Comparative Analysisof Urban Sprawl through KNNand Random Forest Classification(RFC)MLTechniques

The majority of optimization strategies fail to take into account the dynamic impact of urban sprawl on the spatial criteria that underlie decision-making processes. Furthermore, the integration of the existing simulation methodology with land use optimization techniques toarrive at a sustainable judgment regarding the appropriate site involves intricate procedures. The urban heat island phenomenon is a prominent consequence of urban expansion and human activities, leading to elevated temperatures within cities compared to their rural surroundings. The extent of sprawl was estimated through ML algorithms and it was revealed that RFC provided promising results that were near to statistics by various administrative authorities. Urban vegetation plays a crucial role in countering the urban heating effect by providing cooling mechanisms through evaporation and shading. In this context, a study was conducted in Allama Iqbal Town, Lahore, focusing on the assessment of land use changes, as well as the analysis of Normalized Difference Vegetation Index and Land Surface Temperature data for the years 2000, 2010, and 2023, obtained from Landsat 5 and Landsat 8 satellite imagery. The findings reveal significant land use changes of 7.52% (36.2km2) in the study area. The built-up areas expanded by 50.76%, while smart green spaces decreased by 48.30%. The relationships between NDVI and LST demonstratea robust negative relationship (R² = 0.99). This research underscores the potential of utilizing GIS and remote sensing techniques to informurban planning, decision-making, and policy formulation, ultimately contributing to the creation of sustainable urban environments in Allama Iqbal Town.