Projecting Land use change with neural network and GIS in northern Melbourne for 2014–2050

Rahnama, Mohammad Rahim; Wyatt, Ray

doi:10.1080/00049182.2021.1920088

Cited by 9 publications

(4 citation statements)

References 32 publications

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“…The mixed use of multiple methods and comparative studies are emphasized, and there is an increasing number of research papers applying complex theories and multi-agent techniques. Rahnama [43] predicted the land use change of Melbourne from 2014 to 2050 based on the combination of Multilayer Perceptron neural networks and Markov chain model in ArcGIS and TerrSet software. Kumar [44] analyzed the long-term spatio-temporal evolution characteristics of land use in the United States from 1850 to 2000 by Nonlinear Bianalytical mode.…”

Section: Literature Review 21 Research Methods and Modelmentioning

confidence: 99%

A Dynamic Performance and Differentiation Management Policy for Urban Construction Land Use Change in Gansu, China

Zhang

Zhao

et al. 2022

Land

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Making efforts to promote rationalized urban construction land change, distribution, allocation, and its performance is the core task of territory spatial planning and a complex issue that the government must face and solve. Based on the Boston Consulting Group matrix, a decoupling model, and a GIS tool, this paper constructs a new tool that integrates “dynamic analysis + performance evaluation + policy design” for urban construction land. We reached the following findings from an empirical study of Gansu, China: (1) Urban construction land shows diversified changes, where expansion is dominant and shrink cannot be ignored. (2) Most cities are in the non-ideal state of LH (Low-High) and LL (Low-Low), with a small number in the state of HH (High-High) and HL (High-Low). (3) Urban construction land change and population growth, economic development, and income increase are in a discordant relationship, mostly in strong negative decoupling and expansive negative decoupling. (4) The spatial heterogeneity of urban construction land change and its performance are at a high level, and they show a slow upward trend. Additionally, the cold and the hot spots show obvious spatial clustering characteristics, and the spatial pattern of different indexes is different to some extent. (5) It is suggested that in territory spatial planning Gansu should divide the space into four policy areas—incremental, inventory, a reduction development policy area, and a transformation leading policy area—to implement differentiated management policies and to form a new spatial governance system of “control by zoning and management by class.” The change of urban construction land, characterized by dynamics and complexity, is a direct mapping of the urban growth process. The new tools constructed in this paper will help to reveal the laws of urban development and to improve the accuracy of territory spatial planning in the new era. They are of great theoretical significance and practical value for promoting high-quality and sustainable urban development.

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Section: Literature Review 21 Research Methods and Modelmentioning

confidence: 99%

A Dynamic Performance and Differentiation Management Policy for Urban Construction Land Use Change in Gansu, China

Zhang

Zhao

et al. 2022

Land

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“…They used Cellular automata (CA) to improve the model's capacity to accurately anticipate future land use patterns. Morshed et al (2022) and Rahnama and Wyatt's (2021) studies, which used ANNs, also revealed the importance of a strategic land use plan for monitoring and controlling plant encroachment, as well as scientific mitigation approaches to maintain ecological sustainability. ANNs have been widely used by LCLUC modellers over the last two decades , Pijanowski et al 2020.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the effect of ongoing conservation policies and forest cover changes in Iranian Zagros forests based on a Land Transformation Model: transition to forest or deforestation?

