Coupling machine learning, tree-based and statistical models with cellular automata to simulate urban growth

Shafizadeh‐Moghadam, Hossein; Asghari, Ali; Tayyebi, Amin; Taleai, Mohammad

doi:10.1016/j.compenvurbsys.2017.04.002

Cited by 123 publications

(53 citation statements)

References 54 publications

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“…Land-use mapping and modeling of environmental data using machine learning (ML) (Witten et al 2016) have gained increasing interest within the geospatial community (Lary et al 2016). It has become a vital methodology to monitor (Rogan et al 2008, Qian et al 2014, Heung et al 2016, Omrani et al 2019) and forecast land-use change (Samardžić-Petrović et al 2016, Shafizadeh-Moghadam et al 2017, Du et al 2018, Hagenauer and Helbich 2018. ML comprises a set of inductive models that recognize patterns and/or minimize the prediction error of complex regression functions, by means of a repeated learning strategy from training data, linking an output such as land-use change to several underlying drivers.…”

Section: Introductionmentioning

confidence: 99%

Assessing the performance of 38 machine learning models: the case of land consumption rates in Bavaria, Germany

Hagenauer¹,

Omrani

Helbich

2019

International Journal of Geographical Information Science

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Machine learning (ML) is at the forefront of land-use change modeling. Due to numerous available ML approaches, the model choice is complex and usually based on ad hoc decisions, though informed through a few comparative studies that considered a limited number of models. This study contributes a comprehensive comparison of 38 ML models to examine land consumption rates (LCR) (i.e. the transition of landscapes to built-up areas). We modeled LCR for 2009-2015 in Bavaria, Germany, and predicted rates for 2015-2021 at a municipality level. To assess the performance of each approach, we measured the mean absolute error (MAE), the root-mean-square error (RMSE), and the coefficient of determination (R 2 ) using crossvalidation. All algorithms consistently predicted that the land consumption rate for Bavaria will increase. eXtreme gradient boosting decision trees performed best with respect to the RMSE (0.500) and R 2 (0.183), while the support vector machine with polynomial kernel has the lowest MAE (0.288). The generalized additive model and the random forest models also performed well. We recommend these ML approaches for future land consumption and land-use change studies. A poor performance was found for recursive partitioning by decision trees, self-organizing maps, and partitioning using deletion, substitution, and addition moves. ARTICLE HISTORY

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Section: Introductionmentioning

confidence: 99%

Assessing the performance of 38 machine learning models: the case of land consumption rates in Bavaria, Germany

Hagenauer¹,

Omrani

Helbich

2019

International Journal of Geographical Information Science

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“…Due to the intrinsic complex character of urban systems, many researchers combine different modelling approaches for simulation or prediction of urban growth of cities. Often CA are combined with logistic regression (Liu & Feng, 2012;Shafizadeh-Moghadam, Asghari, Tayyebi, & Taleai, 2017), artificial neural networks (Shafizadeh- Moghadam et al, 2017), system dynamics (Han, Hayashi, Cao, & Imura, 2009), potential models (He, Okada, Zhang, Shi, & Li, 2008;Kong, Yin, Nakagoshi, & James, 2012), Markov chains (Arsanjani, Helbich, Kainz, & Darvishi Boloorani, 2013;Moghadam & Helbich, 2013), etc.…”

Section: Introductionmentioning

confidence: 99%

SUSM: a scenario-based urban growth simulation model using remote sensing data

Kantakumar

Kumar

Schneider

2019

European Journal of Remote Sensing

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The introduction of the Foreign Direct Investment (FDI) policy in 1991 made India one of the fastest growing economies in the world. This has had a profound effect on India's urbanization. The rapid urbanization of Indian cities poses a threat to natural and social environments, as expansion of the cities often outpaces the urban planning process. Thus, smart and strategic planning processes that use current and easily available datasets in combination with future urbanization scenarios are needed. To this end, we developed the scenario-based urban growth simulation model (SUSM), which can be used for impact analysis of different planning measures in both spatial and temporal contexts. SUSM uses remote sensing derived inputs, such as land use maps, slope, roads and centres of urban areas along with urban development scenarios. It uses logistic regression for calibration and a constrained stochastic cellular automaton for simulation of urban growth.SUSM is tested in one of the fastest growing urban agglomerations of India: The Pune metropolis, which covers an area of 1642 km 2 . SUSM is calibrated using urban growth maps derived from LANDSAT satellite images from 1992 to 2001. Subsequently, SUSM was used to simulate urban growth of Pune for 2013. A comparison of the SUSM simulation result with the actually measured urban growth derived from a LANDSAT 8 scene from 2013 is used to validate SUSM and to assess the effect of urban plans upon the growth of Pune. Our results show that: (i) SUSM is capable of predicting the location of future urbanization with an accuracy of 79% and a fuzzy kappa index of agreement 0.81; (ii) inclusion of official urban development plans as input for SUSM did not provide a better agreement with the observed growth; (iii) SUSM, parameterized with remote sensing data, can be used effectively to understand urban growth and assess the effects of alternative urban development plans in terms of the spatial expansion of cities. ARTICLE HISTORY

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“…LTM uses ANN, which is a machine learning technique, for modelling land cover change [37,[39][40][41]. The multilayer perceptron is one of the well-known ANN forms that is most commonly employed in land cover change science [1].…”

Section: Land Transformation Modelmentioning

confidence: 99%

“…The LTM has been used to forecast land cover change in a variety of areas across the world, such as the United States [1,29], Europe [30] eastern Africa [23] and Asia [31]. Primarily, the LTM has been used to (1) determine the uncertainty levels of land change model outputs at a variety of spatial-temporal scales and land change contexts [32]; (2) couple other process-based models to understand how land cover alters climate [33,34] and water [26,35] and ecosystem dynamics; and (3) generate baseline data layers for online decision making tools [36,37]. Here, we applied LTM to produce urbanization maps for the Lower Peninsula of Michigan in United States from 2010 to 2050 with five-year intervals.…”

Section: Introductionmentioning

confidence: 99%

Long-Term Land Cover Data for the Lower Peninsula of Michigan, 2010–2050

Tayyebi

Smidt

Pijanowski

2017

Data

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Land cover data are often used to examine the impacts of landscape alterations on the environment from the local to global scale. Although various agencies produce land cover data at various spatial scales, data are still limited at the regional scale over extended timescales. This is a critical data gap since decision-makers often use future and long-term land cover maps to develop effective policies for sustainable environmental systems. As a result, land change science incorporates common data mining tools to create future land cover maps that extend over long timescales. This study applied one of the well-known land cover change models, called Land Transformation Model (LTM), to produce urbanization maps for the Lower Peninsula of Michigan in United States from 2010 to 2050 with five year intervals. Long-term urbanization data in the Lower Peninsula of Michigan can be used in various environmental studies such as assessing the impact of future urbanization on climate change, water quality, food security and biodiversity.Data Set: available as a supplementary file. Data Set License: CCBY

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Coupling machine learning, tree-based and statistical models with cellular automata to simulate urban growth

Cited by 123 publications

References 54 publications

Assessing the performance of 38 machine learning models: the case of land consumption rates in Bavaria, Germany

Assessing the performance of 38 machine learning models: the case of land consumption rates in Bavaria, Germany

SUSM: a scenario-based urban growth simulation model using remote sensing data

Long-Term Land Cover Data for the Lower Peninsula of Michigan, 2010–2050

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