽

Combined Model ◽

Model Based

With the increasing attention to the environment and air quality, PM2.5 has been paid more and more attention. It is expected to excavate useful information in meteorological data to predict air pollution, however, the air quality is greatly affected by meteorological factors, and how to establish an effective air quality prediction model has always been a problem that people urgently need to solve. This paper proposed a combined model based on feature selection and Support Vector Machine (SVM) for PM2.5 prediction. Firstly, aiming at the influence of meteorological factors on PM2.5, a feature selection method based on linear causality is proposed to find out the causality between features and select the features with strong causality, so as to remove the redundant features in air pollution data and reduce the workload of data analysis. Then, a method based on SVM is proposed to analyze and solve the nonlinear problems in the data, for reducing the prediction error, a method of particle swarm optimization is also used to optimize SVM parameters. Finally, the above methods are combined into a prediction model, which is suitable for the current air pollution control. 12 representative data sets on the UCI (University of California, Irvine) website are used to verify the combined model, and the experimental results show that the model is feasible and effective.

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