Optimal Water Quality Management Strategies for urban Watersheds using Macro-level Simulation models linked with Evolutionary algorithms

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  author =       "Mohammad Tufail",
  title =        "Optimal Water Quality Management Strategies for urban
                 Watersheds using Macro-level Simulation models linked
                 with Evolutionary algorithms",
  school =       "Civil Engineering, Engineering Department, University
                 of Kentucky",
  year =         "2006",
  address =      "Lexington, Kentucky, USA",
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://uknowledge.uky.edu/cgi/viewcontent.cgi?article=1316&context=gradschool_diss",
  URL =          "http://uknowledge.uky.edu/gradschool_diss/313",
  size =         "317 pages",
  abstract =     "Urban watershed management poses a very challenging
                 problem due to the various sources of pollution and
                 there is a need to develop optimal management models
                 that can facilitate the process of identifying optimal
                 water quality management strategies. A screening level,
                 comprehensive, and integrated computational methodology
                 is developed for the management of point and non-point
                 sources of pollution in urban watersheds. The
                 methodology is based on linking macro-level water
                 quality simulation models with efficient nonlinear
                 constrained optimisation methods for urban watershed
                 management.The use of macro-level simulation models in
                 lieu of the traditional and complex deductive
                 simulation models is investigated in the optimal
                 management framework for urban watersheds. Two
                 different types of macro-level simulation models are
                 investigated for application to watershed pollution
                 problems namely explicit inductive models and
                 simplified deductive models. Three different types of
                 inductive modelling techniques are used to develop
                 macro-level simulation models ranging from simple
                 regression methods to more complex and nonlinear
                 methods such as artificial neural networks and genetic
                 functions. A new genetic algorithm (GA) based technique
                 of inductive model construction called Fixed Functional
                 Set Genetic Algorithm (FFSGA) is developed and used in
                 the development of macro-level simulation models. A
                 novel simplified deductive model approach is developed
                 for modelling the response of dissolved oxygen in urban
                 streams impaired by point and non-point sources of
                 pollution. The utility of this inverse loading model in
                 an optimal management framework for urban watersheds is

                 In the context of the optimization methods, the
                 research investigated the use of parallel methods of
                 optimisation for use in the optimal management
                 formulation. These included an evolutionary computing
                 method called genetic optimisation and a modified
                 version of the direct search method of optimisation
                 called the Shuffled Box Complex method of constrained
                 optimisation. The resulting optimal management model
                 obtained by linking macro-level simulation models with
                 efficient optimisation models is capable of identifying
                 optimal management strategies for an urban watershed to
                 satisfy water quality and economic related objectives.
                 Finally, the optimal management model is applied to a
                 real world urban watershed to evaluate management
                 strategies for water quality management leading to the
                 selection of near-optimal strategies.",
  notes =        "Paper 313. First Advisor: Lindell E. Ormsbee",

Genetic Programming entries for Mohammad Tufail