Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: a case study
The majority of cities are rapidly growing. This makes the monitoring and modeling of urban change’s spatial patterns critical to urban planners, decision makers, and environment protection activists. Although a wide range of methods exists for modeling and simulating urban growth, machine learning...
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| Main Authors: | , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
11 March 2015
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| In: |
International journal of geographical information science
Year: 2015, Volume: 29, Issue: 4, Pages: 606-623 |
| ISSN: | 1365-8824 |
| DOI: | 10.1080/13658816.2014.993989 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1080/13658816.2014.993989
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| Author Notes: | Hossein Shafizadeh-Moghadam, Julian Hagenauer, Manuchehr Farajzadeh and Marco Helbich |
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| 245 | 1 | 0 | |a Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change |b a case study |c Hossein Shafizadeh-Moghadam, Julian Hagenauer, Manuchehr Farajzadeh and Marco Helbich |
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| 520 | |a The majority of cities are rapidly growing. This makes the monitoring and modeling of urban change’s spatial patterns critical to urban planners, decision makers, and environment protection activists. Although a wide range of methods exists for modeling and simulating urban growth, machine learning (ML) techniques have received less attention despite their potential for producing highly accurate predictions of future urban extents. The aim of this study is to investigate two ML techniques, namely radial basis function network (RBFN) and multi-layer perceptron (MLP) networks, for modeling urban change. By predicting urban change for 2010, the models’ performance is evaluated by comparing results with a reference map and by using a set of pertinent statistical measures, such as average spatial distance deviation and figure of merit. The application of these techniques employs the case study area of Mumbai, India. The results show that both models, which were tested using the same explanatory variables, produced promising results in terms of predicting the size and extent of future urban areas. Although a close match between RBFN and MLP is observed, RBFN demonstrates higher spatial accuracy of prediction. Accordingly, RBFN was utilized to simulate urban change for 2020 and 2030. Overall, the study provides evidence that RBFN is a robust and efficient ML technique and can therefore be recommended for land use change modeling. | ||
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