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宋友城(博士生)、王海軍的論文在GISCIENCE & REMOTE SENSING刊出
發布時間:2024-05-31     發布者:易真         審核者:     浏覽次數:

标題: An adaptive transition probability matrix with quality seeds for cellular automata models

作者: Song, YC (Song, Youcheng); Xu, HT (Xu, Hongtao); Wang, HJ (Wang, Haijun); Zhu, ZY (Zhu, Ziyang); Kang, XY (Kang, Xinyi); Cao, XX (Cao, Xiaoxu); Bin, Z (Bin, Zhang); Zeng, HR (Zeng, Haoran)

來源出版物: GISCIENCE & REMOTE SENSING  : 61  : 1  文獻号: 2347719  DOI: 10.1080/15481603.2024.2347719  Published Date: 2024 DEC 31  

摘要: 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.

作者關鍵詞: Land use and land cover change; land use simulation; cellular automata; genetic algorithm

地址: [Song, Youcheng; Wang, Haijun; Cao, Xiaoxu; Zeng, Haoran] Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.

[Song, Youcheng; Wang, Haijun; Zhu, Ziyang; Kang, Xinyi] Minist Nat Resources, Key Lab Trop & Subtrop Nat Resources Monitoring So, Guangzhou, Peoples R China.

[Xu, Hongtao] Beijing Normal Univ, Coll Water Sci, Beijing, Peoples R China.

[Zhu, Ziyang; Kang, Xinyi] Surveying & Mapping Inst, Lands & Resource Dept Guangdong Prov, Guangzhou, Peoples R China.

[Bin, Zhang] China Univ Geosci, Sch Publ Adm, Wuhan, Peoples R China.

通訊作者地址: Wang, HJ (通訊作者)Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.

Wang, HJ (通訊作者)Minist Nat Resources, Key Lab Trop & Subtrop Nat Resources Monitoring So, Guangzhou, Peoples R China.

電子郵件地址: landgiswhj@163.com

影響因子:6.7


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