Modelling Uncertainties in Long-Term Predictions of Urban Growth: A Coupled Cellular Automata and Agent-Based Approach

Modelling the growth of urban settlements is of considerable interest for different applications, amongst which integrated flood management. This study aims at modelling urban growth for a long time horizon up to 2100 and to integrate the model outcomes with a hydrological model for the same time horizon. Forecasting land-use change over such time frames entails very significant uncertainties. In this regard, the main focus of this paper is attributed to the handling of uncertainty in an urban growth model. To this end, we examine a Monte Carlo Simulation method, which is integrated in the proposed urban growth model. Transition probabilities for each nonurban cell are estimated by a coupled Cellular Automata-Agent-Based approach. The results help to handle uncertainty over long time horizons and to assess the increment in degree of uncertainty at every time-step.

[1]  M. Batty,et al.  Stochastic cellular automata modeling of urban land use dynamics: empirical development and estimation , 2003, Comput. Environ. Urban Syst..

[2]  Célia Ghedini Ralha,et al.  A multi-agent model system for land-use change simulation , 2013, Environ. Model. Softw..

[3]  Annegret H. Thieken,et al.  Spatio-temporal dynamics in the flood exposure due to land use changes in the Alpine Lech Valley in Tyrol (Austria) , 2013, Natural Hazards.

[4]  Elke U. Weber,et al.  An agent based model to simulate structural and land use changes in agricultural systems of the argentine pampas , 2011 .

[5]  Sigrid Reiter,et al.  Energy requirements and solar availability in suburban areas: the influence of density in an existing district , 2011 .

[6]  R. White,et al.  High-resolution integrated modelling of the spatial dynamics of urban and regional systems , 2000 .

[7]  D. Hosmer,et al.  Applied Logistic Regression , 1991 .

[8]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[9]  Benjamin Dewals,et al.  Contribution of land use changes to future flood damage along the river Meuse in the Walloon region , 2013 .

[10]  Fulong Wu,et al.  Calibration of stochastic cellular automata: the application to rural-urban land conversions , 2002, Int. J. Geogr. Inf. Sci..

[11]  Lien Poelmans.  Modelling urban expansion and its hydrological impacts. , 2010 .

[12]  Robert Gilmore Pontius,et al.  Useful techniques of validation for spatially explicit land-change models , 2004 .

[13]  J. Gareth Polhill,et al.  Agent-based land-use models: a review of applications , 2007, Landscape Ecology.

[14]  Xingjian Liu,et al.  Simulating urban expansion using a cloud-based cellular automata model: A case study of Jiangxia, Wuhan, China , 2013 .

[15]  F. Meza,et al.  Assessing spatial dynamics of urban growth using an integrated land use model. Application in Santiago Metropolitan Area, 2010-2045 , 2014 .

[16]  Lien Poelmans,et al.  Complexity and performance of urban expansion models , 2010, Comput. Environ. Urban Syst..

[17]  Linda See,et al.  Calibration of a fuzzy cellular automata model of urban dynamics in Saudi Arabia , 2009 .

[18]  Dawn C. Parker,et al.  Measuring pattern outcomes in an agent-based model of edge-effect externalities using spatial metrics , 2004 .

[19]  Xinhao Wang,et al.  A cellular automata model of land cover change to integrate urban growth with open space conservation , 2011 .

[20]  Andrés Manuel García,et al.  An analysis of the effect of the stochastic component of urban cellular automata models , 2011, Comput. Environ. Urban Syst..

[21]  Mario Cools,et al.  Measuring the Effect of Stochastic Perturbation Component in Cellular Automata Urban Growth Model , 2014 .