Urban Growth Modelling of Malang City using Artificial Neural Network Based on Multi-temporal Remote Sensing

Agung Bayu Nugroho, Abdul Wahid Hasyim, Fadly Usman


In this study, the prediction of urban growth was simulated by Artificial Neural Network (ANN) model using MOLUSCE, plugin of QGIS. Objectives of this study is to illustrate the urban growth in Malang City over time span of 24 years and also to predict the future of urban growth using ANN model for the year 2027. Land cover maps were extracted for 2003, 2009 and 2015 via remote sensing images from Landsat ETM+ and OLI, respectively. The overall classification accuracy and kappa coefficient for all classified maps were over 85% and 0.76, respectively. According to the simulation result, 1049.58 ha of vegetation and 241.29 ha of bare land in 2015 would experience a transition to built-up areas in 2027. Then, the built-up areas would experience an increase by 11.79% from 2015 to 2027. In 2027, the built up areas would covered the city by 73.21% of the city area. There was a trend in increasing of built-up areas during the period 2003 to 2027. Overall, the result shows that urban growth models by using ANN model can be a considerable option for future changes according to past and current factors.


urban growth; artificial neural network; land cover; built-up areas; MOLUSCE

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DOI: https://doi.org/10.21776/ub.civense.2018.00102.2


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