标题: 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)
来源出版物: GISCIENCE & REMOTE SENSING 卷: 61 期: 1 文献号: 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.
作者关键词: 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.
通讯作者地址: Wang, HJ (通讯作者)，Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.
Wang, HJ (通讯作者)，Minist Nat Resources, Key Lab Trop & Subtrop Nat Resources Monitoring So, Guangzhou, Peoples R China.
电子邮件地址: landgiswhj@163.com
影响因子：6.7
