Predicting future urban growth and its impacts on the surrounding environment using artificial neural network and earth observation in Kigali city

Abstract

A significant problem and difficulty for urban and country planners is land use activities. Urban growth modeling and management are challenging issues. Today, it is understood that cities are dynamic, non-linear, complex process systems. A system that can handle these intricacies must be designed, which is a difficult task. The quantity of urban land needed in the future must be estimated by local governments using urban development models, given the expected growth of residential, commercial, recreational, and other urban uses within the boundaries. Numerous negative effects of this kind of unsuitable urban expansion include increased traffic and mobility needs, decreased landscape attractiveness, land use fragmentation, loss of biodiversity, and changes to the hydrological cycle This study evaluates the Land Use Land Cover (LULC) in Kigali, the capital of Rwanda, which has a landscape that is developing quickly. In this study, we use support vector classification and CA-ANN (cellular automata-artificial neural network) techniques to evaluate past, current, and future changes in LULC of Kigali city. We examined 1990, 2000, 2010 that were taken by The Regional Centre for Mapping of Resources for Development (RCMRD) having a Spatial resolution of 30 m that were resampled to match the 2020 raster that was taken by ESRI taken September 22nd 2022 which has a spatial resolution of 10 m which were used to evaluate the LULC of the research area in the past and in the present. The distance to major routes, height, slope, and population data sets were used. In this study, a suitable methodology for modeling urban expansion using satellite remotely sensed data is provided and analyzed. The findings of the models show that, if the current trend rate is maintained, the city trend will double by 2030. Building models will aid in better understanding built-up area dynamics and direct sustainable urban developmental planning for the city of Kigali's future urban growth