Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: A case study.

H Shafizadeh Moghadam, J Hagenauer, M Farajzadeh, M Helbich

    Research output: Contribution to journalArticleAcademicpeer-review

    Abstract

    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.
    Original languageEnglish
    Pages (from-to)606-623
    JournalInternational Journal of Geographical Information Science
    Volume29
    Issue number4
    DOIs
    Publication statusPublished - 11 Mar 2015

    Keywords

    • radial basis function network
    • multi-layer perceptron network
    • urban change
    • spatial accuracy assesment
    • GIS

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