A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
SMART CITY PLANNING IN HYDERABAD : INTEGRATING GIS FOR SUSTAINABLE URBAN GROWTH
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 cityKeywords: AI in Urban Planning, Environmental Impact, GIS-Based Spatial Anlysis, Land Use, Machine Learning, Remote Sensing, Smart City, Sustainable Urban Growth, Urban Expension
SMART CITY PLANNING IN HYDERABAD : INTEGRATING GIS FOR SUSTAINABLE URBAN GROWTH
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 cityKeywords: AI in Urban Planning, Environmental Impact, GIS-Based Spatial Anlysis, Land Use, Machine Learning, Remote Sensing, Smart City, Sustainable Urban Growth, Urban Expension
SMART CITY PLANNING IN HYDERABAD : INTEGRATING GIS FOR SUSTAINABLE URBAN GROWTH
Nagaruju, Kotagiri (author) / Ramakrishna, S (author)
2024-06-26
doi:10.17509/k.v22i2.81742
Kokoh; Vol 22, No 2 (2024): Juli 2024; 141-156 ; 2828-5778 ; 1412-050X ; 10.17509/k.v22i2
Article (Journal)
Electronic Resource
English
| UB Braunschweig | 2013
| DSpace@MIT | 1987