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Development of Artificial Intelligence Based Regional Flood Estimation Techniques for Eastern Australia

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Artificial Neural Network Modelling

Part of the book series: Studies in Computational Intelligence ((SCI,volume 628))

Abstract

This chapter focuses on the development of artificial intelligence based regional flood frequency analysis (RFFA) techniques for Eastern Australia. The techniques considered in this study include artificial neural network (ANN), genetic algorithm based artificial neural network (GAANN), gene-expression programing (GEP) and co-active neuro fuzzy inference system (CANFIS). This uses data from 452 small to medium sized catchments from Eastern Australia. In the development/training of the artificial intelligence based RFFA models, the selected 452 catchments are divided into two groups: (i) training data set, consisting of 362 catchments; and (ii) validation data set, consisting of 90 catchments. It has been shown that in the training of the four artificial intelligence based RFFA models, no model performs the best across all the considered six average recurrence intervals (ARIs) for all the adopted statistical criteria. Overall, the ANN based RFFA model is found to outperform the other three models in the training. Based on an independent validation, the median relative error values for the ANN based RFFA model are found to be in the range of 35–44 % for eastern Australia. The results show that ANN based RFFA model is applicable to eastern Australia.

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Author information

Authors and Affiliations

  1. School of Computing, Engineering and Mathematics, Western Sydney University Australia, Building XB, Kingswood, Locked Bag 1797, Penrith, NSW, 2751, Australia

    Kashif Aziz & Ataur Rahman

  2. Institute for Infrastructure Engineering, Western Sydney University, Penrith, NSW, Australia

    Ataur Rahman

  3. University of Auckland, Auckland, New Zealand

    Asaad Shamseldin

Authors
  1. Kashif Aziz
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  2. Ataur Rahman
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  3. Asaad Shamseldin
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Correspondence to Ataur Rahman .

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Editors and Affiliations

  1. School of Computer and Mathematical, Auckland University of Technology (, Auckland, New Zealand

    Subana Shanmuganathan

  2. Dept of Informatics & Enabling Tech, Lincoln University, Lincoln, New Zealand

    Sandhya Samarasinghe

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Aziz, K., Rahman, A., Shamseldin, A. (2016). Development of Artificial Intelligence Based Regional Flood Estimation Techniques for Eastern Australia. In: Shanmuganathan, S., Samarasinghe, S. (eds) Artificial Neural Network Modelling. Studies in Computational Intelligence, vol 628. Springer, Cham. https://doi.org/10.1007/978-3-319-28495-8_13

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  • DOI: https://doi.org/10.1007/978-3-319-28495-8_13

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