Urban Growth Prediction Using Satellite Imageries with Applications of Machine Learning Algorithm in Pokhara Metropolitan City

Abstract

Accurate land use and land cover (LULC) classification is essential for sustainable resource management and understanding landscape changes due to climate variations. High-quality datasets and robust classification methods are necessary for effective LULC classification. This study evaluates the LULC pattern of Pokhara Metropolitan City using Landsat 5, 7, and 8 satellite imagery over 30 years (1991-2021) through supervised classification techniques, employing random forest (RF) and support vector machine (SVM) algorithms to compare their accuracy. The study further utilizes the artificial neural network (ANN) model in the MOLUSCE plugin in QGIS to predict future LULC patterns for 2031 and 2050. Results indicate a significant urbanization trend, with agricultural land decreasing from 54.74% in 1991 to 32.02% in 2021, while built-up areas and forest cover increased from 2.93% to 10.96% and from 38.87% to 53.56%, respectively. Future predictions show continued trends, with agricultural land decreasing to 26.62% and built-up areas increasing to 12.08% by 2031. The findings provide crucial spatial information for urban planners, meteorologists, and policymakers to develop local and regional strategies for sustainable development.