Assessment and Prediction of Urban Expansion Using CA-Based SLEUTH Urban Growth Model: A Case Study of Kolkata Metropolitan Area (KMA), West Bengal, India

Das, Sandipta; Jain, Gaurav

doi:10.1007/s12524-022-01602-y

Cited by 8 publications

(4 citation statements)

References 76 publications

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“…Therefore, although the CA-Markov method is more effective in predicting LULCs in extensive areas and the ANN method is more effective for smaller areas, we suggest that other researchers evaluate other algorithms in other regions to better understand these prediction models. We also recommend that researchers compare the results from other methods, such as the Genetic Algorithm Optimized Neural Network Model [112], CA-Based SLEUTH [113], and CycleGANs-based CNN [114], using several evaluation indices.…”

Section: Discussionmentioning

confidence: 99%

Urban Sprawl Simulation Mapping of Urmia (Iran) by Comparison of Cellular Automata–Markov Chain and Artificial Neural Network (ANN) Modeling Approach

Asadi¹,

Oshnooei-Nooshabadi

Saleh

et al. 2022

Sustainability

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Considering urbanization can lead to irreversible land transformations, it is crucial to provide city managers, environmental resources managers, and even people with accurate predicting land use/land cover (LULC) to accomplish sustainable development goals. Although many methods have been used to predict land use/land cover (LULC), few studies have compared them. Therefore, by analyzing the results of various prediction models and, consequently, recognizing the most accurate and reliable ones, we can assist city managers, environmental resources managers, and researchers.. In this regard, this research compares Cellular Automata–Markov Chain and Artificial Neural Network (ANN) as frequently used models to overcome this gap and help those concerned about sustainable development to predict urban sprawl with the most reliable accuracy. In the first step, Landsat satellite images acquired in 2000, 2010, and 2020 were classified with Maximum Likelihood Classification (MLC), and LULC maps were prepared for each year. In the second step, to investigate the LULC prediction, validation of the CA–Markov and ANN methods was performed. In this way, the LULC simulation map of 2020 was prepared based on the LULC map of 2000 and 2010; next, the predicted LULC map of 2020 and the actual LULC map for 2020 were compared using correctness, completeness, and quality indices. Finally, the LULC map for 2030 was generated using both algorithms, and the corresponding change map was extracted, showing a reduction in soil and vegetation areas (respectively, 39% and 12%) and an expansion (58%) in built-up regions. Moreover, the validation test of the methods showed that the two algorithms were closer to each other; however, ANN had the highest completeness (96.21%) and quality (93.8%), while CA–Markov had the most correctness (96.47%). This study showed that the CA–Markov algorithm is more accurate in predicting the future of larger areas with higher allocations (urban and vegetation cover) while the ANN algorithm is more accurate in predicting the future of small areas with fewer allocations (soil and rock).

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Section: Discussionmentioning

confidence: 99%

Urban Sprawl Simulation Mapping of Urmia (Iran) by Comparison of Cellular Automata–Markov Chain and Artificial Neural Network (ANN) Modeling Approach

Asadi¹,

Oshnooei-Nooshabadi

Saleh

et al. 2022

Sustainability

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“…Within the framework of the Kamrup Metropolitan study, the acknowledgment pertaining to the neighboring nation of Bhutan, which shares physiographic similarities, yielded valuable insights [20]. The study by [21], which covers the spatiotemporal urban dynamics of the Kolkata Metropolitan Area (KMA), utilizes the SLEUTH model to predict urban growth and its environmental impacts, revealing organic growth patterns and peripheral expansion. This study underscores the importance of modeling and planning for sustainable urban development.…”

Section: Introductionmentioning

confidence: 99%

Projecting Urban Expansion by Analyzing Growth Patterns and Sustainable Planning Strategies—A Case Study of Kamrup Metropolitan, Assam, North-East India

