Knowledge transfer and adaptation for land-use simulation with a logistic cellular automaton

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, CA_trans, is tested by choosing different periods and study areas in the Pearl River Delta. The 'Figure of Merit' measurements in the experiments indicate that CA_trans can yield better simulation results. The variance of traditional logistic-CA is about 2-5 times the variance of CA_trans 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.