SMART CITY PLANNING IN HYDERABAD : INTEGRATING GIS FOR SUSTAINABLE URBAN GROWTH
Abstract
Urban expansion in Hyderabad has accelerated due to rapid population growth, economic development, and infrastructural advancements, leading to unplanned settlements, traffic congestion, and environmental degradation. Traditional urban planning methods fail to address these challenges effectively. This study integrates Artificial Intelligence (AI) and Geographic Information Systems (GIS) to develop a predictive urban expansion model, enabling accurate forecasting of land use changes, high-risk growth zones, and infrastructure needs. Using machine learning algorithms (Random Forest, CNN, LSTM) and multi-temporal remote sensing data, the study analyzes spatial trends, environmental impact, and urban heat island effects. The findings highlight the loss of 40% green cover, increased congestion, and rising pollution levels by 2045. This research provides data-driven recommendations for smart city planning, sustainable resource management, and climate-resilient urban policies, ensuring Hyderabad's transformation into a well-planned, environmentally sustainable, and technologically advanced smart city
Keywords: AI in Urban Planning, Environmental Impact, GIS-Based Spatial Anlysis, Land Use, Machine Learning, Remote Sensing, Smart City, Sustainable Urban Growth, Urban Expension
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Ali, M. A., & Kamraju, M. (2018). Remote sensing and GIS study on change detection in land use land cover in Kondarapet Mandal, Karimnagar District, Telangana. Remote Sensing, 93(2), 118-125.
Alshahrani, H., & Kumar, P. (2022). Remote sensing and GIS applications for urban sprawl detection and prediction using machine learning models. Geospatial Journal of Urban Development, 29(2), 75-98.
Bhat, T., & Nanda, S. (2021). Deep learning-based convolutional networks for land cover classification in rapidly growing cities. Remote Sensing and AI Applications, 17(3), 120-142.
Dhingra, S., & Pathak, H. (2022). Spatio-temporal analysis of urban heat islands using remote sensing and AI modeling. International Journal of Environmental Research, 30(1), 89-115.
Gupta, S., & Sharma, R. (2022). Analyzing urban congestion and air pollution using GIS and remote sensing: A case study of Hyderabad. Journal of Urban Environmental Studies, 18(2), 112-130.
Jain, R., & Kapoor, S. (2021). Urban sustainability and land use planning: A comparative GIS-based analysis of Indian metro cities. Journal of Urban Geography and Development, 21(4), 65-89.
Khan, M., & Ahmed, T. (2021). Machine learning approaches for urban growth prediction: A comparative study of Random Forest, SVM, and CNN. International Journal of Geospatial Science, 27(3), 85-101.
Kishan, A. B., Ali, M. A., & Kamraju, M. (2019). Impact of urban growth on land use-land cover in Hyderabad City. Journal of Emerging Technologies and Innovative Research (JETIR), 6(5), 308-316.
Kumar, R., Patel, V., & Singh, P. (2021). Geospatial risk mapping of urban expansion: Identifying high-risk zones for sustainable city planning. Remote Sensing and Urban Studies, 22(1), 45-68.
Li, Y., & Zhao, X. (2020). AI-based spatial simulations for urban land use change modeling: A case study from China. Smart City Planning and GIS, 19(2), 58-77.
Mehta, D., Reddy, S., & Kumar, P. (2021). The impact of rapid urbanization on public infrastructure: A GIS-based analysis of Hyderabad’s development. Journal of Urban Planning & Sustainability, 34(4), 92-110.
Mukherjee, A., Verma, S., & Joshi, P. (2021). Using GIS and satellite data to assess land-use transformation in Indian metropolitan cities. Geospatial Intelligence Journal, 15(2), 29-54.
Patil, A., & Verma, R. (2020). Predicting urban expansion with deep learning and remote sensing: A case study of Hyderabad’s metropolitan region. AI and Geospatial Science Review, 25(1), 57-79.
Prathap, T. S., Yamini, Y., Ali, M. A., & Kamraju, M. (2019). Change detection in land use land cover using geospatial technology and open-source data in Lankamalla Forest and the neighborhood: A case study in Andhra Pradesh, India. Journal of Emerging Technologies and Innovative Research (JETIR), 6(4), 557-573.
Rao, K., & Prasad, H. (2020). The impact of urbanization on water bodies: A case study of Hyderabad using GIS and remote sensing. Environmental Planning Journal, 19(2), 38-64.
Reddy, G., & Kumar, N. (2021). Analyzing land use changes using remote sensing: Implications for urban sustainability in Hyderabad. Journal of Urban Geography, 28(3), 75-93.
Sharma, V., & Das, P. (2019). Challenges of traditional urban planning: The need for AI-driven geospatial intelligence. International Journal of Urban Studies, 31(4), 101-126.
Singh, R., & Yadav, P. (2020). A multi-temporal analysis of urban sprawl using Landsat data and geospatial modeling. Remote Sensing Applications for Urban Growth, 20(1), 33-56.
Sridhar, K., & Balaji, P. (2020). Remote sensing and GIS for urban transformation analysis: A case of Hyderabad’s peri-urban regions. Journal of Urban & Regional Planning, 14(3), 112-128.
Tiwari, A., & Goel, S. (2021). AI-based real-time monitoring of urban expansion: A GIS integrated approach. International Journal of Geospatial Engineering, 24(1), 77-98.
United Nations. (2019). Smart cities and sustainable urban development report. United Nations Development Programme (UNDP).
Vani, M., & Kamraju, M. (2016). Impact of urbanisation on lakes: A case study of Hyderabad. Journal of Urban and Regional Studies, 5(1).
Venkatesh, K., & Kamraju, M. (2018). Urban sprawl and sustainable development in Hyderabad: A geoinformatic approach. International Journal of Creative Research Thoughts (IJCRT), 6(1), 1285-1294.
Verma, S., & Patel, R. (2021). Integrating AI and GIS for predictive urban planning: A case study of Indian metropolitan regions. Journal of Smart Cities and Geospatial Science, 26(1), 90-108.
World Bank. (2021). Urban development in South Asia: Strategies for sustainable growth. World Bank Publications.
Wu, J., & Liu, H. (2021). The role of AI in monitoring land-use dynamics: A review of geospatial applications. Journal of Remote Sensing and Urban Ecology, 18(3), 72-98.
Zhang, L., & Sun, Y. (2022). AI and spatial intelligence in urban risk assessment: An application in India’s smart cities. Geospatial Intelligence Review, 20(2), 109-126.
DOI: https://doi.org/10.17509/k.v22i2.81742
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