During the last decade, urban growth has been increased rapidly in Gopalganj district of Bangladesh. Therefore, this study seeks to observe fluctuations in land use and land cover (LULC) in Gopalganj with its effects over the past two decades. Through Landsat 5 (TM) and 8 (OLI) data, this research focuses on Maximum Likelihood Supervised Classification (MLSC) technique for creating land-use classes of different years. Built-up areas have increased by 21.17% over the last two decades. Urban vegetation has declined in parallel with the increase in vacant land in the district from 2000 to 2010. However, in the next ten years, it has occupied about 72.76 square km of bare-land. By using Artificial Neural Network (ANN) with integrated cellular automation (CA) simulation, the model predicted that urban areas would grow by 10.88% in the central and north-western regions of the district by 2050. Urban vegetation will decrease by 4.09%, with a significant reduction in bare land and water bodies. The accuracy of the predicted LULC is 89.48% based on validation result. This prediction may help municipal and administrative authorities, urban planners to achieve a planned and sustainable future city of Gopalganj.

在过去十年中，孟加拉国Gopalganj区的城市增长迅速增长。因此，本研究旨在观察戈帕尔加尼的土地利用和土地覆被波动（LULC）及其在过去二十年中的影响。通过Landsat 5（TM）和8（OLI）数据，本研究着重于最大似然监督分类（MLSC）技术，以创建不同年份的土地利用类别。在过去的二十年中，建筑面积增加了21.17％。从2000年到2010年，该地区的空地减少与城市植被的减少同步。但是，在接下来的十年中，它已占据了约72.76平方公里的荒地。通过使用人工神经网络（ANN）和集成的蜂窝自动化（CA）仿真，该模型预测市区将增长10。到2050年，该地区的中部和西北地区将达到88％。城市植被将减少4.09％，其中裸露的土地和水体将大大减少。根据验证结果，预测的LULC的准确性为89.48％。该预测可能会帮助市政当局，行政当局，城市规划人员实现Gopalganj的规划且可持续的未来城市。