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Information 2018, 9(8), 193;

Application of an Improved ABC Algorithm in Urban Land Use Prediction

School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
Author to whom correspondence should be addressed.
Received: 1 June 2018 / Revised: 25 July 2018 / Accepted: 26 July 2018 / Published: 29 July 2018
(This article belongs to the Section Information Applications)
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Scientifically and rationally analyzing the characteristics of land use evolution and exploring future trends in land use changes can provide the scientific reference basis for the rational development and utilization of regional land resources and sustainable economic development. In this paper, an improved hybrid artificial bee colony (ABC) algorithm based on the mutation of inferior solutions (MHABC) is introduced to combine with the cellular automata (CA) model to implement a new CA rule mining algorithm (MHABC-CA). To verify the capabilities of this algorithm, remote sensing data of three stages, 2005, 2010, and 2015, are adopted to dynamically simulate urban development of Dengzhou city in Henan province, China, using the MHABC-CA algorithm. The comprehensive validation and analysis of the simulation results are performed by two aspects of comparison, the visual features of urban land use types and the quantification analysis of simulation accuracy. Compared with a cellular automata model based on a particle swarm optimization (PSO-CA) algorithm, the experimental results demonstrate the effectiveness of the MHABC-CA algorithm in the prediction field of urban land use changes. View Full-Text
Keywords: ABC algorithm; cellular automata; rule mining; land use change prediction ABC algorithm; cellular automata; rule mining; land use change prediction

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Huo, J.; Zhang, Z. Application of an Improved ABC Algorithm in Urban Land Use Prediction. Information 2018, 9, 193.

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