• Use of gene expression programing for multi-model combination of rainfall-runoff models 

  Fernando, Achela; Shamseldin, Asaad; Abrahart, Robert (Journal of Hydrologic Engineering, 2012)

  This paper deals with the application of an innovative method for combining estimated outputs from a number of rainfall-runoff models using Gene Expression Programming (GEP) to perform symbolic regression. The GEP multi-model ...
• Use of a supercomputer to advance parameter optimisation using genetic algorithms 

  Fernando, Achela; Jayawardena, Amithirigala (Journal of Hydroinformatics, 2007)

  Parameter optimisation is a significant but time consuming process that is inherent to conceptual hydrological models representing rainfall-runoff process. This study presents two modifications to achieve optimised results ...
• Investigation of the internal functioning of the radial basis function neural network river flow forecasting models 

  Fernando, Achela; Shamseldin, Asaad (Journal of Hydrologic Engineering, 2009)

  This paper deals with the challenging problem of hydrological interpretation of the internal functioning of ANNs by extracting knowledge from their solutions. The neural network used in this study is based on the structure ...
• Employee preferences for work-life benefits in a large New Zealand construction company 

  Thurnell, Derek; Morrison, Emily (Australasian Journal of Construction Economics and Building, 2012)

  The construction industry is a challenging environment in which to work, with job characteristics that are linked to work-life conflict, and it is important for its future sustainability that initiatives to support employee ...
• Creative and imaginative scholarship: Lifelong learning for an unpredictable future 

  Sharma, Rashika; Monteiro, Sylila (International Journal of Learning, 2010)

  The continued process of economic globalization challenges education as a means of achieving individual success in a constantly changing society. Education of today might be obsolete tomorrow in a technologically driven ...

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