Optimal water allocation using a multi-objective evolutionary algorithm

Ullah, G. M. Wali (2020) Optimal water allocation using a multi-objective evolutionary algorithm. [Thesis (PhD/Research)]

[img]
Preview
Text (Whole Thesis)
0061110143_MSc_final_thesis.pdf

Download (2MB) | Preview

Abstract

Agriculture water management in Bangladesh has become a subject of increasing attention due to population growth. Therefore, it is necessary that we optimize water use in order to increase the agricultural production with
the increasing needs of the population as well as to fulfil the need for a sound economy of the country as a whole.

The research engages with the optimum allocation of water in the agricultural sector of Bangladesh. We model the problem using multi-objective constrained optimization problem. The objectives in this problem are to maximize net return and minimizing deficit in environmental flow. A Non-Dominating Sorting Genetic Algorithm, NSGA-II, is used to solve the problem in this research to find the optimum result.

The research indicates that the crops which are produced more and are more profitable in trade should be cultivated more as recommended by the model. The model predictions indicate that rainfall impacts on net return and environmental flow deficit more than water inflow under the scenarios in the Muhuri Irrigation Project (MIP) considered.


Statistics for USQ ePrint 39909
Statistics for this ePrint Item
Item Type: Thesis (PhD/Research)
Item Status: Live Archive
Additional Information: Master of Science (Research) thesis.
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Faculty/School / Institute/Centre: Current - Faculty of Health, Engineering and Sciences - School of Sciences (6 Sep 2019 -)
Supervisors: Langlands, Trevor; Addie, Ron
Date Deposited: 16 Oct 2020 01:11
Last Modified: 23 Jul 2021 22:05
Uncontrolled Keywords: Muhuri Irrigation Project, net return, environmental flow deficit, multi-objective optimization problem, evolutionary algorithm, NSGAII
Fields of Research (2008): 01 Mathematical Sciences > 0102 Applied Mathematics > 010206 Operations Research
Fields of Research (2020): 49 MATHEMATICAL SCIENCES > 4901 Applied mathematics > 490108 Operations research
Identification Number or DOI: doi:10.26192/04p7-fp29
URI: http://eprints.usq.edu.au/id/eprint/39909

Actions (login required)

View Item Archive Repository Staff Only