Agent-based modeling and simulations of land-use and land-cover change according to ant colony optimization: a case study of the Erhai Lake Basin, China

The land-use structure and ecological service functions of the Erhai Lake Watershed are being altered by rapid socioeconomic development and urbanization, which will ultimately lead to the generation and aggravation of agricultural and urban non-point source pollution over the entire region. Therefore, the relationships between human activities and land-use/land-cover changes (LUCCs) must be studied to support scientific decisions regarding reasonable land planning and land use. This paper combines geographic information system technology for spatial analysis and the ant colony optimization artificial intelligence algorithm. Moreover, this study applies agent-based modeling to establish a spatiotemporal process model for LUCCs that effectively simulates the dynamic land-use changes in the basin. A selection is first made and evaluated for dynamic land-use change impact factors. Then, the agent classes and their rules in the LUCC processes are established. The program is designed using the Java programming language, and the model is implemented based on the Repast modeling platform. Finally, the models are validated, and the simulated results are analyzed and discussed. Some conclusions were drawn from the experiments, as well as some policies on land use were suggested.

[1]  Ma Xinwei,et al.  Integration of a Site Selection Model with the Multi-Agent System and the Ant Colony Algorithm and Its Application to Changsha , 2011 .

[2]  M. Janssen,et al.  Multi-Agent Systems for the Simulation of Land-Use and LandCover Change : A Review , 2002 .

[3]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[4]  Impacts of Impervious Cover and Other Factors on Storm-Water Quality in Austin, Tex. , 2009 .

[5]  Andrew Crooks,et al.  Perspectives on Agent-Based Models and Geographical Systems , 2012 .

[6]  Peter Nijkamp,et al.  Regional challenges in tourist wetland systems: an integrated approach to the Ria Formosa in the Algarve, Portugal , 2013, Regional Environmental Change.

[7]  Xiaoping Liu,et al.  A multi-type ant colony optimization (MACO) method for optimal land use allocation in large areas , 2012, Int. J. Geogr. Inf. Sci..

[8]  Duncan A. Robertson.  Book and Resource Reviews , 2005 .

[9]  Liang Yan-chun.  Research of optimal selection on parameters of ant colony algorithm , 2007 .

[10]  Tao Jia,et al.  Ant colony algorithms for optimal site selection in large regions , 2009 .

[11]  Eric Vaz,et al.  Spatiotemporal simulation of urban growth patterns using agent-based modeling: The case of Tehran , 2013 .

[12]  Angus R. Simpson,et al.  Ant colony optimization for power plant maintenance scheduling optimization , 2005, GECCO '05.

[13]  J. Southworth,et al.  Evaluation of conservation interventions using a cellular automata-Markov model , 2010 .

[14]  José Manuel Galán.  Agent-Based Models of Geographical Systems by Alison J. Heppenstall, Andrew T. Crooks, Linda M. See and Michael Batty (eds.) , 2012, J. Artif. Soc. Soc. Simul..

[15]  Ye Zhiwei,et al.  Configuration of Parameters α,β,ρ in Ant Algorithm , 2004 .

[16]  Jörg Homberger,et al.  An ant-based coordination mechanism for resource-constrained project scheduling with multiple agents and cash flow objectives , 2013 .

[17]  Yuanhai Li,et al.  Optimal groundwater monitoring design using an ant colony optimization paradigm , 2007, Environ. Model. Softw..

[18]  ZhangChao,et al.  Simulation of land use spatial pattern of towns and villages based on CA-Markov model , 2011 .

[19]  Duncan A. Robertson,et al.  Agent-Based Modeling Toolkits NetLogo, RePast, and Swarm. , 2005 .

[20]  Wai Kuan Foong.  Ant colony optimisation for power plant maintenance scheduling. , 2007 .

[21]  Peter Nijkamp,et al.  A Multi-Scenario Forecast of Urban Change: A Study on Urban Growth in the Algarve , 2011 .

[22]  Jamal Jokar Arsanjani,et al.  ntegration of logistic regression , Markov chain and cellular automata odels to simulate urban expansion amal , 2012 .

[23]  Michael Batty,et al.  A Generic Framework for Computational Spatial Modelling , 2012 .

[24]  A. Hanlon,et al.  Is Catchment Imperviousness a Keystone Factor Degrading Urban Waterways? A Case Study from a Partly Urbanised Catchment (Georges River, South-Eastern Australia) , 2012, Water, Air, & Soil Pollution.

[25]  M. Janssen,et al.  Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A Review , 2003 .

[26]  Andrew Crooks,et al.  Agent-based Models of Geographical Systems , 2012 .

[27]  Nina Schwarz,et al.  ABMland - a Tool for Agent-Based Model Development on Urban Land Use Change , 2012, J. Artif. Soc. Soc. Simul..

[28]  Jianyu Yang,et al.  Simulation of land use spatial pattern of towns and villages based on CA-Markov model , 2011, Math. Comput. Model..

[29]  Andrew Crooks,et al.  Agent-Based Modelling and Simulation using Repast: A Gallery of GIS Applications from CASA , 2006 .

[30]  Peter Nijkamp,et al.  Regional challenges in tourist wetland systems: An integrated approach to the Ria Formosa area , 2013 .