PREDICTING OF URBAN EXPANSION USING CONVOLUTIONAL LSTM NETWORK MODEL: THE CASE OF SEOUL METROPOLITAN AREA, KOREA

Abstract

As urbanization progresses, many studies about the analysis and prediction of land-use change and urban sprawl have been conducted recently. As the sprawl phenomenon progresses rapidly, the urban expansion phenomenon became uncontrolled and it has affected negatively on the city's environment and transportation finally. So, it is essential to identify lands likely to be urbanized in the future because it aids in establishing land use plans and policies pre-acting the negative impact of spatially urban expansion the sprawl by determining factors affecting the urban sprawl. Previous studies based on statistical models are limited to identifying determining factors, so the prediction performance is low compared to deep learning. On the other hand, existing studies using machine learning and deep learning overlook selecting specific region-focused variables. Therefore, this study aims to analyze and predict changes in the Seoul Metropolitan Area's sprawl in Korea using the Convolutional Long Short-Term Memory Network (ConvLSTM) with factors at the city scale and neighboring factors at the local scale in the Seoul Metropolitan Area (SMA). ConvLSTM is a type of combination model: combining Recurrent Neural Network(RNN) and Convolutional Neural Network(CNN). This study showed that ConvLSTM with factors at the city and neighboring factors at the local scale predicted the urbanized land. The determinants contain population and roads ratio at the city scale, and neighboring urban lands, distance to the nearest subway stations, slope, and elevation at the local scale. The results reveal that predicted urban lands in 2020 increase over the entire region. In particular, the expected urban lands in 2020 increase by reducing farmlands in the southern part of the SMA. It is consistent with the trend of urbanized lands from 1980 to 2010. In addition, urbanization occurs in areas adjacent to Seoul due to the well-established urban infrastructure. The results of this study can be used as evidence to establish sustainable land use plans and regulations in the future.