Integrated Modeling System for Multi-ObjectiveWatershed Management

This dissertation presents an integrated modelingsystem for multi-objective management oflife-supporting ecosystem services, which arefrequently impaired by a wide variety of natural andanthropogenic activities in watersheds. The researchwork specifically focuses on maintaining waterquality in streams and rivers through evolution ofagricultural landscapes. The development of thisintegrated modeling system involves coupling ofmulti-objective evolutionary algorithms withphysically-based, distributed hydrologic model. Aspart of this research effort, automatic calibrationtools for the distributed hydrologic model aredeveloped, and sensitivity and uncertainty associatedwith the hydrologic modeling are investigated. Inaddition, the use of artificial intelligencetechniques is explored to reduce the computationaldemand of the integrated modeling system.

Autorentext

Elias G. Bekele, PhD, Hydrologist with the Institute of Natural
Resources Sustainability at UIUC, has published several articles
in peer-reviewed journals and conference proceedings. Dr. Bekele
is Honorable Mention Recipient in the 2007 UCOWR Best
Dissertation Award and is the Runner-up winner in the 2008 SIUC
Outstanding Dissertation Award.

Klappentext

This dissertation presents an integrated modeling
system for multi-objective management of
life-supporting ecosystem services, which are
frequently impaired by a wide variety of natural and
anthropogenic activities in watersheds. The research
work specifically focuses on maintaining water
quality in streams and rivers through evolution of
agricultural landscapes. The development of this
integrated modeling system involves coupling of
multi-objective evolutionary algorithms with
physically-based, distributed hydrologic model. As
part of this research effort, automatic calibration
tools for the distributed hydrologic model are
developed, and sensitivity and uncertainty associated
with the hydrologic modeling are investigated. In
addition, the use of artificial intelligence
techniques is explored to reduce the computational
demand of the integrated modeling system.