Prediction of surface water total dissolved solids using hybridized wavelet-multigene genetic programming: New approach

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@Article{JAMEI:2020:JH,

• author = "Mehdi Jamei and Iman Ahmadianfar and Xuefeng Chu and Zaher Mundher Yaseen",
• title = "Prediction of surface water total dissolved solids using hybridized wavelet-multigene genetic programming: New approach",
• journal = "Journal of Hydrology",
• volume = "589",
• pages = "125335",
• year = "2020",
• ISSN = "0022-1694",
• DOI = "doi:10.1016/j.jhydrol.2020.125335",
• URL = "http://www.sciencedirect.com/science/article/pii/S0022169420307952",
• keywords = "genetic algorithms, genetic programming, Water quality, Total dissolved solids, Wavelet-multigene genetic programming, Wavelet analysis, River engineering",
• abstract = "Total dissolved solids (TDS) are recognized as an essential indicator of surface water quality. The current research investigates the potential of a novel computer aid approach based on the hybridization of wavelet pre-processing with multigene genetic programming (W-MGGP) for monthly TDS prediction at the Sefid Rud River in Northern Iran. 20-year historical monthly river flow (Q) and TDS data measured at the Astaneh station were used for the model training and testing. The employed time series data were decomposed into several sub-series using three mother wavelets (i.e., Daubechies4 (db4), biorthogonal (bior6.8), and discrete meyer (dmey)) to assess appropriate combinations of the time series and their lag times, which were further used for prediction process. The W-MGGP model was compared against the wavelet-gene expression programming (W-GEP), stand-alone MGGP, and GEP models. Results were evaluated using several performance metrics including root mean square error (RMSE), correlation coefficient (R), and Nash-Sutcliffe efficiency (NSE). Modeling results indicated that W-MGGP and W-GEP provided a superior prediction capacity for the TDS in comparison with the other stand-alone artificial intelligence (AI) models. The discrete meyer method exhibited the best performance in time series data decomposition as a pre-processing approach. The proposed W-MGGP model based on the dmey mother wavelet attained the best statistical metrics (R = 0.942, RMSE = 90.383, and NSE = 0.862). The research findings demonstrated the hybridization of the wavelet pre-processing approach with MGGP predictive model for the TDS simulation",

}

Genetic Programming entries for Mehdi Jamei Iman Ahmadianfar Xuefeng Chu Zaher Mundher Yaseen

Citations