Chaos-based multigene genetic programming: A new hybrid strategy for river flow forecasting

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@Article{GHORBANI2018455,

• author = "Mohammad Ali Ghorbani and Rahman Khatibi and Ali {Danandeh Mehr} and Hakimeh Asadi",
• title = "Chaos-based multigene genetic programming: A new hybrid strategy for river flow forecasting",
• journal = "Journal of Hydrology",
• year = "2018",
• volume = "562",
• pages = "455--467",
• keywords = "genetic algorithms, genetic programming, Multigene genetic programming (MGGP), Chaos theory, Forecasting, Hybrid models, Phase-Space Reconstruction (PSR), River flow",
• ISSN = "0022-1694",
• URL = "http://www.sciencedirect.com/science/article/pii/S002216941830307X",
• DOI = "doi:10.1016/j.jhydrol.2018.04.054",
• abstract = "Chaos theory is integrated with Multi-Gene Genetic Programming (MGGP) engine as a new hybrid model for river flow forecasting. This is to be referred to as Chaos-MGGP and its performance is tested using daily historic flow time series at four gauging stations in two countries with a mix of both intermittent and perennial rivers. Three models are developed: (i) Local Prediction Model (LPM); (ii) standalone MGGP; and (iii) Chaos-MGGP, where the first two models serve as the benchmark for comparison purposes. The Phase-Space Reconstruction (PSR) parameters of delay time and embedding dimension form the dominant input signals derived from original time series using chaos theory and these are transferred to Chaos-MGGP. The paper develops a procedure to identify global optimum values of the PSR parameters for the construction of a regression-type prediction model to implement the Chaos-MGGP model. The inter-comparison of the results at the selected four gauging stations shows that the Chaos-MGGP model provides more accurate forecasts than those of stand-alone MGGP or LPM models.",

}

Genetic Programming entries for Mohammad Ali Ghorbani Rahman Khatibi Ali Danandeh Mehr H Asadi

Citations