Hydrologic prediction using pattern recognition and soft-computing techniques

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Kamban Parasuraman",
  title =        "Hydrologic prediction using pattern recognition and
                 soft-computing techniques",
  school =       "Civil Engineering, University of Saskatchewan",
  year =         "2007",
  address =      "Saskatoon, Canada",
  month =        aug,
  keywords =     "genetic algorithms, genetic programming, neural
                 networks, modeling, hydrology, uncertainty,
  URL =          "http://library2.usask.ca/theses/available/etd-08172007-165636/unrestricted/Thesis_KP_CGSR_ETD.pdf",
  size =         "213 pages",
  abstract =     "Several studies indicate that the data-driven models
                 have proven to be potentially useful tools in
                 hydrological modeling. Nevertheless, it is a common
                 perception among researchers and practitioners that the
                 usefulness of the system theoretic models is limited to
                 forecast applications, and they cannot be used as a
                 tool for scientific investigations. Also, the
                 system-theoretic models are believed to be less
                 reliable as they characterise the hydrological
                 processes by learning the input-output patterns
                 embedded in the dataset and not based on strong
                 physical understanding of the system. It is imperative
                 that the above concerns needs to be addressed before
                 the data-driven models can gain wider acceptability by
                 researchers and practitioners. In this research
                 different methods and tools that can be adopted to
                 promote transparency in the data-driven models are
                 probed with the objective of extending the usefulness
                 of data-driven models beyond forecast applications as a
                 tools for scientific investigations, by providing
                 additional insights into the underlying input-output
                 patterns based on which the data-driven models arrive
                 at a decision. In this regard, the utility of
                 self-organising networks (competitive learning and
                 self-organizing maps) in learning the patterns in the
                 input space is evaluated by developing a novel neural
                 network model called the spiking modular neural
                 networks (SMNNs). The performance of the SMNNs is
                 evaluated based on its ability to characterize stream
                 flows and actual evapotranspiration process. Also the
                 utility of self-organising algorithms, namely genetic
                 programming (GP), is evaluated with regards to its
                 ability to promote transparency in data-driven models.
                 The robustness of the GP to evolve its own model
                 structure with relevant parameters is illustrated by
                 applying GP to characterise the
                 actual-evapo-transpiration process. The results from
                 this research indicate that self-organisation in
                 learning, both in terms of self-organising networks and
                 self-organising algorithms, could be adopted to promote
                 transparency in data-driven models.

                 In pursuit of improving the reliability of the
                 data-driven models, different methods for incorporating
                 uncertainty estimates as part of the data-driven model
                 building exercise is evaluated in this research. The
                 local-scale models are shown to be more reliable than
                 the global-scale models in characterising the saturated
                 hydraulic conductivity of soils. In addition, in this
                 research, the importance of model structure uncertainty
                 in geophysical modeling is emphasised by developing a
                 framework to account for the model structure
                 uncertainty in geophysical modeling. The contribution
                 of the model structure uncertainty to the predictive
                 uncertainty of the model is shown to be larger than the
                 uncertainty associated with the model parameters. Also
                 it has been demonstrated that increasing the model
                 complexity may lead to a better fit of the function,
                 but at the cost of an increasing level of uncertainty.
                 It is recommended that the effect of model structure
                 uncertainty should be considered for developing
                 reliable hydrological models.",
  notes =        "etd-08172007-165636",

Genetic Programming entries for Kamban Parasuraman