Back-propagation neural network model to predict visibility at a road link-level

Duddu, Venkata R.; Pulugurtha, Srinivas S.; Mane, Ajinkya S.; Godfrey, Christopher M.

doi:10.1016/j.trip.2020.100250

Cited by 13 publications

(12 citation statements)

References 30 publications

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“…Since the input variables such as voltage and current have different scales and large numerical differences according to the characteristics of the network, the direct use of the data will not accurately reflect the changes in the data. In addition, the values will be very large after passing the accumulator based on weighting, which will easily lead to the difficulty of convergence of the network.It is therefore necessary to normalise the data before modelling [14].…”

Section: Bp Neural Network Temperature Prediction Model Training Resultsmentioning

confidence: 99%

Prediction and Modelling of Plasma Jet Zone Temperature for DC Arc Graphite Purification

Wang,

Zhang,

et al. 2023

J. Phys.: Conf. Ser.

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The random forest algorithm was used to analyze the degree of influence of four variables, namely, voltage, current, main gas flow rate, and powder feed, on the temperature of the plasma jet zone of the DC arc graphite purification. The BP algorithm was then used to build a network prediction model. After the training of the network, the validity of the model was verified by comparing the measured temperature values with the predicted results. From the results, it can be seen that the relative error of the prediction results is between -0.3 %and +0.3%, which proves that the built BP neural network temperature model has a satisfactory prediction effect. In addition, this paper also derives specific mathematical expressions based on the threshold and weight values of this temperature prediction model, which can be used to fit the temperature variation curve in the plasma jet region.

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Section: Bp Neural Network Temperature Prediction Model Training Resultsmentioning

confidence: 99%

Prediction and Modelling of Plasma Jet Zone Temperature for DC Arc Graphite Purification

Wang,

Zhang,

et al. 2023

J. Phys.: Conf. Ser.

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“…In which X represents the mean matrix of the sample and X n represents the mean of the nth column of the sample matrix. The feature matrix on each PCA was obtained by Equation (10), denoted as P, the final dimension reduction data is the first column of matrix P:…”

Section: Data Dimension Reduction By Pca Methodsmentioning

confidence: 99%

“…In recent years, the neural network model has become a popular method for exploring the trends of fog occurrence and dissipation. Based on a large amount of meteorological data collected from meteorological monitoring stations throughout North Carolina, a Back Propagation (BP) neural network visibility model was proposed by Duddu et al [10], with deep learning from the vast data, the relationship between meteorological data and visibility was investigated by using function approximation method. Aiming at the problem of foggy events classification by meteorological input variables, an artificial neural network with evolutionary training and different basic functions was proposed by Duran-Rosal et al [11].…”

Section: Introductionmentioning

confidence: 99%

Estimating the visibility in foggy weather based on meteorological and video data: A Recurrent Neural Network approach

Chen

Qureshi

Geng

2022

IET Signal Processing

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The research of visibility detection in foggy days is of great significance to both road traffic and air transport safety. Based on the meteorological and video data collected from an airport, a deep Recurrent Neural Network (RNN) model was established in this study to predict the visibility. First, the Fourier Transform was used to extract feature variables from video data. Then, the Principal Component Analysis method was used to reduce the dimension of features. After that, 462 sets of sample data include image features, air pressure, temperature and wind speed, were used as inputs to train the RNN model. By comparing the predicted results with the actual visibility data as well as some other stateof-the-art methods, it can be found that the proposed model makes up for the deficiency of models based only on meteorological or image data, and has higher accuracy in different grades of visibility. With considering the meteorological data, the accuracy of RNN model is improved by 18.78%. Besides, with aids of correlation analysis, the influence of the meteorological factors on the predicted visibility was analysed, for fog at night, temperature is the dominant factor affecting visibility.

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“…It is a multilayer feed-forward neural network trained according to the error back propagation algorithm, and it is the most widely used neural network [25]. For recent examples, Gao et al [26] used the BP neural network to study the forecast of the short-term rainstorm; Deshwal et al [27] has established a language recognition system using the BP neural network model; Duddu et al [28] used the BP neural network model to predict visibility at the road connectivity level; Liu et al [29] used fractional GM (1, 1) and BP neural network for power load forecasting and so on.…”

Section: Introduction To the Methodology In Is Papermentioning

confidence: 99%

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

Wang

2021

Journal of Mathematics

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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.

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Back-propagation neural network model to predict visibility at a road link-level

Cited by 13 publications

References 30 publications

Prediction and Modelling of Plasma Jet Zone Temperature for DC Arc Graphite Purification

Prediction and Modelling of Plasma Jet Zone Temperature for DC Arc Graphite Purification

Estimating the visibility in foggy weather based on meteorological and video data: A Recurrent Neural Network approach

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

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