Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W5, 31-36, 2015
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-1-W5/31/2015/
doi:10.5194/isprsarchives-XL-1-W5-31-2015
© Author(s) 2015. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
10 Dec 2015
USING MULTIVARIATE ADAPTIVE REGRESSION SPLINE AND ARTIFICIAL NEURAL NETWORK TO SIMULATE URBANIZATION IN MUMBAI, INDIA
M. Ahmadlou1, M. R. Delavar2, A. Tayyebi3, and H. Shafizadeh-Moghadam4 1GIS Dept., School of Surveying and Geospatial Eng., College of Eng., University of Tehran, Tehran, Iran
2Center of Excellence in Geomatic Eng. in Disaster Management, School of Surveying and Geospatial Eng., College of Engineering, University of Tehran, Tehran, Iran
3University of California-Riverside, Center for Conservation Biology, Riverside, CA, USA
4Tarbiat Modares University, Department of GIS & RS, Tehran, Iran
Keywords: Land Use Change, Data Mining, Multivariate Adaptive Regression Spline, Artificial Neural Network, Receiver Operating Characteristic Abstract. Land use change (LUC) models used for modelling urban growth are different in structure and performance. Local models divide the data into separate subsets and fit distinct models on each of the subsets. Non-parametric models are data driven and usually do not have a fixed model structure or model structure is unknown before the modelling process. On the other hand, global models perform modelling using all the available data. In addition, parametric models have a fixed structure before the modelling process and they are model driven. Since few studies have compared local non-parametric models with global parametric models, this study compares a local non-parametric model called multivariate adaptive regression spline (MARS), and a global parametric model called artificial neural network (ANN) to simulate urbanization in Mumbai, India. Both models determine the relationship between a dependent variable and multiple independent variables. We used receiver operating characteristic (ROC) to compare the power of the both models for simulating urbanization. Landsat images of 1991 (TM) and 2010 (ETM+) were used for modelling the urbanization process. The drivers considered for urbanization in this area were distance to urban areas, urban density, distance to roads, distance to water, distance to forest, distance to railway, distance to central business district, number of agricultural cells in a 7 by 7 neighbourhoods, and slope in 1991. The results showed that the area under the ROC curve for MARS and ANN was 94.77% and 95.36%, respectively. Thus, ANN performed slightly better than MARS to simulate urban areas in Mumbai, India.
Conference paper (PDF, 1113 KB)


Citation: Ahmadlou, M., Delavar, M. R., Tayyebi, A., and Shafizadeh-Moghadam, H.: USING MULTIVARIATE ADAPTIVE REGRESSION SPLINE AND ARTIFICIAL NEURAL NETWORK TO SIMULATE URBANIZATION IN MUMBAI, INDIA, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W5, 31-36, doi:10.5194/isprsarchives-XL-1-W5-31-2015, 2015.

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