Knowledge transfer and adaptation for land-use simulation with a logistic cellular automaton |
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| Authors: | Xia Li Yilun Liu Xiaoping Liu Yimin Chen Bin Ai |
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| Institution: | 1. School of Geography and Planning, and Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou, 510275, PR, Chinalixia@mail.sysu.edu.cnlixia@graduate.hku.hk;4. School of Geography and Planning, and Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou, 510275, PR, China |
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| Abstract: | Few studies have been conducted into the use of knowledge transfer for tackling geo-simulation problems. Cellular automata (CA) have proven to be an effective and convenient means of simulating urban dynamics and land-use changes. Gathering the knowledge required to build the CA may be difficult when these models are applied to large areas or long periods. In this paper, we will explore the possibility that the knowledge from previously collected data can be transferred spatially (a different region) and/or temporally (a different period) for implementing urban CA. The domain adaptation of CA is demonstrated by integrating logistic-CA with a knowledge-transfer technique, the TrAdaBoost algorithm. A modification has been made to the TrAdaBoost algorithm by incorporating a dynamicweight-trimming technique. This proposed model, CAtrans, is tested by choosing different periods and study areas in the Pearl River Delta. The ‘Figure of Merit’ measurements in the experiments indicate that CAtrans can yield better simulation results. The variance of traditional logistic-CA is about 2–5 times the variance of CAtrans until the number of new data reaches 30. The experiments have demonstrated that the proposed method can alleviate the sparse data problem using knowledge transfer. |
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| Keywords: | knowledge transfer cellular automata urban simulation model adaptation |
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