Heidarlou,

Banj Shafiei,

Tayyebi

et al. 2023

AFR

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In recent decades, Zagros forests from western Iran have experienced dramatic changes in cover and structure. Conservation policies, on the other hand, have existed or are being implemented in these forests since 2002 to prevent deforestation. There is, however, the question on how effective were the conservation policies in preventing forest loss. The goal of this study was to analyze the effect of conservation policies in preventing forest loss, as well as to forecast their future effectiveness. Since the spatio-temporal changes in forest cover, land-use and its patterns occur in a non-linear way, this study was based on the use of Land Transformation Model (LTM). Using geographic information systems (GIS) and artificial neural networks (ANNs), this model forecasts future forest changes for the next 30 years. Three scenarios were used for this purpose, in which the input patterns included the years 2002-2012, 2002-2022, and 2012-2022. Based on these, deforestation was predicted for the next three decades using 14 variables. Assuming no changes in the implementation of conservation policies in the Zagros forests, the model was characterized by a consistent accuracy and indicated a projected pattern of increased deforestation over the next years in the region. In other words, by the ongoing conservation policies, the net deforestation overtakes net reforestation. It appears that to stop further forest degradation, Iran's Forestry Service decision-makers must implement improved forest conservation policies.

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“…Further, the MLP model explores the effects of variables through a backward stepwise constant forcing equation [56]. The crucial concept is learning and producing knowledge from the neurons [57].…”

Section: Introductionmentioning

confidence: 99%

“…The crucial concept is learning and producing knowledge from the neurons [57]. Consequently, it is a model approach applied in recent research [56][57][58][59][60][61][62][63][64], determining the influence of variables on LULC change and urban expansion. In research by Losiri et al [63], the authors modeled urban expansion in the Bangkok metropolitan area for 2035 employing the MLP model and using proximity factors of LULC and socioeconomic variables.…”

Section: Introductionmentioning

confidence: 99%

Prediction Power of Logistic Regression (LR) and Multi-Layer Perceptron (MLP) Models in Exploring Driving Forces of Urban Expansion to Be Sustainable in Estonia

Pour

Oja

2021

Sustainability

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Estonia mainly experienced urban expansion after regaining independence in 1991. Employing the CORINE Land Cover dataset to analyze the dynamic changes in land use/land cover (LULC) in Estonia over 28 years revealed that urban land increased by 33.96% in Harju County and by 19.50% in Tartu County. Therefore, after three decades of LULC changes, the large number of shifts from agricultural and forest land to urban ones in an unplanned manner have become of great concern. To this end, understanding how LULC change contributes to urban expansion will provide helpful information for policy-making in LULC and help make better decisions for future transitions in urban expansion orientation and plan for more sustainable cities. Many different factors govern urban expansion; however, physical and proximity factors play a significant role in explaining the spatial complexity of this phenomenon in Estonia. In this research, it was claimed that urban expansion was affected by the 12 proximity driving forces. In this regard, we applied LR and MLP neural network models to investigate the prediction power of these models and find the influential factors driving urban expansion in two Estonian counties. Using LR determined that the independent variables “distance from main roads (X7)”, “distance from the core of main cities of Tallinn and Tartu land (X2)”, and “distance from water land (X11)” had a higher negative correlation with urban expansion in both counties. Indeed, this investigation requires thinking towards constructing a balance between urban expansion and its driving forces in the long term in the way of sustainability. Using the MLP model determined that the “distance from existing residential areas (X10)” in Harju County and the “distance from the core of Tartu (X2)” in Tartu County were the most influential driving forces. The LR model showed the prediction power of these variables to be 37% for Harju County and 45% for Tartu County. In comparison, the MLP model predicted nearly 80% of variability by independent variables for Harju County and approximately 50% for Tartu County, expressing the greater power of independent variables. Therefore, applying these two models helped us better understand the causative nature of urban expansion in Harju County and Tartu County in Estonia, which requires more spatial planning regulation to ensure sustainability.

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Projecting Land use change with neural network and GIS in northern Melbourne for 2014–2050

Cited by 9 publications

References 32 publications

A Dynamic Performance and Differentiation Management Policy for Urban Construction Land Use Change in Gansu, China

A Dynamic Performance and Differentiation Management Policy for Urban Construction Land Use Change in Gansu, China

Evaluating the effect of ongoing conservation policies and forest cover changes in Iranian Zagros forests based on a Land Transformation Model: transition to forest or deforestation?

Prediction Power of Logistic Regression (LR) and Multi-Layer Perceptron (MLP) Models in Exploring Driving Forces of Urban Expansion to Be Sustainable in Estonia

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