Integration of Local and Global Support Vector Machines to Improve Urban Growth Modelling

The use of local information for the classification and modelling of spatial variables has increased with the application of statistical and machine learning algorithms, such as support vector machines (SVMs). This study presents a new local SVM (LSVM) model that was developed to model the probabili...

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Bibliographic Details
Published in:ISPRS international journal of geo-information 2018-09, Vol.7 (9), p.347
Main Authors: Mirbagheri, Babak, Alimohammadi, Abbas
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial intelligence
Cellular automata
Computer simulation
Geographic information systems
Growth
Land use
Learning algorithms
Machine learning
Metropolitan areas
Modelling
probability maps
Probability theory
Signal processing
Spatial data
Statistical analysis
Studies
Support vector machines
Training
Urban areas
Urban development
Urban planning
Urban sprawl
Urbanization
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Summary:The use of local information for the classification and modelling of spatial variables has increased with the application of statistical and machine learning algorithms, such as support vector machines (SVMs). This study presents a new local SVM (LSVM) model that was developed to model the probability of urban development and simulate urban growth in a subregion in the southwestern suburb of the Tehran metropolitan area, Iran, for the periods of 1992–1996 and 1996–2002. Based on the focal training sample, the model was calibrated using the cross-validation method, and the optimal bandwidth was determined. The results were compared with those of a nonlinear global SVM (GSVM) model that was calibrated based on the ten-fold cross-validation method. This study then evaluated an integrated SVM model (LGSVM) obtained based on a weighted combination of the local and global urban development probabilities. A comparison of the probability maps showed a higher accuracy for the LGSVM than for either the LSVM or GSVM model. To assess the performance of the LSVM, GSVM and LGSVM models in the simulation of urban growth, probability maps were employed as the transition rules for urban cellular automata. The results show that a trade-off between local and global SVM models can enhance the performance of urban growth modelling.
ISSN:2220-9964
2220-9964
DOI:10.3390/ijgi7090347