A land-use spatial optimization model based on genetic optimization and game theory

Tang, Wei; He, Jifeng; Liu, Yanfang; Ai, Tinghua; Liu, Dianfeng

doi:10.1016/j.compenvurbsys.2014.09.002

Cited by 109 publications

(70 citation statements)

References 33 publications

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“…making sure the allocated area of crop j on pixel i would be less than suitable cultivated land area of crop j on pixel i: Area j × p ij ≤ Suitable ij (12) making sure the allocated area on pixel i would be less than the arable land area on pixel i:…”

Section: Spatial Distribution Optimization Model Based On Cross Entropymentioning

confidence: 99%

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Spatial Optimization of Agricultural Land Use Based on Cross-Entropy Method

Hao

Singh

et al. 2017

Entropy

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An integrated optimization model was developed for the spatial distribution of agricultural crops in order to efficiently utilize agricultural water and land resources simultaneously. The model is based on the spatial distribution of crop suitability, spatial distribution of population density, and agricultural land use data. Multi-source remote sensing data are combined with constraints of optimal crop area, which are obtained from agricultural cropping pattern optimization model. Using the middle reaches of the Heihe River basin as an example, the spatial distribution of maize and wheat were optimized by minimizing cross-entropy between crop distribution probabilities and desired but unknown distribution probabilities. Results showed that the area of maize should increase and the area of wheat should decrease in the study area compared with the situation in 2013. The comprehensive suitable area distribution of maize is approximately in accordance with the distribution in the present situation; however, the comprehensive suitable area distribution of wheat is not consistent with the distribution in the present situation. Through optimization, the high proportion of maize and wheat area was more concentrated than before. The maize area with more than 80% allocation concentrates on the south of the study area, and the wheat area with more than 30% allocation concentrates on the central part of the study area. The outcome of this study provides a scientific basis for farmers to select crops that are suitable in a particular area.

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Section: Spatial Distribution Optimization Model Based On Cross Entropymentioning

confidence: 99%

“…To assess the agricultural land suitability of crops, the required environmental and socio-economic conditions are considered [12]. The Food and Agricultural Organization (FAO) developed crop-specific…”

Section: Introductionmentioning

confidence: 99%

Spatial Optimization of Agricultural Land Use Based on Cross-Entropy Method

Hao

Singh

et al. 2017

Entropy

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“…The model was applied to a case study in California and solved by genetic algorithm. Liu et al [7] performed land-use spatial optimization by coordinating the competitions between different land-use types in the example of Gaoqiao Town, Zhejiang Province, China. Li and Parrott [8], Haque and Asami [9], Taromi et al [10] and Zhang and Huang [11] reached a consensus that evolutionary algorithm-based model is powerful tool in optimizing land use allocation in the cases of the Delaware, USA and Shenzhen, China, etc.…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm-based Optimal Investment Scheduling for Public Rental Housing Projects in South Korea

Park¹,

Yu²,

Geem³

2018

IJFIS

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Declining birthrate is a serious problem that threatens the sustainability of Korean society. The main cause of this phenomenon is high living cost where housing cost accounts for the majority in household expenditure. South Korea has a very low supply rate in public rental housing when compared to other OECD countries. Because young people cannot afford to buy or lease a house for their new houses, some of them postpone or even give up marriage. As a countermeasure, Gyeonggi Province (surrounding area of Seoul) recently announced the supplying plan of 10,000 public rental houses by 2020. We expect this measure to alleviate the low birthrate problem and increase the demographic sustainability of the province. This study optimizes multi-annual investment scheduling for rental housing projects using genetic algorithm while satisfying the constraints such as budget, human resources, regional balance, etc. Through the optimal investment scheduling, we hope that public corporation will supply public rental houses more efficiently and more sustainably for the community.

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“…One important issue in CA modeling is the quantification of the impacts of the factors that drive urban growth and land use change at both global and local scales. Many approaches have been developed to define CA transition rules and each is aimed at improving the overall accuracy and reducing errors of simulation [21][22][23][24]. These approaches vary widely in theoretical assumptions, underlying methodologies, and spatio-temporal resolutions and extents [25].…”

Section: Introductionmentioning

confidence: 99%

Simulation of Dynamic Urban Growth with Partial Least Squares Regression-Based Cellular Automata in a GIS Environment

Feng

Liu

Chen

et al. 2016

IJGI

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Abstract:We developed a geographic cellular automata (CA) model based on partial least squares (PLS) regression (termed PLS-CA) to simulate dynamic urban growth in a geographical information systems (GIS) environment. The PLS method extends multiple linear regression models that are used to define the unique factors driving urban growth by eliminating multicollinearity among the candidate drivers. The key factors (the spatial variables) extracted are uncorrelated, resulting in effective transition rules for urban growth modeling. The PLS-CA model was applied to simulate the rapid urban growth of Songjiang District, an outer suburb in the Shanghai Municipality of China from 1992 to 2008. Among the three components acquired by PLS, the first two explained more than 95% of the total variance. The results showed that the PLS-CA simulated pattern of urban growth matched the observed pattern with an overall accuracy of 85.8%, as compared with 83.5% of a logistic-regression-based CA model for the same area. The PLS-CA model is readily applicable to simulations of urban growth in other rapidly urbanizing areas to generate realistic land use patterns and project future scenarios.

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A land-use spatial optimization model based on genetic optimization and game theory

Cited by 109 publications

References 33 publications

Spatial Optimization of Agricultural Land Use Based on Cross-Entropy Method

Spatial Optimization of Agricultural Land Use Based on Cross-Entropy Method

Genetic Algorithm-based Optimal Investment Scheduling for Public Rental Housing Projects in South Korea

Simulation of Dynamic Urban Growth with Partial Least Squares Regression-Based Cellular Automata in a GIS Environment

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