Knowledge Transfer and Adaptation for Urban Simulation Cellular AutomataModel Base on Multi-source TrAdaBoost Algorithm

Objective Traditional cellular automata(CA)cannot adequately simulate urban dynamics and land-usechanges when there are insufficient training samples.To address this problem,we propose a multi-source knowledge transfer CA model.This model utilizes several existing label data sets to help traina new model.This proposed model,MSTra CA,is employed to urban simulation in Shenzhen City inGuangdong Province of China.Experiments have demonstrated that the proposed method can alleviatethe sparse data problem using knowledge transfer thus reducing the negative transfer learning risk.