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Multi Resolution Genetic Programming Approach for Stream Flow Forecasting

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Swarm, Evolutionary, and Memetic Computing (SEMCCO 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7076))

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Abstract

Genetic Programming (GP) is increasingly used as an alternative for Artificial Neural Networks (ANN) in many applications viz. forecasting, classification etc. However, GP models are limited in scope as their application is restricted to stationary systems. This study proposes use of Multi Resolution Genetic Programming (MRGP) based approach as an alternative modelling strategy to treat non-stationaries. The proposed approach is a synthesis of Wavelets based Multi-Resolution Decomposition and Genetic Programming. Wavelet transform is used to decompose the time series at different scales of resolution so that the underlying temporal structures of the original time series become more tractable. Further, Genetic Programming is then applied to capture the underlying process through evolutionary algorithms. In the case study investigated, the MRGP is applied for forecasting one month ahead stream flow in Fraser River, Canada, and its performance compared with the conventional, but scale insensitive, GP model. The results show the MRGP as a promising approach for flow forecasting.

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Authors and Affiliations

  1. Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India

    Rathinasamy Maheswaran & Rakesh Khosa

Authors
  1. Rathinasamy Maheswaran
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  2. Rakesh Khosa
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Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, IIT Delhi, India

    Bijaya Ketan Panigrahi

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Department of Electronics and Telecommunications, Jadavpur University, 700032, Kolkata, India

    Swagatam Das

  4. ANITS, Visakhapatnam, India

    Suresh Chandra Satapathy

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Maheswaran, R., Khosa, R. (2011). Multi Resolution Genetic Programming Approach for Stream Flow Forecasting. In: Panigrahi, B.K., Suganthan, P.N., Das, S., Satapathy, S.C. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2011. Lecture Notes in Computer Science, vol 7076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27172-4_84

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  • DOI: https://doi.org/10.1007/978-3-642-27172-4_84

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-27171-7

  • Online ISBN: 978-3-642-27172-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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