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Performance comparison of artificial neural network models for daily rainfall prediction

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Abstract

With an aim to predict rainfall one-day in advance, this paper adopted different neural network models such as feed forward back propagation neural network (BPN), cascade-forward back propagation neural network (CBPN), distributed time delay neural network (DTDNN) and nonlinear autoregressive exogenous network (NARX), and compared their forecasting capabilities. The study deals with two data sets, one containing daily rainfall, temperature and humidity data of Nilgiris and the other containing only daily rainfall data from 14 rain gauge stations located in and around Coonoor (a taluk of Nilgiris). Based on the performance analysis, NARX network outperformed all the other networks. Though there is no major difference in the performances of BPN, CBPN and DTDNN, yet BPN performed considerably well confirming its prediction capabilities. Levenberg Marquardt proved to be the most effective weight updating technique when compared to different gradient descent approaches. Sensitivity analysis was instrumental in identifying the key predictors.

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Authors and Affiliations

  1. School of Electronics Engineering, VIT University, Vellore, 632 014, Tamil Nadu, India

    S. Renuga Devi, P. Arulmozhivarman & Pranay Agarwal

  2. Faculty of Engineering, EBET Group of Institutions, Kangayam, Tamil Nadu, India

    C. Venkatesh

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  1. S. Renuga Devi
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  2. P. Arulmozhivarman
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  3. C. Venkatesh
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  4. Pranay Agarwal
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Corresponding author

Correspondence to S. Renuga Devi.

Additional information

Recommended by Associate Editor Matjaz Gams

S. Renuga Devi received her bachelor’s degree in electronics and communication engineering and master’s degree in applied electronics from Bharathiar University, India in 1994 and 1996 respectively. Currently she is working as an associate professor in the School of Electronics Engineering at VIT University, India. She has published technical papers in national and international conferences. She is a member of IEEE and ISTE.

Her research interests include machine learning, artificial intelligence and its applications and wireless sensor networks.

ORCID iD: 0000-0003-3343-2953

P. Arulmozhivarman received his B. Sc. and M. Sc. degrees in applied physics from Bharathidasan University, India and Ph.D. degree from NIT Trichy, India. Currently, he is working as a professor in the School of Electronics Engineering at VIT University, India. Also he has undertaken DRDO sponsored research projects in the area of remote sensing and image processing. He has published more than 35 papers in national and international journals.

His research interests include biomedical signal processing, image processing, vision based surveillance system, and biometric detection system.

C. Venkatesh received his bachelor’s degree in electronics and communication engineering and master’s in applied electronics from Bharathiar University, India. He received his Ph.D. degree in information and communication engineering from JNTU, Hyderabad in 2007. Currently he is the dean, faculty of engineering, EBET Group of Institutions, Erode, Tamil Nadu, India. He is a member of ISTE, IETE, IEEE society.

He received best paper award by GESTS International publications and he is also a recipient of Best Faculty Award by KVITT Trust, when he was working as an assistant professor in Kongu Engineering College. He has published 80 papers in national and international conferences and reputed journals.

His research interests include networking, soft computing techniques and VLSI in networking.

Pranay Agarwal received his B.Eng. degree in electronics and communication engineering from VIT University in 2014.

His research interests include digital image processing, machine learning and artificial intelligence.

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Devi, S.R., Arulmozhivarman, P., Venkatesh, C. et al. Performance comparison of artificial neural network models for daily rainfall prediction. Int. J. Autom. Comput. 13, 417–427 (2016). https://doi.org/10.1007/s11633-016-0986-2

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  • DOI: https://doi.org/10.1007/s11633-016-0986-2

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