利用多源领域知识迁移CA的城市建设用地模拟

基金项目: 国家自然科学基金资助项目(41371376)

详细信息

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

¹School of Geography and Planning,Sun Yat-sen University,Guangzhou 510275,China;²Key Laboratory of Urbanization and Geographical Spatial Simulation,Sun Yat-sen University,Guangzhou 510275,China

Funds: The National Natural Science Foundation of China,No.41371376.

摘要: 目的建立传统元胞自动机(CA)模型时,如果样本数量不足,模拟效果往往非常不理想。提出了多源领域知识迁移CA模型,利用多个已有的旧样本数据集来帮助建立新的CA模型,并选取广东省深圳市作为试验区验证了其有效性。试验结果表明,该模型在新样本数量不足的情况下能够明显改善模拟效果,并且有效减小产生负迁移现象的风险。
- 知识迁移 /
- 元胞自动机模型(CA) /
- 城市土地利用模拟 /
- 多源领域TrAdaBoost算法
Abstract: 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.
- knowledge transfer /
- cellular automata /
- urban simulation /
- multi-source TrAdaBoost