Simulation of land use land cover change in Melbourne metropolitan area from 2014 to 2030: using multilayer perceptron neural networks and Markov chain model

References

• Aburas, Maher M, Sabrina HO Abdullah, Mohammad F Ramli, and Zulfa H Asha’ari. 2017. “Land Suitability Analysis of Urban Growth in Seremban Malaysia, Using GIS Based Analytical Hierarchy Process.” Procedia Engineering 198: 1128–1136. doi:10.1016/j.proeng.2017.07.155.
   Google Scholar
• Aithal, Bharath H., Vinay Shivamurthy, and T. V. Ramachandra. 2017. “Characterization and Visualization of Spatial Patterns of Urbanisation and Sprawl Through Metrics and Modeling.” Cities and the Environment (CATE) 10 (1): 7–14. http://digitalcommons.lmu.edu/cate/vol10/iss1/5.
   Google Scholar
• Angel, Shlomo, Jason Parent, Daniel L. Civco, Alexander Blei, and David Potere. 2011. “The Dimensions of Global Urban Expansion: Estimates and Projections for all Countries, 2000–2050.” Progress in Planning 75: 53–107. doi:10.1016/j.progress.2011.04.001.
   Web of Science ®Google Scholar
• Australian Government. 2020. Planning for Australia’s Future Population. Canberra: Australian Government.
   Google Scholar
• The Australian Governmwent. 2020. Population Statement. Canberra: The Australian Governmwent: Centre for Population analysis. Retrieved 2 14, 2021, from https://population.gov.au/docs/population_statement_2020_overview.pdf.
   Google Scholar
• Bhagawat, R., S. Sloan, and H. Keshtk. 2020. “Patterns of Historical and Future Urban Expansion.” Remote Sensing 12 (628): 1–22.
   Google Scholar
• Borana, S. L., and S. K. Yadav. 2017. “Modelling and Prediction of Land Use Changes in Jodhpur City Using Multi Layer Perceptron Markov Techniques.” International Journal of Research in Engineering, IT and Social Sciences 07 (11): 14–21.
   Google Scholar
• Buxton, M., R. Haynes, D. Mercer, and A. Butt. 2010. “Vulnerability to Bushfire Risk at Melbourne’s Urban Fringe: The Failure of Regulatory Land Use Planning.” Geographical Research 49 (1): 1–12. doi:10.1111/j.1745-5871.2010.00670.x.
   Web of Science ®Google Scholar
• Dereli, Mehmet Ali. 2018. “Monitoring and Prediction of Urban Expansion Using Multilayer Perceptron Neural Network by Remote Sensing and GIS Technologies: A Case Study from Istanbul Metroplitan City.” Fresenius Environmental Bulletin 27 (12A): 9336–9344.
   Web of Science ®Google Scholar
• Ewing, Reid, Rolf Pendall, and Don Chen. 2019. Measuring Sprawl and Its Impact. Smart Growth America. https://smartgrowthamerica.org/app/legacy/documents/MeasuringSprawlTechnical.pdf.
   Google Scholar
• Explorer, E. 2020, 7 13. EarthExplorer - Home. Retrieved from USGS.gov: https://earthexplorer.usgs.gov/.
   Google Scholar
• Extract.bbbike. 2020, 3 12. Retrieved from bbbike.org/: https://extract.bbbike.org/.
   Google Scholar
• Fody, Giles M. 2001. “Thematic Mapping from Remotely Sensed Data with Neural Network; MLP,RBF and pnn Based Approaches.” Journal of Geographical Systems 3: 2017–2232.
   Google Scholar
• Guneralp, B, and K C Seto. 2013. “Futures of Global Urban Expansion: Uncertainties and Implications for Biodiversity Conservation.” Environmental Research Letters 8: 1–11.
   Web of Science ®Google Scholar
• Hakim, A. M. Y., S. Baja, D. A. Rampisela, and S. Arif. 2019. Spatial dynamic prediction of landuse / landcover change (case study:tamalanrea sub-district, makassar city.” The 4th International Conference of Indonesian Society for Remote Sensing. Makassar: Hasanuddin University. 1-10. doi:doi:10.1088/1755-1315/280/1/012023.
