An adaptive transition probability matrix with quality seeds for cellular automata models
The cellular automata (CA) model is the predominant method for predicting land use and land cover (LULC) changes. The accuracy of this model critically depends on well-defined transition rules, which encapsulate the local dynamics of complex systems and facilitate the manifestation of organized glob...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | GIScience & Remote Sensing |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/15481603.2024.2347719 |
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| author | Youcheng Song Xu Hongtao Haijun Wang Ziyang Zhu Xinyi Kang Xiaoxu Cao Zhang Bin Haoran Zeng |
| author_facet | Youcheng Song Xu Hongtao Haijun Wang Ziyang Zhu Xinyi Kang Xiaoxu Cao Zhang Bin Haoran Zeng |
| author_sort | Youcheng Song |
| collection | DOAJ |
| description | The cellular automata (CA) model is the predominant method for predicting land use and land cover (LULC) changes. The accuracy of this model critically depends on well-defined transition rules, which encapsulate the local dynamics of complex systems and facilitate the manifestation of organized global patterns. While current studies largely concentrate on land use transition matrices as core elements of these rules, exclusive reliance on these matrices is insufficient for capturing the full spectrum of land use change potential. Addressing this gap, our research introduces the adaptive transition probability matrix with quality seeds (ATPMS) model, which incorporates both the Markov model and the genetic algorithm (GA) into the traditional CA framework. Furthermore, an artificial neural network (ANN) is utilized to determine land suitability. Implemented in Beijing, Wuhan, and the Pearl River Delta (PRD), our results indicate that the ATPMS-ANN-CA model surpasses the standard Markov-ANN-CA model in various validation metrics, displaying improvements in overall accuracy (OA) by 0.03% to 0.74% and figure of merit (FoM) by 3.67% to 63.14%. Additionally, the ATPMS-ANN-CA model excels in providing detailed landscape analysis. |
| format | Article |
| id | doaj-art-bef2023bfec84b70a4f8411b0d55016d |
| institution | OA Journals |
| issn | 1548-1603 1943-7226 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | GIScience & Remote Sensing |
| spelling | doaj-art-bef2023bfec84b70a4f8411b0d55016d2025-08-20T01:59:30ZengTaylor & Francis GroupGIScience & Remote Sensing1548-16031943-72262024-12-0161110.1080/15481603.2024.2347719An adaptive transition probability matrix with quality seeds for cellular automata modelsYoucheng Song0Xu Hongtao1Haijun Wang2Ziyang Zhu3Xinyi Kang4Xiaoxu Cao5Zhang Bin6Haoran Zeng7School of Resource and Environmental Science, Wuhan University, Wuhan, ChinaCollege of Water Sciences, Beijing Normal University, Beijing, ChinaSchool of Resource and Environmental Science, Wuhan University, Wuhan, ChinaKey Laboratory of Tropical and Subtropical Natural Resources Monitoring in South China, Ministry of Natural Resources, Guangzhou, ChinaKey Laboratory of Tropical and Subtropical Natural Resources Monitoring in South China, Ministry of Natural Resources, Guangzhou, ChinaSchool of Resource and Environmental Science, Wuhan University, Wuhan, ChinaSchool of Public Administration, China University of Geosciences, Wuhan, ChinaSchool of Resource and Environmental Science, Wuhan University, Wuhan, ChinaThe cellular automata (CA) model is the predominant method for predicting land use and land cover (LULC) changes. The accuracy of this model critically depends on well-defined transition rules, which encapsulate the local dynamics of complex systems and facilitate the manifestation of organized global patterns. While current studies largely concentrate on land use transition matrices as core elements of these rules, exclusive reliance on these matrices is insufficient for capturing the full spectrum of land use change potential. Addressing this gap, our research introduces the adaptive transition probability matrix with quality seeds (ATPMS) model, which incorporates both the Markov model and the genetic algorithm (GA) into the traditional CA framework. Furthermore, an artificial neural network (ANN) is utilized to determine land suitability. Implemented in Beijing, Wuhan, and the Pearl River Delta (PRD), our results indicate that the ATPMS-ANN-CA model surpasses the standard Markov-ANN-CA model in various validation metrics, displaying improvements in overall accuracy (OA) by 0.03% to 0.74% and figure of merit (FoM) by 3.67% to 63.14%. Additionally, the ATPMS-ANN-CA model excels in providing detailed landscape analysis.https://www.tandfonline.com/doi/10.1080/15481603.2024.2347719Land use and land cover changeland use simulationcellular automatagenetic algorithm |
| spellingShingle | Youcheng Song Xu Hongtao Haijun Wang Ziyang Zhu Xinyi Kang Xiaoxu Cao Zhang Bin Haoran Zeng An adaptive transition probability matrix with quality seeds for cellular automata models GIScience & Remote Sensing Land use and land cover change land use simulation cellular automata genetic algorithm |
| title | An adaptive transition probability matrix with quality seeds for cellular automata models |
| title_full | An adaptive transition probability matrix with quality seeds for cellular automata models |
| title_fullStr | An adaptive transition probability matrix with quality seeds for cellular automata models |
| title_full_unstemmed | An adaptive transition probability matrix with quality seeds for cellular automata models |
| title_short | An adaptive transition probability matrix with quality seeds for cellular automata models |
| title_sort | adaptive transition probability matrix with quality seeds for cellular automata models |
| topic | Land use and land cover change land use simulation cellular automata genetic algorithm |
| url | https://www.tandfonline.com/doi/10.1080/15481603.2024.2347719 |
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