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Simulation of urban expansion via integrating artificial neural network with Markov chain – cellular automata.
- Source :
-
International Journal of Geographical Information Science . Oct2019, Vol. 33 Issue 10, p1960-1983. 24p. - Publication Year :
- 2019
-
Abstract
- Accurate simulations and predictions of urban expansion are critical to manage urbanization and explicitly address the spatiotemporal trends and distributions of urban expansion. Cellular Automata integrated Markov Chain (CA-MC) is one of the most frequently used models for this purpose. However, the urban suitability index (USI) map produced from the conventional CA-MC is either affected by human bias or cannot accurately reflect the possible nonlinear relations between driving factors and urban expansion. To overcome these limitations, a machine learning model (Artificial Neural Network, ANN) was integrated with CA-MC instead of the commonly used Analytical Hierarchy Process (AHP) and Logistic Regression (LR) CA-MC models. The ANN was optimized to create the USI map and then integrated with CA-MC to spatially allocate urban expansion cells. The validated results of kappa and fuzzy kappa simulation indicate that ANN-CA-MC outperformed other variously coupled CA-MC modelling approaches. Based on the ANN-CA-MC model, the urban area in South Auckland is predicted to expand to 1340.55 ha in 2026 at the expense of non-urban areas, mostly grassland and open-bare land. Most of the future expansion will take place within the planned new urban growth zone. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13658816
- Volume :
- 33
- Issue :
- 10
- Database :
- Academic Search Index
- Journal :
- International Journal of Geographical Information Science
- Publication Type :
- Academic Journal
- Accession number :
- 137843851
- Full Text :
- https://doi.org/10.1080/13658816.2019.1600701