   Google Scholar
• Hamdy, Omar, Shichen Zhao, and Mohamed A Salheen. 2017. “Analyses the Driving Forces for Urban Growth by Using IDRISI®Selva Models Abouelreesh Aswan as a Case Study.” International Journal of Engineering and Technology 9 (3): 226–232. doi:10.7763/IJET.2017.V9.975.
   Google Scholar
• Hyandye, Canute, and Lawrence W Martz. 2017. “A Markovian and Cellular Automata Land-use Change Predictive Model of the Usangu Catchment.” International Journal of Remote Sensing 38 (1): 64–81. doi:10.1080/01431161.2016.1259675.
   Web of Science ®Google Scholar
• Imran, H., J. Kala, A. Ng, and D. Muthukumaran. 2019. “Impacts of Future Urban Expansion on Urban Heat Island Effects During Heatwave Events in the City of Melbourne in Southeast Australia.” Quarterly Journal of the Royal Meteorological Society 145 (723): 2586–2602. doi:10.1002/qj.3580.
   Web of Science ®Google Scholar
• Kantakumar, Lakshmi N, Shamita Kumar, and Karl Schneider. 2019. “SUSM: a Scenario-Based Urban Growth Simulation Model Using Remote Sensing Data.” European Journal of Remote Sensing 52: 1–17.
   Web of Science ®Google Scholar
• Kumar, Sathees, Nisha Radhakrishnan, and Samson Mathew. 2014. “Land use Change Modelling Using a Markov Model and Remote Sensing.” Geomatics, Natural Hazards and Risk 5 (2): 145–156. doi:0.1080/19475705.2013.795502.
   Web of Science ®Google Scholar
• Kumar Singh, Sudhir, Sk Mustak, Prashant K Srivastava, Szilárd Szabó, and Tanvir Islam. 2015. “Predicting Spatial and Decadal LULC Changes Through Cellular Automata Markov Chain Models Using Earth Observation Datasets and Geo-Information.” Environ. Process 2, 61–78. doi:10.1007/s40710-015-0062-x.
   Google Scholar
• Lantman, Jonas van Schrojenstein, Peter H. Verburg, Arnold Bregt, and Stan Geertman. 2011. “Core Principles and Concepts in Land-Use Modelling: A Literature Review.” In Land-Use Modelling in Planning Practice, edited by Eric Koomen, and Judith Borsboom-van Beurden, 35–54. Springer Dordrecht Heidelberg: Springer Science + Business Media. www.springer.com.
   Google Scholar
• Mcguirk, P., and N. Argent. 2011. “Population Growth and Change: Implications for Australia's Cities and Regions.” Geographical Research 49 (3): 317–335. doi:10.1111/j.1745-5871.2011.00695.x.
   Web of Science ®Google Scholar
• Mendiratta, Priya, and Shirish Gedam. 2018. “Assessment of Urban Growth Dynamics in Mumbai Metropolitan Region, India Using Object-Based Image Analysis for Medium-Resolution Data.” Applied Geography 98: 110–120. doi:10.1016/j.apgeog.2018.05.017.
   Web of Science ®Google Scholar
• Mishra, V. N., and P. K. Rai. 2016. “A Remote Sensing Aided Multi-Layer Perceptron-Markov Chain Analysis for Land use and Land Cover Change Prediction in Patna District (Bihar), India.” Arab J Geosci 9 (249): 1–18. doi:10.1007/s12517-015-2138-3.
   Google Scholar
• Noszczyk, Tomasz. 2019. “A Review of Approaches to Land use Changes Modeling.” Human and Ecological Risk Assessment An International Journal 25 (6): 1377–1405.
   Web of Science ®Google Scholar
• Olga, LuciaPuertas, Henríquez Cristian, and Javier Meza Francisco. 2014. “Assessing Spatial Dynamics of Urban Growth Using an Integrated Land use Model. Application in Santiago Metropolitan Area, 2010–2045.” Land Use Policy 38: 415–425. doi:10.1016/j.landusepol.2013.11.024.
   Web of Science ®Google Scholar
• Osman, Taher, David Shawb, and Emad Kenawy. 2018. “An Integrated Land use Change Model to Simulate and Predict the Future of Greater Cairo Metropolitan Region.” Journal of Land Use Science 13 (6): 565–584.
