Evaluating the effectiveness of the city master plan in regulating future urban spatial growth of Varanasi City, India

Anish Kumar¹Pankaj Kumar^1*Bharat Dahiya²Barbaros Gönençgil³Shipra Singh⁴Ashwani .¹Abhinav Rai¹

• ¹Department of Geography, Faculty of Social Sciences, University of Delhi, New Delhi, India
• ²Research Centre for Sustainable Development and Studies, School of Global Studies, Thammasat University, Pathumthani, Thailand
• ³Department of Geography, University of Turkey, Istanbul, Türkiye
• ⁴University of Delhi, New Delhi, India

This paper evaluates the effectiveness of the Varanasi City Master Plan 2031 in regulating urban growth by analyzing Land Use and Land Cover (LULC) changes. By comparing the model's predictions for 2031 with the Varanasi Development Authority's Master Plan, the study identifies discrepancies in the direction and extent of urban expansion. Rapid urbanization, driven by industrialization, migration, and infrastructural development, has dramatically reshaped Varanasi's spatial patterns. Utilizing remote sensing data from Landsat images (1990, 2000, 2010, and 2021) and integrating machine learning techniques, including the Multi-layer Perceptron and Markov Chain Analysis (MLP-MCA), this study simulates and predicts future urban expansion. The model's predictions, with an accuracy above 80%, offer critical insights for policymakers to revisit urban planning strategies. The built-up area has grown from 45.10 km² in 1990 to a projected 262.05 km² by 2031, representing a 480.95% increase over four decades. Simultaneously, agricultural acreage has declined from 908.23 km² to 656 km², a reduction of 252.23 km², or 27.77%, highlighting the shift from rural to urban land use. Notably, in the southwest, the Masterplan consistently exceeds predicted built-up areas across most zones, except in Zone 4 (9-12 km), with over-allocations around the Mughalsarai area. Furthermore, Sectors A, B, C, and D anticipate higher built-up areas, particularly in zones 6-9 km and 9-12 km. This study underscores the need for sustainable development planning to mitigate the negative impacts of rapid urbanization, such as loss of green spaces, environmental degradation, and urban heat island effects. The combined approach of remote sensing and machine learning provides a robust and replicable methodology for other rapidly urbanizing cities, ensuring future expansion aligns with sustainable development goals.