Monitoring and prediction of land cover changes of Kirkuk City using machine learning and remote sensing data

Expansion of land cover (LC) is significantly impacted by man-made activities, particularly in areas that are expanding quickly. A CA-ANN model, which stands for cellular automata and artificial neural network, the combination is one of the many models that are commonly employed to evaluate potential changes in land cover (LC) using satellite images using Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI with spatial resolution 30 m. Examining shifts in LC patterns was the driving force behind this article over the previous thirty years (1993 to 2023) in Kirkuk City, Iraq. The available satellite pictures were analyzed using the Maximum Likelihood Supervised Classification (MLSC) approach in this article. The output of this study shows that from 1993 to 2023, the urban area is projected to rise by almost 215%, while the water area increased by nearly 150%, but the vegetation areas decreased from 1993 to 2023 by about 150%, however, the bare land slightly increased by about 2%. The CA-ANN model, whose kappa value was 0.97 and accuracy percentage was 93.06%, was made to make future LC expectations. The findings from the LC prediction model for the future indicate that by 2033 and 2043, the urban area will grow by 28% and 28.3% respectively, even though there will be less vegetation and less barren ground, factors that significantly influence urban planning and their implications for environmental management such as Environmental quality, City scale, Economic level, Industrial structure, Population structure and Government management and services. This study’s results may inform future efforts to improve the advancement of long-term sustainability in urban design and administration, as well as inform decision-making processes by governing bodies focused on improving ecological and environmental conditions within the designated study region.