Gradient diffusion entropy corrected ALNS optimization for vegetation topology interaction networks
Vegetation interaction network structures can reveal ecological relationships and interactions between ecological communities such as Grasslands, Croplands and Forests, and are essential for fitting and optimizing ecological numerical systems as well as ecological restoration evolution. In this stud...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X24015012 |
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| author | Shengwei Wang Hongquan Chen Yulin Guo Wenjing Su Yurong Xu Shuohao Cui Zhiqiang Zhou |
| author_facet | Shengwei Wang Hongquan Chen Yulin Guo Wenjing Su Yurong Xu Shuohao Cui Zhiqiang Zhou |
| author_sort | Shengwei Wang |
| collection | DOAJ |
| description | Vegetation interaction network structures can reveal ecological relationships and interactions between ecological communities such as Grasslands, Croplands and Forests, and are essential for fitting and optimizing ecological numerical systems as well as ecological restoration evolution. In this study, two characteristics of the vegetation interaction network constructed through density analysis linking land cover and land class transfer space can realize the objectivity of ecosystem simulation: (1) Constructing relationships between vegetation types using spaces where transfer patterns occur as aggregated clusters. (2) Calculation of relationship strength based on density analysis. In addition the interaction topology network was optimized using the gradient diffusion entropy correction ALNS to further enhance the stability of the proposed ecological interaction network structure. The following findings were obtained by parsing the corresponding study area through the constructed interaction network structure. From 2000 to 2020. All types of ecological nodes showed an increasing trend. the number of ecological nodes in cropland, grassland and forest in 2020 was 27, 8 and 19 respectively. The interaction structure is dominated by competition for cropland and forest in the middle and lower reaches. The GDEO-ALNS optimized interaction structure slows down the ECV drop rate in topological attacks by 0.03 and 0.02, respectively, compared to the original interaction structure, which provides better immunity to interference compared to the optimized structure of ALNS. The optimized vegetation structure consists of three optimal subplot structures that form a sub-ecosystem within the spatial region with a strong anti-disturbance capability. |
| format | Article |
| id | doaj-art-7b2ae1e66d0d4f9dadd7595ce8389934 |
| institution | Kabale University |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-7b2ae1e66d0d4f9dadd7595ce83899342025-01-31T05:10:38ZengElsevierEcological Indicators1470-160X2025-01-01170113044Gradient diffusion entropy corrected ALNS optimization for vegetation topology interaction networksShengwei Wang0Hongquan Chen1Yulin Guo2Wenjing Su3Yurong Xu4Shuohao Cui5Zhiqiang Zhou6Corresponding author.; School of Computer Science and Engineering, Northwest Normal University, Lanzhou 730000, China; Medical College of Northwest Minzu University, Lanzhou 730000, ChinaSchool of Computer Science and Engineering, Northwest Normal University, Lanzhou 730000, China; Medical College of Northwest Minzu University, Lanzhou 730000, ChinaSchool of Computer Science and Engineering, Northwest Normal University, Lanzhou 730000, China; Medical College of Northwest Minzu University, Lanzhou 730000, ChinaSchool of Computer Science and Engineering, Northwest Normal University, Lanzhou 730000, China; Medical College of Northwest Minzu University, Lanzhou 730000, ChinaSchool of Computer Science and Engineering, Northwest Normal University, Lanzhou 730000, China; Medical College of Northwest Minzu University, Lanzhou 730000, ChinaSchool of Computer Science and Engineering, Northwest Normal University, Lanzhou 730000, China; Medical College of Northwest Minzu University, Lanzhou 730000, ChinaSchool of Computer Science and Engineering, Northwest Normal University, Lanzhou 730000, China; Medical College of Northwest Minzu University, Lanzhou 730000, ChinaVegetation interaction network structures can reveal ecological relationships and interactions between ecological communities such as Grasslands, Croplands and Forests, and are essential for fitting and optimizing ecological numerical systems as well as ecological restoration evolution. In this study, two characteristics of the vegetation interaction network constructed through density analysis linking land cover and land class transfer space can realize the objectivity of ecosystem simulation: (1) Constructing relationships between vegetation types using spaces where transfer patterns occur as aggregated clusters. (2) Calculation of relationship strength based on density analysis. In addition the interaction topology network was optimized using the gradient diffusion entropy correction ALNS to further enhance the stability of the proposed ecological interaction network structure. The following findings were obtained by parsing the corresponding study area through the constructed interaction network structure. From 2000 to 2020. All types of ecological nodes showed an increasing trend. the number of ecological nodes in cropland, grassland and forest in 2020 was 27, 8 and 19 respectively. The interaction structure is dominated by competition for cropland and forest in the middle and lower reaches. The GDEO-ALNS optimized interaction structure slows down the ECV drop rate in topological attacks by 0.03 and 0.02, respectively, compared to the original interaction structure, which provides better immunity to interference compared to the optimized structure of ALNS. The optimized vegetation structure consists of three optimal subplot structures that form a sub-ecosystem within the spatial region with a strong anti-disturbance capability.http://www.sciencedirect.com/science/article/pii/S1470160X24015012Gradient diffusion entropy optimizationAdaptive large neighborhood searchVegetation interaction structureTopology optimization |
| spellingShingle | Shengwei Wang Hongquan Chen Yulin Guo Wenjing Su Yurong Xu Shuohao Cui Zhiqiang Zhou Gradient diffusion entropy corrected ALNS optimization for vegetation topology interaction networks Ecological Indicators Gradient diffusion entropy optimization Adaptive large neighborhood search Vegetation interaction structure Topology optimization |
| title | Gradient diffusion entropy corrected ALNS optimization for vegetation topology interaction networks |
| title_full | Gradient diffusion entropy corrected ALNS optimization for vegetation topology interaction networks |
| title_fullStr | Gradient diffusion entropy corrected ALNS optimization for vegetation topology interaction networks |
| title_full_unstemmed | Gradient diffusion entropy corrected ALNS optimization for vegetation topology interaction networks |
| title_short | Gradient diffusion entropy corrected ALNS optimization for vegetation topology interaction networks |
| title_sort | gradient diffusion entropy corrected alns optimization for vegetation topology interaction networks |
| topic | Gradient diffusion entropy optimization Adaptive large neighborhood search Vegetation interaction structure Topology optimization |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X24015012 |
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