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A Robustness Analysis of Different Nonlinear Autoregressive Networks Using Monte Carlo Simulations for Predicting High Fluctuation Rainfall

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Micro-Electronics and Telecommunication Engineering

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 106))

Abstract

In this study, the main objective is to carry out the robustness analysis of an artificial intelligence (AI) approach, namely nonlinear autoregressive neural networks (NAR) using Monte Carlo simulations for predicting the high fluctuation rainfall. Various algorithms of the NAR including Levenberg–Marquardt (LM), Bayesian regularization (BR) and scaled conjugate gradient (SCG) were developed. Statistical criteria, namely coefficient of determination (R2), root mean squared error (RMSE) and mean absolute error (MAE), were used to quantify the impact of fluctuations on the prediction output. Results showed that SCG algorithm was not sufficiently robust, while LM and BR methods exposed a strong capability in forecasting daily rainfall. In addition, prediction using BR was slightly better than LM, especially in terms of standard deviation of R2, RMSE and MAE distributions over 500 Monte Carlo realizations.

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Conflict of Interest

The author declares no conflict of interest.

Author information

Authors and Affiliations

  1. NTT Hi-Tech Institute, Nguyen Tat Thanh University, Ho Chi Minh City, 700000, Vietnam

    Tien-Thinh Le & Vuong Minh Le

  2. University of Transport Technology, Hanoi, 100000, Vietnam

    Binh Thai Pham & Hai-Bang Ly

  3. Faculty of Engineering, Vietnam National University of Agriculture, Gia Lam, Hanoi, 100000, Vietnam

    Lu Minh Le

Authors
  1. Tien-Thinh Le
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  2. Binh Thai Pham
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  3. Vuong Minh Le
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  4. Hai-Bang Ly
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  5. Lu Minh Le
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Corresponding author

Correspondence to Binh Thai Pham .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Delhi NCR Campus, Ghaziabad, India

    Devendra Kumar Sharma

  2. Department of Automatics and Applied Software, “Aurel Vlaicu” University of Arad, Arad, Romania

    Valentina Emilia Balas

  3. Department of Multimedia and Virtual Reality, Institute of Information Technology, Vietnam National University, Hanoi, Vietnam

    Le Hoang Son

  4. Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Delhi NCR Campus, Ghaziabad, India

    Rohit Sharma

  5. Department of Electrical and Electronics Engineering, Trakya University, Edirne, Turkey

    Korhan Cengiz

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Le, TT., Pham, B.T., Le, V.M., Ly, HB., Le, L.M. (2020). A Robustness Analysis of Different Nonlinear Autoregressive Networks Using Monte Carlo Simulations for Predicting High Fluctuation Rainfall. In: Sharma, D.K., Balas, V.E., Son, L.H., Sharma, R., Cengiz, K. (eds) Micro-Electronics and Telecommunication Engineering. Lecture Notes in Networks and Systems, vol 106. Springer, Singapore. https://doi.org/10.1007/978-981-15-2329-8_21

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  • DOI: https://doi.org/10.1007/978-981-15-2329-8_21

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  • Print ISBN: 978-981-15-2328-1

  • Online ISBN: 978-981-15-2329-8

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