Integrating Cellular Automata and Agent-Based Modeling for Predicting Urban Growth: A Case of Dehradun City

This paper proposes a framework for land-use land cover (LULC) simulation for urban growth estimation. The framework couples Cellular Automata Markov (CAM) and agent-based modeling (ABM) to explore the impact of socioeconomic factors, spatial neighborhoods, stakeholder choices, and development plans on LULC. The approach applies CA model to examine the spatiotemporal change in LULC patterns and ABM to observe the role of different socioeconomic drivers and commercial factors in the simulated environment to predict future urban LULC. For Dehradun city of India, the analysis of spatial patterns shows spatial accuracy of 87.86%. Most of the urban agriculture and vacant areas are converted to commercial, low-, medium-, and high-density residential areas. The coupled CAM-ABM model was found more efficient in prediction compared to the traditional CAM model. The methodologies presented in the paper can help decision makers foresight the requirements, thus leading to better resource management and informed decision making.