Investigating the capabilities of evolutionary data-driven techniques using the challenging estimation of soil moisture content

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@Article{Elshorbagy:2009:JH,

• author = "Amin Elshorbagy and Ibrahim El-Baroudy",
• title = "Investigating the capabilities of evolutionary data-driven techniques using the challenging estimation of soil moisture content",
• journal = "Journal of Hydroinformatics",
• year = "2009",
• volume = "11",
• number = "3-4",
• pages = "237--251",
• keywords = "genetic algorithms, genetic programming, evolutionary polynomial regression, EPR, prediction, soil moisture, tool uncertainty",
• ISSN = "1464-7141",
• URL = "http://www.iwaponline.com/jh/011/0237/0110237.pdf",
• DOI = "doi:10.2166/hydro.2009.032",
• size = "15 pages",
• abstract = "Soil moisture has a crucial role in both the global energy and hydrological cycles; it affects different ecosystem processes. Spatial and temporal variability of soil moisture add to its complex behaviour, which undermines the reliability of most current measurement methods. In this paper, two promising evolutionary data-driven techniques, namely (i) Evolutionary Polynomial Regression and (ii) Genetic Programming, are challenged with modelling the soil moisture response to the near surface atmospheric conditions. The utility of the proposed models is demonstrated through the prediction of the soil moisture response of three experimental soil covers, used for the restoration of watersheds that were disturbed by the mining industry. The results showed that the storage effect of the soil moisture response is the major challenging factor; it can be quantified using cumulative inputs better than time-lag inputs, which can be attributed to the effect of the soil layer moisture-holding capacity. This effect increases with the increase in the soil layer thickness. Three different modelling tools are tested to investigate the tool effect in data-driven modelling. Despite the promising results with regard to the prediction accuracy, the study demonstrates the need for adopting multiple data-driven modelling techniques and tools (modelling environments) to obtain reliable predictions.",
• notes = "Laucelli EPR toolbox, South Bison Hill, oil sands reclamation, 1 foot or more peat layer, AB Canada, Discipulus \cite{francone:manual}

  p242 'Discipulus produced better models than EPR'. p246 EPR provides insight. p258 GPLAB always evolved constants (not formulae).",

}

Genetic Programming entries for Amin Elshorbagy Ibrahim El-Baroudy

Citations