Pareto-optimal MPSA-MGGP: A new gene-annealing model for monthly rainfall forecasting

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@Article{DANANDEHMEHR:2019:JH,

• author = "Ali {Danandeh Mehr} and Masood Jabarnejad and Vahid Nourani",
• title = "Pareto-optimal {MPSA-MGGP}: A new gene-annealing model for monthly rainfall forecasting",
• journal = "Journal of Hydrology",
• volume = "571",
• pages = "406--415",
• year = "2019",
• keywords = "genetic algorithms, genetic programming, Rainfall, Time series forecasting, Multigene genetic programming, Simulated annealing, Semiarid region",
• ISSN = "0022-1694",
• DOI = "doi:10.1016/j.jhydrol.2019.02.003",
• URL = "http://www.sciencedirect.com/science/article/pii/S0022169419301362",
• abstract = "Rainfall is considered the hardest weather variable to forecast, and its cause-effect relationships often cannot be expressed in simple or complex mathematical forms. This study introduces a novel hybrid model to month ahead forecasting monthly rainfall amounts which is motivated to be used in semi-arid basins. The new approach, called MPSA-MGGP, is based on integrating multi-period simulated annealing (MPSA) optimizer with multigene genetic programming (MGGP) symbolic regression so that the hybrid model reflects the periodic patterns in rainfall time series into a Pareto-optimal multigene forecasting equation. The model was trained and verified using observed rainfall at two meteorology stations located in north-west of Iran. The model accuracy was also cross-validated against two benchmarks: conventional genetic programming (GP) and MGGP. The results indicated that the proposed gene-annealing model provides slight to moderate decline in absolute error as well as noteworthy augment in Nash-Sutcliffe coefficient of efficiency. Promising efficiency together with parsimonious structure endorse the proposed model to be used for monthly rainfall forecasting in practice, particularly in semi-arid regions",

}

Genetic Programming entries for Ali Danandeh Mehr Masood Jabarnejad Vahid Nourani

Citations