Grey BP Neural Network Model Research Articles

Introduct Risk Prediction Protection Study Based on GA-BP and K-means Clustering

This paper mainly focuses on the risk management problems caused by frequent extreme weather events. Firstly, a grey bp neural network model is used as a heuristic optimization algorithm to construct an IWM model, which is used to decide whether insurance company provides insurance under extreme weather conditions. Secondly, based on the Comprehensive evaluation method (RSR), a DDI evaluation model was constructed to analyze the data of 100 buildings in 10 provinces in China to help developers evaluate the rationality of decisions. Finally, a PM evaluation model is constructed based on AHP and EWM-CRITIC, and K-means clustering is used to conduct comprehensive rating and grading evaluation of 30 low-insurance landmarks, and corresponding protection strategies are designed. The comprehensive application of these models provides useful suggestions and decision support for insurance company underwriting, real estate bidding, community building protection and so on.

Exploring the Carbon Footprint of Energy Consumption in Shanxi Province Based on the Tapio Decoupling and Grey BP Neural Network Model

This paper focuses on analyzing the carbon footprint of energy consumption in Shanxi Province, China. It utilizes data from sources such as the 'China Energy Statistical Yearbook' (2001-2022), the China Carbon Accounting Database, and the 'Shanxi Statistical Yearbook' for an exploratory analysis of energy consumption and the province's economy. The study calculates the carbon footprint of energy consumption in Shanxi Province and employs the LMDI model to identify influencing factors. Additionally, it uses the Tapio decoupling model to assess the relationship between these factors and economic development. To forecast the carbon footprint from 2022 to 2026, a grey BP neural network prediction model is applied, demonstrating improved accuracy compared to BP and grey models, with MAE and RMSE values of 2.44 and 10.91.

Demand forecasting of cold-chain logistics of aquatic products in China under the background of the Covid-19 post-epidemic era.

In the background of the post-epidemic era, the consumption demand and market scale of cold chain logistics in China are expanding, but there is still an obvious gap with developed countries. To complete the balance between the supply and demand for aquatic products and the rational allocation of logistics resources and promote the rapid development trend of aquatic product cold chain logistics, it is particularly important to forecast and analyze the demand for aquatic product cold chain logistics. This article selects six main factors that affect the demand for aquatic products in cold chain logistics, uses the traditional grey model and the grey-BP neural network model to simulate and predict the demand for aquatic products in cold chain logistics in China from 2012 to 2021, and compares and analyzes the simulation results. Generally speaking, the demand for aquatic products from Chinese residents is on the rise. In the simulation prediction process, the prediction error of the grey-BP neural network is reduced compared to the traditional grey model, and the processing ability of the nonlinear system is ideal. The results show that the grey-BP neural network model is an effective method to predict the demand for cold chain logistics of aquatic products. Finally, suggestions are made on the future development of aquatic cold chain logistics in the post-epidemic era from the economic, social, and environmental aspects, which provide valuable decision-making reference for the development of marine aquaculture enterprises and cold chain logistics industry.

Water Pollution Prevention and Prediction Based on Grey BP Neural Network Model

Water Pollution Prevention and Prediction Based on Grey BP Neural Network Model

Deformation Prediction of a Deep Foundation Pit Based on the Combination Model of Wavelet Transform and Gray BP Neural Network

The purpose of this study was to predict the deformation of a deep foundation pit based on a combination model of wavelet transform and gray BP neural network. Using a case of a deep foundation pit, a combination model of wavelet transform and gray BP neural network was used to predict the deformation of the deep foundation pit. The results show that compared with the traditional gray BP neural network model, the relative error of the combination model of wavelet transform and gray BP neural network was reduced by 2.38%. This verified that the combined model has high accuracy and reliability in the prediction of foundation pit deformation and also conforms to the actual situation of the project. The research results can provide a valuable reference for foundation pit deformation monitoring.

Prediction of Grain Yield in Henan Province Based on Grey BP Neural Network Model

BP neural network (BPNN) is widely used due to its good generalization and robustness, but the model has the defect that it cannot automatically optimize the input variables. In response to this problem, this study uses the grey relational analysis method to rank the importance of input variables, obtains the key variables and the best BPNN model structure through multiple training and learning for the BPNN models, and proposes a variable optimization selection algorithm combining grey relational analysis and BP neural network. The predicted values from the metabolic GM (1, 1) model for key variables was used as input to the best BPNN model for prediction modeling, and a grey BP neural network model prediction model (GR-BPNN) was proposed. The long short-term memory neural network (LSTM), convolutional neural network (CNN), traditional BP neural network (BP), GM (1, N) model, and stepwise regression (SR) are also implemented as benchmark models to prove the superiority and applicability of the new model. Finally, the GR-BPNN forecasting model was applied to the grain yield forecast of the whole province and subregions for Henan Province. The forecasting results found that the growth rate of grain production in Henan Province slowed down and the center of gravity for grain production shifted northwards.

Application of grey systems in predicting the degree of cotton spider mite infestations

PurposeMany factors affect the emergence and development of crop diseases and insect pests. Traditional methods for investigating this subject are often difficult to employ and produce limited data with considerable uncertainty. The purpose of this paper is to predict the annual degree of cotton spider mite infestations by employing grey theory.Design/methodology/approachThe authors established a GM(1,1) model to forecast mite infestation degree based on the analysis of historical data. To improve the prediction accuracy, the authors modified the grey model using Markov chain and BP neural network analyses. The prediction accuracy of the GM(1,1), Grey-Markov chain, and Grey-BP neural network models was 84.31, 94.76, and 96.84 per cent, respectively.FindingsCompared with the single grey forecast model, both the Grey-Markov chain model and the Grey-BP neural network model had higher forecast accuracy, and the accuracy of the latter was highest. The improved grey model can be used to predict the degree of cotton spider mite infestations with high accuracy and overcomes the shortcomings of traditional forecasting methods.Practical implicationsThe two new models were used to estimate mite infestation degree in 2015 and 2016. The Grey-Markov chain model yielded respective values of 1.27 and 1.15, whereas the Grey-BP neural network model yielded values 1.4 and 1.68; the actual values were 1.5 and 1.8.Originality/valueThe improved grey model can be used for medium- and long-term predictions of the occurrence of cotton spider mites and overcomes problems caused by data singularity and fluctuation. This research method can provide a reference for the prediction of similar diseases.