Akademik Veri Yönetim Sistemi
Frontiers in Sustainable Cities, cilt.7, 2025 (ESCI, Scopus)
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.