Assessment of urban expansion susceptibility in major urban units of Bangladesh leveraging machine learning and geostatistical approach
Year: 2024
Type: article
Abstract: This study examines the governing factors and susceptibility zones for urban expansion in Bangladesh's major urban areas, including Dhaka, Barisal, Chittagong, Comilla, Narayanganj, Gazipur, Khulna, Sylhet, Rajshahi, Rangpur, and Mymensingh. The main goals of this research are to determine the impact of governing factors and to identify susceptibility zones for urban expansion in major urban units using a data-driven approach. By using governing factors (DEM, Slope, LST, NDVI, Population, distance to (industry, growth center, settlement, facilities, waterbody, road), and machine learning (Random Forest) and geostatistical approach (Binary Logistic Regression), the research identifies the most important factors influencing urban expansion, including NDVI, LST, waterbodies, roads, and settlements. The RF model's ROC-AOC values showed the highest accuracy (1.00) in Comilla and Mymensingh, moderate accuracy (0.99) in Barisal, Chittagong, Narayanganj, Gazipur, Khulna, and Rajshahi, and lower accuracy in Dhaka (0.98), Sylhet (0.89), and Rangpur (0.85). For the Binary Logistic Regression model, Comilla, Narayanganj, Gazipur, and Mymensingh had the best fit (Nagelkerke R 2 = 1.00), while Sylhet had the lowest significance (0.482). Furthermore, Khulna, a major urban unit, is the highest urban expansion susceptibility zone which is 35.72%. Rajshahi and Barisal are the moderate and low urban expansion susceptibility where 83.17% and 0.88% respectively. This unplanned and rapid urban expansion zone has also confronted policymakers and planners with an insurmountable challenge and stressed local governments' ability to manage and use their scarce land-based resources with geospatial data. Thus, this study's machine learning and geostatistical findings will help explain land cover change and urban expansion in Bangladesh's eleven metropolitan areas. This study will improve urban development understanding in Bangladesh. Findings will help planners, stakeholders, and policymakers understand urban expansion patterns, enabling better environmental planning.
Source: Journal of Urban Management
Authors: Mafrid Haydar, Sakib Hosan, Al Hossain Rafi
Institution: Khulna University of Engineering and Technology
Language: English
Topic: Remote Sensing and Land Use
Subfield: Atmospheric Science
Field: Earth and Planetary Sciences
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,200
OpenAlex ID: W4405112928