Forecasting urban sprawl dynamics in Islamabad : a neural network approach

Abstract

In the past two decades, Islamabad has experienced significant urbanization. As a result of inadequate urban planning and spatial distribution, it has significantly influenced land use–land cover (LULC) changes and green areas. To assess these changes, there is an increasing need for reliable and appropriate information about urbanization. Landsat imagery is categorized into four thematic classes using a supervised classification method called the support vector machine (SVM): built-up, bareland, vegetation, and water. The results of the change detection of post-classification show that the city region increased from 6.37% (58.09 km2) in 2000 to 28.18% (256.49 km2) in 2020, while vegetation decreased from 46.97% (428.28 km2) to 34.77% (316.53 km2) and bareland decreased from 45.45% (414.37 km2) to 35.87% (326.49 km2). Utilizing a land change modeler (LCM), forecasts of the future conditions in 2025, 2030, and 2035 are predicted. The artificial neural network (ANN) model embedded in IDRISI software 18.0v based on a well-defined backpropagation (BP) algorithm was used to simulate future urban sprawl considering the historical pattern for 2015–2020. Selected landscape morphological measures were used to quantify and analyze changes in spatial structure patterns. According to the data, the urban area grew at a pace of 4.84% between 2015 and 2020 and will grow at a rate of 1.47% between 2020 and 2035. This growth in the metropolitan area will encroach further into vegetation and bareland. If the existing patterns of change persist over the next ten years, a drop in the mean Euclidian Nearest Neighbor Distance (ENN) of vegetation patches is anticipated (from 104.57 m to 101.46 m over 2020–2035), indicating an accelerated transformation of the landscape. Future urban prediction modeling revealed that there would be a huge increase of 49% in urban areas until the year 2035 compared to the year 2000. The results show that in rapidly urbanizing areas, there is an urgent need to enhance land use laws and policies to ensure the sustainability of the ecosystem, urban development, and the preservation of natural resources.