Frontiers of Data and Computing ›› 2020, Vol. 2 ›› Issue (3): 137-145.doi: 10.11871/jfdc.issn.2096-742X.2020.03.012

• Technology and Applicaton • Previous Articles    

Artificial Intelligence Modeling of Land Use Evolution Process

Yang Jianpeng1,2(),Luo Ze1,*(),Zhang Yingming3()   

  1. 1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
    3. Guangdong Chebaling National Nature Reserve Administration, Shixing, Guangdong 512526, China
  • Received:2020-03-13 Online:2020-06-20 Published:2020-08-19
  • Contact: Luo Ze E-mail:yangjianpeng@cnic.cn;luoze@cnic.cn;cblbhq@163.com

Abstract:

[Objective] Land is the basis for human survival and development. It is of great significance to study how the land use changes the human economy, politics and the environment. In order to provide references for land use evolution research and understand the research status on land use clearly and thoroughly, a new model is proposed in this paper. [Methods] This paper takes the Chebaling Ecological Reserve in Guangdong Province as the study case for model verification in land use simulations. The model combines the Recurrent Neural Network based on LSTM and Cellular Automata to study the land use change data in 2005-2017. Spatial analysis of Chebaling Ecological Reserve is based on massive vector and raster data are made with the aid of ArcGIS 10.2. Driving force mechanism as studied by constructing the fourteen spatial variables which include natural factors, social factors, distance factors and so on. In addition, the experiments are conducted by different threshold settings and random disturbance adoptions for better simulation improvements in terms of accuracy. [Results] The simulated results are shown as follows: the improved model has higher precision and Kappa coefficient than traditional models. Besides, the threshold and random disturbance can be set conveniently by the new model. [Limitations] In the experiment, the number and types of spatial variables are relatively insufficient, and more relevant variables and driving factors need to be considered in later studies.[Conclusions] The proposed LSTM-based RNN-CA model verified by the improved simulation results satisfies the requirements and provides references for land use evolution researches.

Key words: Recurrent Neural Network(RNN), Cellular Automaton(CA), land use, Long Short-Term Memory network(LSTM)