Choudhury,

Kanga,

Singh

et al. 2024

Earth

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This research focuses on the urban expansion occurring in the Kamrup Metropolitan District—an area experiencing significant urbanization—with the aim of understanding its patterns and projecting future growth. The research covers the period from 2000 to 2022 and projects growth up to 2052, providing insights for sustainable urban planning. The study utilizes the maximum likelihood method for land use/land cover (LULC) delineation and the Shannon entropy technique for assessing urban sprawl. Additionally, it integrates the cellular automata (CA)-Markov model and the analytical hierarchy process (AHP) for future projections. The results indicate a considerable shift from non-built-up to built-up areas, with the proportion of built-up areas expected to reach 36.2% by 2032 and 40.54% by 2052. These findings emphasize the importance of strategic urban management and sustainable planning. The study recommends adaptive urban planning strategies and highlights the value of integrating the CA Markov model and AHP for policymakers and urban planners. This can contribute to the discourse on sustainable urban development and informed decision-making.

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“…Over the years, numerous models have been developed to assess the ULULC change and growth prediction (Abdu, 2019;Baqa et al, 2021;Chaturvedi & de Vries, 2021;Das & Jain, 2022;Kumar & Agrawal, 2022;Zhang et al, 2022). Jat et al, 2008 classified two methodological approaches to urban growth analysisnumerical and spatial.…”

Section: Introductionmentioning

confidence: 99%

“…These are -Cellular Automata (CA), Artificial Neural Network (ANN), regression-based, fractal-based, agent-based, and decision-tree based. Among these, the CA models are widely applied in the context of statistical analyses of ULULC dynamics considering its capacity to assess transitional parameters (Arsanjani et al, 2011;Bhatta, 2010;Das & Jain, 2022;Deep & Saklani, 2014;Jafari et al, 2016;Kushwaha et al, 2021;Munthali et al, 2020;Shafizadeh Moghadam & Helbich, 2013;Yang et al, 2023;Zhang et al, 2023;Zhang et al, 2022). CA models have also been associated with the Logistic Regression (LR), Multi-Layer Perceptron (MLP), and Stochastic (St) models in different studies (Ahmed & Ahmed, 2012).…”

Section: Introductionmentioning

confidence: 99%

Performance testing of selected hybrid Markovian models in urban growth simulation of the Kolkata Metropolitan Area, India

Santra

Mitra²,

Routh

et al. 2023

Preprint

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In this study an attempt was made to compare the performance of three widely accepted Markovian models of urban growth based on Cellular Automata (CA_MC), Multi-Layer Perceptron (MLP_MC), and Logistic Regression (LR_MC) in the Kolkata Metropolitan Area. The long-term Landsat images (from 1975 to 2020) were used to study the urban growth. A set of performance metrics, i.e., Kappa, Probability of Detection, False Alarm Ratio, Critical Success Index, and Accuracy Score, were employed to assess the accuracy of the model outputs. Different factors and constraints, were considered to observe their impacts on urban growth. The results indicate that while AHP-based CA_MC performs better overall, relying on any one performance metric alone may provide a misleading conclusion. It was observed that the CA_MC with the AHP performed the best and used for future simulation of the urban land-use/cover maps was generated from 2025 to 2070 at regular intervals. Much of that happens at the expense of the agricultural lands and vegetation cover, which are predicted to decrease by 18% and 5.3%, respectively. The distance-directional growth analysis showed that the areas closer to the central locations are expected to reach saturation, and the fringe areas are expected to register higher urban growth.

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Assessment and Prediction of Urban Expansion Using CA-Based SLEUTH Urban Growth Model: A Case Study of Kolkata Metropolitan Area (KMA), West Bengal, India

Cited by 8 publications

References 76 publications

Urban Sprawl Simulation Mapping of Urmia (Iran) by Comparison of Cellular Automata–Markov Chain and Artificial Neural Network (ANN) Modeling Approach

Urban Sprawl Simulation Mapping of Urmia (Iran) by Comparison of Cellular Automata–Markov Chain and Artificial Neural Network (ANN) Modeling Approach

Projecting Urban Expansion by Analyzing Growth Patterns and Sustainable Planning Strategies—A Case Study of Kamrup Metropolitan, Assam, North-East India

Performance testing of selected hybrid Markovian models in urban growth simulation of the Kolkata Metropolitan Area, India

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