Modelling land use/land cover changes prediction using multi-layer perceptron neural network (MLPNN): a case study in Makassar City, Indonesia

Abstract

This study used Remote Sensing and Geographic Information System (GIS) tools to produce a predictive model of land use/land cover changes in Makassar City by 2031. The model was based on the classification of SPOT satellite images from 2006, 2011, and 2016 with an assessment of 10 driving factors. The Multi-Layer Perceptron Neural Network (MLPNN) method was run with the Markov Chain model using the Business as Usual scenario. The results predict that built-up areas will cover about 80% of the total area of Makassar City by 2031. This research can inform stakeholder decision-making in Makassar City.

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