- 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
investigated.
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",
