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Sustainable and Resilient Land Use Planning : A Multi-Objective Optimization Approach

Sicuaio, Tomé LU ; Zhao, Pengxiang LU ; Pilesjo, Petter LU ; Shindyapin, Andrey and Mansourian, Ali LU (2024) In ISPRS International Journal of Geo-Information 13(3).
Abstract

Land use allocation (LUA) is of prime importance for the development of urban sustainability and resilience. Since the process of planning and managing land use requires balancing different conflicting social, economic, and environmental factors, it has become a complex and significant issue in urban planning worldwide. LUA is usually regarded as a spatial multi-objective optimization (MOO) problem in previous studies. In this paper, we develop an MOO approach for tackling the LUA problem, in which maximum economy, minimum carbon emissions, maximum accessibility, maximum integration, and maximum compactness are formulated as optimal objectives. To solve the MOO problem, an improved non-dominated sorting genetic algorithm III (NSGA-III)... (More)

Land use allocation (LUA) is of prime importance for the development of urban sustainability and resilience. Since the process of planning and managing land use requires balancing different conflicting social, economic, and environmental factors, it has become a complex and significant issue in urban planning worldwide. LUA is usually regarded as a spatial multi-objective optimization (MOO) problem in previous studies. In this paper, we develop an MOO approach for tackling the LUA problem, in which maximum economy, minimum carbon emissions, maximum accessibility, maximum integration, and maximum compactness are formulated as optimal objectives. To solve the MOO problem, an improved non-dominated sorting genetic algorithm III (NSGA-III) is proposed in terms of mutation and crossover operations by preserving the constraints on the sizes for each land use type. The proposed approach was applied to KaMavota district, Maputo City, Mozambique, to generate a proper land use plan. The results showed that the improved NSGA-III yielded better performance than the standard NSGA-III. The optimal solutions produced by the MOO approach provide good trade-offs between the conflicting objectives. This research is beneficial for policymakers and city planners by providing alternative land use allocation plans for urban sustainability and resilience.

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author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
land use planning, multi-objective optimization, NSGA-III, sustainability and resilience, Geospatial Artificial Intelligence (GeoAI)
in
ISPRS International Journal of Geo-Information
volume
13
issue
3
article number
99
publisher
MDPI AG
external identifiers
  • scopus:85189010620
ISSN
2220-9964
DOI
10.3390/ijgi13030099
language
English
LU publication?
yes
id
fe5c53f4-09ff-4042-9d92-d943928e5fe4
date added to LUP
2024-04-12 13:33:27
date last changed
2024-04-12 13:51:08
@article{fe5c53f4-09ff-4042-9d92-d943928e5fe4,
  abstract     = {{<p>Land use allocation (LUA) is of prime importance for the development of urban sustainability and resilience. Since the process of planning and managing land use requires balancing different conflicting social, economic, and environmental factors, it has become a complex and significant issue in urban planning worldwide. LUA is usually regarded as a spatial multi-objective optimization (MOO) problem in previous studies. In this paper, we develop an MOO approach for tackling the LUA problem, in which maximum economy, minimum carbon emissions, maximum accessibility, maximum integration, and maximum compactness are formulated as optimal objectives. To solve the MOO problem, an improved non-dominated sorting genetic algorithm III (NSGA-III) is proposed in terms of mutation and crossover operations by preserving the constraints on the sizes for each land use type. The proposed approach was applied to KaMavota district, Maputo City, Mozambique, to generate a proper land use plan. The results showed that the improved NSGA-III yielded better performance than the standard NSGA-III. The optimal solutions produced by the MOO approach provide good trade-offs between the conflicting objectives. This research is beneficial for policymakers and city planners by providing alternative land use allocation plans for urban sustainability and resilience.</p>}},
  author       = {{Sicuaio, Tomé and Zhao, Pengxiang and Pilesjo, Petter and Shindyapin, Andrey and Mansourian, Ali}},
  issn         = {{2220-9964}},
  keywords     = {{land use planning; multi-objective optimization; NSGA-III; sustainability and resilience; Geospatial Artificial Intelligence (GeoAI)}},
  language     = {{eng}},
  number       = {{3}},
  publisher    = {{MDPI AG}},
  series       = {{ISPRS International Journal of Geo-Information}},
  title        = {{Sustainable and Resilient Land Use Planning : A Multi-Objective Optimization Approach}},
  url          = {{http://dx.doi.org/10.3390/ijgi13030099}},
  doi          = {{10.3390/ijgi13030099}},
  volume       = {{13}},
  year         = {{2024}},
}