Application of artificial neural network to predict amount of carried weight of cargo train in rail transportation system
| Autor: | Siti Nasuha Zubir, S. Sarifah Radiah Shariff, Siti Meriam Zahari |
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| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Conjugate gradient descent Information Systems and Management Mean squared error Computer science Quick propagation Training (meteorology) Levenberg–Marquardt algorithm Mean absolute percentage error Artificial Intelligence Control and Systems Engineering Carried weight Conjugate gradient method Rail transportation Statistics Levenberg-marquardt Electrical and Electronic Engineering |
| Popis: | Derailments of cargo have frequently occurred in Malaysian train services during the last decade. Many factors contribute to this incident, especially its total amount of carried weight. It is found that severe derailments cause damage to both lives and properties every year. If the amount of carried weight of cargo train could be accurately forecasted in advance, then its detrimental effect could be greatly minimized. This paper presents the application of Artificial Neural Network (ANN) to predict the amount of carried weight of cargo train, with KTMB used as the study case. As there are many types of cargo being carried by KTMB, this study focuses only on cement that being carried in twelve (12) different routes. In this study, Artificial Neural Network (ANN) has been incorporated for developing a predictive model with three (3) different training algorithms, Levenberg-Marquardt (LM), Quick Propagation (QP) and Conjugate Gradient Descent (CGD). The best training algorithm is selected to predict the amount of carried weight by comparing the error measures of all the training algorithm which are Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The obtained results indicated that the ANN technique is suitable for predicting the amount of carried weight. |
| Databáze: | OpenAIRE |
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