The explosive growth of the Melbourne metropolitan area (MMA) population in the last two decades and the physical expansion of the city to the periphery have necessitated the analysis of land use land cover changes (LULCc). To this end, Landsat-8 OLI and Multi layer Perceptron (MLP) neural networks, Markov chain model, GIS, and TerrSet software package were used. Initially, nine driving variables affecting the future development of the city were identified. Then, using the maximum probability estimation model, land uses were classified into six categories in the MMA with an area of 8,819.6 km2 for 2014, 2017 and 2020. During this period, two land uses have experienced positive changes (residential and cultivated land uses with 16.48% and 11.56% respectively) and four of them (forest cover -5.74%, grass and green space -9.72%, barren lands -11.14%, and water body -1.45%) have experienced a decrease in area. To validate the prediction of land use changes until 2030, the kappa coefficient (0.6169) and the area under the curve (AUC = 0.66) in the ROC diagram were used. The result of using Cramer\\\'s V statistics (above 0.15) to measure the effect of driving variables on land use forecast was acceptable. LULCc was predicted by MLP of neural network and the Markov chain model for 2030. The results showed that two land uses faced with positive change (residential and industrial land use 26.3% and cultivated land 6.99%) and four of them have negative growth (forest coverage -17.11%, barren land -9 29%, grass and green space -5.33%, and water bodies -1.38%). Spatial changes of land use will occur mostly in northern and north-western regions on barren land and on the east and southeast on forest and green spaces. Findings are useful for planning authorities for MMA which is highly affected by population growth and LULCc.

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