   Web of Science ®Google Scholar
• Pahlavani, Parham, Hosein Askarian Omran, and Behnaz Bigdeli. 2017. “A Multiple Land use Change Model Based on Artificial Neural Network,Markov Chain, and Multi Objective Land Allocation.” Earth Observation and Geomatics Engineering 1 (2): 82–99.
   Google Scholar
• Plan Melbourne 2017 - 2050. 2020. https://www.planning.vic.gov.au/policy-and-strategy/planning-for-melbourne/plan-melbourne. 7 23. Accessed 7 23, 2020. https://www.planning.vic.gov.au/policy-and-strategy/planning-for-melbourne/plan-melbourne.
   Google Scholar
• Rahnama, Mohammad Rahim, Ray Wyatt, and Akbar Heydari. 2015. “What Happened from 2001 to 2011 in Melbourne? Compactness Versus Sprawl.” Sustainable Cities and Society 19: 09–120.
   Web of Science ®Google Scholar
• Rahnama, Mohammad Rahim, Ray Wyatt, and Lia Shaddel. 2020. “A Spatial-Temporal Analysis of Urban Growth in Melbourne; Were Localgovernment Areas Moving Toward Compact or Sprawl from 2001–2016?” Applied Geography 124: 1–9.
   Web of Science ®Google Scholar
• Ranagalage, Manjula, Ruci Wang, M. H. J. P. Gunarathna, D. M. S. L. B. Dissanayake, Yuji Murayama, and Matamyo Simwanda. 2019. “Spatial Forecasting of the Landscape in Rapidly Urbanizing Hill Stations of South Asia: A Case Study.” Remote Sensing 11: 2–31.
   Web of Science ®Google Scholar
• Safari, S., and A. Baratloo. 2015. “Evidence Based Medicine; Positive and Negative Predictive Values of Diagnostic Tests.” Iranian Journal of Emergency Medicine 2 (3): 147–149.
   Google Scholar
• Shafizadeh-Moghadam, Hossein, Amin Tayyeb, and Marco Helbich. 2017. “Transition Index Maps for Urban Growth Simulation: Application of Artificial Neural Networks, Weight of Evidence and Fuzzy Multi-Criteria Evaluation.” Environmental Monitoring and Assessment 300: 1–14.
   Google Scholar
• Simwanda, Matamyo, and Yuji Murayama. 2018. “Spatiotemporal Patterns of Urban Land use Change in the Rapidly Growing City of Lusaka, Zambia: Implications for Sustainable Urban Development.” Sustainable Cities and Society 39: 262–274. doi:10.1016/j.scs.2018.01.039.
   Web of Science ®Google Scholar
• Statista. 2021, 2 14. Statista. Retrieved from tatista.com: https://www.statista.com/statistics/263740/total-population-of-australia/.
   Google Scholar
• Varun, N., and K. Praveen. 2016. “A Remote Sensing Aided Multi-Layer Perceptron-Markov Chain Analysis for Land use and Land Cover Change Prediction in Patna District (Bihar).” Arabian Journal of Geosciences 9 (249): 1–18.
   Google Scholar
• Wang, Jianzhu, and Ikechukwu Nnamdi Maduako. 2018. “Spatio-temporal Urban Growth Dynamics of Lagos Metropolitan Region of Nigeria Based on Hybrid Methods for LULC Modeling and Prediction.” European Journal of Remote Sensing 51 (1): 251–265. doi:10.1080/22797254.2017.1419831.
   Web of Science ®Google Scholar
• Xu, Chi, Maosong Liu, Chao Hong, and Ting Chi. 2012. “Temporal Variation of Characteristic Scales in Urban Landscapes: an Insight Into the Evolving Internal Structures of China’s two Largest Cities.” Landscape Ecology 27: 1063–1074. doi:10.1007/s10980-012-9764-x.
   Web of Science ®Google Scholar
• Zhu, Xinhua, Peifeng Zhang, Yigang Wei, Yan Li, and Hongrui Zhao. 2019. “Measuring the Efficiency and Driving Factors of Urban Land use Based on the DEA Method and the PLS-SEM Model—A Case Study of 35 Large And medium-Sized Cities in China.” Sustainable Cities and Society 50.
   Web of Science ®Google Scholar