Prediction of the Traffic Volume of Henan Province with a BP Neural Network

ICTE 2015 ◽  
2015 ◽  
Author(s):  
Yulong Chen ◽  
Weixiong Zha
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Traffic Volume ◽  
Henan Province

Multidimensional research on agrometeorological disasters based on grey BP neural network

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Bingjun Li ◽  
Shuhua Zhang
Keyword(s):  
Neural Network ◽  
Natural Disaster ◽  
Bp Neural Network ◽  
Henan Province ◽  
Assessment System ◽  
Systematic Analysis ◽  
Content Type ◽  
Analysis And Evaluation ◽  
Grain Crops ◽  
Disaster Vulnerability

PurposeThe purpose of this study to provide a reference basis for effectively managing the risk of agrometeorological disasters in Henan Province, speeding up the establishment of a scientific and reasonable system of agrometeorological disasters prevention and reduction and guaranteeing grain security.Design/methodology/approachFirstly, according to the statistical data of areas covered by natural disaster, areas affected by natural disaster, sown area of grain crops and output of grain crops from 1979 to 2018 in Henan Province, China. We have constructed an agrometeorological disaster risk assessment system for Henan province, China, which is composed of indicators such as rate covered by natural disaster, rate affected by natural disaster, disaster coefficient of variation and disaster vulnerability. The variation characteristics of agrometeorological disasters in Henan Province and their effects on agricultural production are analyzed. Secondly, the grey relational analysis method is used to analyze the relation degree between the main agrometeorological disaster factors and the output of grain crops of Henan Province. Based on the grey BP neural network, the rate covered by various natural disaster and the rate affected by various natural disaster are simulated and predicted.FindingsThe results show that: (1) the freeze injury in the study period has a greater contingency, the intensity of the disaster is also greater, followed by floods. Droughts, windstorm and hail are Henan Province normal disasters. (2) According to the degree of disaster vulnerability, the ability to resist agricultural disasters in Henan Province is weak. (3) During the study period, drought and flood are the key agrometeorological disasters affecting the grain output of Henan Province, China.Practical implicationsThe systematic analysis and evaluation of agrometeorological disasters are conducive to the sustainable development of agriculture, and at the same time, it can provide appropriate and effective measures for the assessment and reduction of economic losses and risks.Originality/valueBy calculating and analyzing the rate covered by natural disaster, the rate affected by natural disaster, disaster coefficient of variation and disaster vulnerability of crops in Henan Province of China and using grey BP neural network simulation projections for the rate covered by various natural disaster and the rate affected by various natural disaster, the risk assessment system of agrometeorological disasters in Henan is constructed, which provides a scientific basis for systematic analysis and evaluation of agrometeorological disasters.

Prediction on Number of Agricultural Pumps in Henan Province of China Based on BP Neural Network

2012 ◽  
Vol 155-156 ◽  
pp. 1170-1174
Author(s):  
Jian Wei Li ◽  
Xiu Shan Wang ◽  
Yu Gui Tang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Statistical Data ◽  
Henan Province ◽  
Future Market ◽  
Market Conditions ◽  
Absolute Value ◽  
The Neural Network ◽  
Relative Errors

To understanding future market conditions of agricultural pumps in Henan province, need to predict numbers of agricultural pumps at each year-end. Expatiated the principle of prediction based on BP neural network, and constructed the model of prediction. The BP neural network was trained with normalized statistical data of agricultural pumps in Henan province from 1990 to 2010, and gained parameters of the neural network. Test shows the model has high predictive precision. The average absolute value of predicted relative errors is 1.333%. Numbers of agricultural pumps at each year-end in Henan province of China from 2011 to 2015 were predicted, and the trend was also presented.

scholarly journals Prediction of Ship Traffic Volume in Jiujiang Port Based on Genetic Wavelet Neural Network

2019 ◽  
Vol 296 ◽  
pp. 01004
Author(s):  
Shouwei XIE ◽  
Yadong YANG
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Yangtze River ◽  
Traffic Volume ◽  
Wavelet Neural Network ◽  
The Yangtze River ◽  
Ship Traffic ◽  
Neural Network Prediction ◽  
Network Prediction

In recent years, the traffic volume of the Yangtze River has increased dramatically. In order to provide more favorable assistance to port planning and traffic management, the accuracy of port ship traffic volume prediction is very important. In this paper, genetic algorithm and wavelet analysis and neural network are used to construct the genetic wavelet neural network model prediction model, and BP neural network prediction model is established. The ship volume of Jiujiang Port is used as experimental data to simulate and analyze. The results show that the prediction accuracy of the genetic wavelet neural network prediction model is significantly higher than that of the BP neural network prediction model. It is proved that the genetic wavelet neural network has broad application prospects for ship traffic flow prediction in the Yangtze River port. This method has practical application significance.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Bingjun Li ◽  
Yifan Zhang ◽  
Shuhua Zhang ◽  
Wenyan Li
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Henan Province ◽  
Grain Production ◽  
Relational Analysis ◽  
Key Variables ◽  
Input Variables ◽  
Bp Neural Network Model ◽  
Grey Relational ◽  
Bpnn 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.

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