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Development of Artificial Neural Network Model for Indian Steel Consumption Forecast

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Abstract

Steel is an essential raw material for various industrial and economic activities in any country. The products of the steel industry also play a significant role in the prosperity and development of society. India is the second-largest producer of steel and one of its largest consumers globally. As a result, this sector plays a vital role in the rapid development of the Indian economy. Studying the previous consumption pattern becomes crucial to understand the growth of steel demand, which will help estimate the future demand trends in this sector. The main objective of this research paper is to develop an accurate model for forecasting steel consumption in India using artificial neural networks. For creating this model, the current study considered multiple input parameters that can influence the country’s steel consumption. The data used in this research are obtained from the websites of various Indian ministries and steel associations. The quarterly data were used to train the developed model, and its performance was measured using various statistical tools. Calculations revealed that the ANN model with two hidden layers has priority over other models.

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Data and Materials Availability

The datasets generated and/or analyzed during the current study are not publicly available as it is part of an ongoing academic research and will be published along with thesis but datasets are available from the corresponding author on reasonable request.

Abbreviations

ANN:

Artificial Neural network

IoU:

Intensity of Use

GDP:

Gross domestic product

IIP:

Index of industrial production

GVA:

Gross value added

MSE:

Mean square error

MAPE:

Mean absolute percentage error

RBF:

Radial basis function

MoSPI:

Ministry of statistics and Programme implementation

GoI:

Government of India

Trainlm:

Levenberg–Marquardt Algorithm

Trainrp:

Resilient backpropagation

Traincgb:

Conjugate Gradient

Trainbr:

Bayesian regularization

EFA:

Exploratory factor analysis

CFA:

Confirmatory factor analysis

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Acknowledgements

The author is thankful to the respondents of the survey and executives of the Indian industries who helped in getting the responses.

Funding

Current research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Mechanical Engineering Department, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonipat, Haryana, 131039, India

    Vishant Kumar (Ph.D. Scholar) & Ramesh Kumar (Professor)

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  1. Vishant Kumar
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  2. Ramesh Kumar
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Correspondence to Vishant Kumar.

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Kumar, V., Kumar, R. Development of Artificial Neural Network Model for Indian Steel Consumption Forecast. J. Inst. Eng. India Ser. D 105, 323–331 (2024). https://doi.org/10.1007/s40033-023-00482-x

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