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Spatial multi-objective optimization approach for land use allocation using NSGA-II

Shaygan, Mehran; Alimohammadi, Abbas; Mansourian, Ali LU ; Govara, Zohreh Shams and Kalami, S. Mostapha (2014) In IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7(3). p.873-883
Abstract

Analysis and evaluation of land use patterns are of prime importance for natural resources management. Recent studies on land use allocation have been mainly based on linear programming optimization. Although these methods have the ability to solve multi-objective problems, spatial aspects of optimization are not considered when they are used for land use management. This study applied the non-dominated sorting genetic algorithm II (NSGA-II) to optimize land-use allocation in the Taleghan watershed, northwest of Karaj, Iran. The four land use classes of irrigated farming, dry farming, rangeland, and other uses were extracted from the ETM+ image. The objective functions of the proposed model were erosion, economic return, suitability,... (More)

Analysis and evaluation of land use patterns are of prime importance for natural resources management. Recent studies on land use allocation have been mainly based on linear programming optimization. Although these methods have the ability to solve multi-objective problems, spatial aspects of optimization are not considered when they are used for land use management. This study applied the non-dominated sorting genetic algorithm II (NSGA-II) to optimize land-use allocation in the Taleghan watershed, northwest of Karaj, Iran. The four land use classes of irrigated farming, dry farming, rangeland, and other uses were extracted from the ETM+ image. The objective functions of the proposed model were erosion, economic return, suitability, and compactness-compatibility. A novel crossover operator called exchange randomly block (ERB) was used to exchange information between individuals. Results showed that the optimization model can find a set of optimal land use combinations in accordance with the proposed conditions. For comparison purposes, land use allocation was also done using the combined goal attainment-multi-objective land allocation (GoA-MOLA) approach. The results showed that NSGA-II performance acceptably when compared to GoA-MOLA.

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author
publishing date
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Contribution to journal
publication status
published
subject
keywords
land use, Multi-objective optimization, non-dominated sorting genetic algorithm II, taleghan watershed
in
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
volume
7
issue
3
pages
11 pages
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
external identifiers
  • scopus:84897112249
ISSN
1939-1404
DOI
10.1109/JSTARS.2013.2280697
language
English
LU publication?
no
id
b0aea8ba-dbfb-48cf-935e-e1cc2dcda689
date added to LUP
2016-05-27 15:32:15
date last changed
2017-10-08 04:49:38
@article{b0aea8ba-dbfb-48cf-935e-e1cc2dcda689,
  abstract     = {<p>Analysis and evaluation of land use patterns are of prime importance for natural resources management. Recent studies on land use allocation have been mainly based on linear programming optimization. Although these methods have the ability to solve multi-objective problems, spatial aspects of optimization are not considered when they are used for land use management. This study applied the non-dominated sorting genetic algorithm II (NSGA-II) to optimize land-use allocation in the Taleghan watershed, northwest of Karaj, Iran. The four land use classes of irrigated farming, dry farming, rangeland, and other uses were extracted from the ETM+ image. The objective functions of the proposed model were erosion, economic return, suitability, and compactness-compatibility. A novel crossover operator called exchange randomly block (ERB) was used to exchange information between individuals. Results showed that the optimization model can find a set of optimal land use combinations in accordance with the proposed conditions. For comparison purposes, land use allocation was also done using the combined goal attainment-multi-objective land allocation (GoA-MOLA) approach. The results showed that NSGA-II performance acceptably when compared to GoA-MOLA.</p>},
  articleno    = {6676851},
  author       = {Shaygan, Mehran and Alimohammadi, Abbas and Mansourian, Ali and Govara, Zohreh Shams and Kalami, S. Mostapha},
  issn         = {1939-1404},
  keyword      = {land use,Multi-objective optimization,non-dominated sorting genetic algorithm II,taleghan watershed},
  language     = {eng},
  number       = {3},
  pages        = {873--883},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  series       = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
  title        = {Spatial multi-objective optimization approach for land use allocation using NSGA-II},
  url          = {http://dx.doi.org/10.1109/JSTARS.2013.2280697},
  volume       = {7},
  year         = {2014},
}