Modeling spatial climate change landuse adaptation with multi-objective genetic algorithms to improve resilience for rice yield and species richness and to mitigate disaster risk Article Swipe
Eun Joo Yoon
,
James H. Thorne
,
Chan Park
,
Dong Kun Lee
,
Kwang Soo Kim
,
Heeyeun Yoon
,
Changwan Seo
,
Chul-Hee Lim
,
Haeryung Kim
,
Young-Il Song
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1088/1748-9326/aaf0cf
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1088/1748-9326/aaf0cf
As climate change is ongoing, many studies have recently focused on adaptation to climate change from a spatial perspective. However little is known about how changing the spatial composition of landuse could improve climate change resilience. Consideration of climate change impacts when spatially allocating landuse could be a useful and fundamental long term adaptation strategy, particularly for regional planning. Here, we identify climate adaptation scenarios based on existing extents of three landuse classes using multi-objective genetic algorithms for a 9982 km 2 region with 3.5 million inhabitants in South Korea. We selected five objectives for adaptation based on predicted climate change impacts and regional economic conditions: minimization of disaster damage and existing landuse conversion, maximization of rice yield, protection of high species richness areas, and economic value. We generated 17 Pareto landuse scenarios by six weighted combinations of the adaptation objectives. Most scenarios, although varying in magnitude, showed better performance than the current spatial landuse composition for all adaptation objectives, suggesting that some alteration of current landuse patterns could increase overall climate resilience. Given the flexible structure of the optimization model, we expect that regional stakeholders could efficiently generate other scenarios by adjusting model parameters (weighting combinations) or replacing input data (impact maps), and selecting a scenario depending on preference or a number of problem-related factors.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1748-9326/aaf0cf
- OA Status
- gold
- Cited By
- 18
- References
- 55
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2900637780
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2900637780Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1748-9326/aaf0cfDigital Object Identifier
- Title
-
Modeling spatial climate change landuse adaptation with multi-objective genetic algorithms to improve resilience for rice yield and species richness and to mitigate disaster riskWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2018Year of publication
- Publication date
-
2018-11-14Full publication date if available
- Authors
-
Eun Joo Yoon, James H. Thorne, Chan Park, Dong Kun Lee, Kwang Soo Kim, Heeyeun Yoon, Changwan Seo, Chul-Hee Lim, Haeryung Kim, Young-Il SongList of authors in order
- Landing page
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https://doi.org/10.1088/1748-9326/aaf0cfPublisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1088/1748-9326/aaf0cfDirect OA link when available
- Concepts
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Land use, Climate change, Environmental resource management, Species richness, Computer science, Adaptation (eye), Weighting, Psychological resilience, Spatial ecology, Environmental science, Geography, Ecology, Radiology, Medicine, Psychology, Optics, Physics, Psychotherapist, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
18Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 3, 2023: 4, 2022: 3, 2021: 2Per-year citation counts (last 5 years)
- References (count)
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55Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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