scholarly journals Container Throughput Forecasting of Tianjin-Hebei Port Group Based on Grey Combination Model

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Chen He ◽  
Huipo Wang
Keyword(s):  
Neural Network ◽  
Information System ◽  
Bp Neural Network ◽  
Capacity Planning ◽  
Forecasting Model ◽  
Forecasting Accuracy ◽  
Combination Model ◽  
Comparison Results ◽  
Container Operation ◽  
Better Than

Container throughput forecasting plays an important role in port capacity planning and management. Regarding the issue of container throughput of Tianjin-Hebei Port Group, considering the container throughput is an incomplete grey information system affected by various factors, the effect is often unsatisfactory by adopting a single forecasting model. Therefore, this paper studies the issue by combining fractional GM (1, 1) and BP neural network. The comparison results show that the combination model performs better than other single models separately and has a higher level of forecasting accuracy. Furthermore, the combination model is adopted to forecast the container throughput of Tianjin-Hebei Port Group from 2021 to 2025, which would be a data reference for the future development optimization for the container operation of Tianjin-Hebei Port Group.

Prediction of Siltation from a Rainfall in Check Dam Based on RBF Neural Network

2012 ◽  
Vol 263-266 ◽  
pp. 2150-2154
Author(s):  
Guo Zhong Wang ◽  
Ya Dong Mei ◽  
Jian Gang Qu ◽  
Rui Shuang ◽  
Jianhua Xu
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Rbf Neural Network ◽  
Rainfall Erosivity ◽  
Nonlinear Characteristics ◽  
Maximum Rainfall ◽  
Forecasting Accuracy ◽  
Sediment Volume ◽  
Erosive Rainfall ◽  
Better Than

Due to the complex nonlinear characteristics between erosive rainfall and corresponding sediment volume, radial basis function (RBF) neural network is adopted to predict siltation in matlab2010 environment, and the results were compared with that one from BP neural network. In the course, the 3 major indicators of a rainfall such as single rainfall erosivity (R), maximum rainfall intensity in thirty minutes (I30) , rainfall quantity(P) are as input vectors, with the actual sediment deposition as a target vector. The results show that: RBF neural network is better than BP neural network in forecasting accuracy, computation speed, fitting accuracy.

scholarly journals Tourism Demand Forecasting Based on Grey Model and BP Neural Network

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xing Ma
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Grey Model ◽  
Series Data ◽  
Forecasting Model ◽  
Data Series ◽  
Tourism Demand ◽  
Combination Model ◽  
Combination Forecasting ◽  
Domestic Tourism

This article aims to explore a more suitable prediction method for tourism complex environment, to improve the accuracy of tourism prediction results and to explore the development law of China’s domestic tourism so as to better serve the domestic tourism management and tourism decision-making. This study uses grey system theory, BP neural network theory, and the combination model method to model and forecast tourism demand. Firstly, the GM (1, 1) model is established based on the introduction of grey theory. The regular data series are obtained through the transformation of irregular data series, and the prediction model is established. Secondly, in the structure algorithm of the BP neural network, the BP neural network model is established using the data series of travel time and the number of people. Then, combining BP neural network with the grey model, the grey neural network combination model is established to forecast the number of tourists. The prediction accuracy of the model is analyzed by the actual time series data of the number of tourists. Finally, the experimental analysis shows that the combination forecasting makes full use of the information provided by each forecasting model and obtains the combination forecasting model and the best forecasting result so as to improve the forecasting accuracy and reliability.

A Two-Stage Combination Model for Wind Power Forecasting

2015 ◽  
Vol 737 ◽  
pp. 9-13
Author(s):  
Jun Zhang ◽  
Yuan Hao Wang ◽  
Ying Yi Li ◽  
Feng Guo
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Wind Farm ◽  
General Trend ◽  
Two Stage ◽  
Combination Model ◽  
Second Stage ◽  
Combination Forecasting ◽  
The Mean ◽  
Power Sequence

With the wind farm data from the southeast coast this paper builds a two-stage combination forecasting model of output power based on data preprocessing which include filling up missing data and pre-decomposition. The first stage is a composite prediction of decomposed power sequence in which a time series and optimized BP neural network predict the general trend and the correlation of various factors respectively. The second stage is BP neural network with its input is the results of first stage. The effectiveness and accuracy of the two-stage combination model are verified by comparing the mean square error of the combination model and other models.

scholarly journals Forecast of Waterway Cargo Turnover Volume Based on Genetic Algorithm to Optimize Neural Network Parameters

2021 ◽  
Vol 2083 (3) ◽  
pp. 032010
Author(s):  
Rong Ma
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Bp Neural Network ◽  
Forecast Model ◽  
Network Parameters ◽  
The Neural Network ◽  
Target Effect ◽  
The Empirical Analysis ◽  
Better Than

Abstract The traditional BP neural network is difficult to achieve the target effect in the prediction of waterway cargo turnover. In order to improve the accuracy of waterway cargo turnover forecast, a waterway cargo turnover forecast model was created based on genetic algorithm to optimize neural network parameters. The genetic algorithm overcomes the trap that the general iterative method easily falls into, that is, the “endless loop” phenomenon that occurs when the local minimum is small, and the calculation time is small, and the robustness is high. Using genetic algorithm optimized BP neural network to predict waterway cargo turnover, and the empirical analysis of the waterway cargo turnover forecast is carried out. The results obtained show that the neural network waterway optimized by genetic algorithm has a higher accuracy than the traditional BP neural network for predicting waterway cargo turnover, and the optimization model can long-term analysis of the characteristics of waterway cargo turnover changes shows that the prediction effect is far better than traditional neural networks.

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