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<p>Urban growth prediction is vital for sustainable urban planning and resource management. This study employs a hybrid Cellular Automata (CA) and Artificial Neural Network (ANN) model to predict urban expansion in Indore, one of India’s fastest-growing cities. The model integrates CA’s spatial dynamics with ANN's computational capabilities to simulate land-use changes over time. Using spatial data inputs such as land-use maps, population density, and infrastructure development, the ANN model was trained to drive CA-based simulations. This approach captures the nonlinear complexities of urban growth while accounting for spatial relationships and neighbourhood effects. The results predict detailed patterns of urban sprawl, identifying growth hotspots and key areas requiring attention for future urban planning. The findings provide actionable insights for urban planners and policymakers to manage Indore's expansion sustainably, addressing challenges associated with unplanned growth. The CA-ANN model demonstrates its reliability and adaptability for urban growth simulation, offering a valuable tool for planning in other rapidly urbanizing regions. This research underscores the importance of integrating advanced modelling techniques with spatial analysis to support informed decision-making and sustainable development strategies.</p>

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Urban growth prediction using CA-ANN model and spatial analysis for planning policy in Indore city, India

  • 期刊论文

  • 发表日期：

    2025年6月4日

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  • Gopal Kumar,
  • Supriya Vyas,
  • Shashikant Nishant Sharma,
  • Kavita Dehalwar

摘要

Urban growth prediction is vital for sustainable urban planning and resource management. This study employs a hybrid Cellular Automata (CA) and Artificial Neural Network (ANN) model to predict urban expansion in Indore, one of India’s fastest-growing cities. The model integrates CA’s spatial dynamics with ANN's computational capabilities to simulate land-use changes over time. Using spatial data inputs such as land-use maps, population density, and infrastructure development, the ANN model was trained to drive CA-based simulations. This approach captures the nonlinear complexities of urban growth while accounting for spatial relationships and neighbourhood effects. The results predict detailed patterns of urban sprawl, identifying growth hotspots and key areas requiring attention for future urban planning. The findings provide actionable insights for urban planners and policymakers to manage Indore's expansion sustainably, addressing challenges associated with unplanned growth. The CA-ANN model demonstrates its reliability and adaptability for urban growth simulation, offering a valuable tool for planning in other rapidly urbanizing regions. This research underscores the importance of integrating advanced modelling techniques with spatial analysis to support informed decision-making and sustainable development strategies.