predictive analysis and data-driven approaches for developing sustainable municipal solid waste management strategies in smart cities: an urban analysis of madurai

The development of predictive models for waste handling remains the primary barrier to sustainable waste management in madurai smart city for accurate policy formulation. the research brought together quantitative data collected through stakeholder interviews of waste policies and quantitative methods measuring waste collection rates using population projections and machine learning algorithms. xg gradient boosting (a machine learning technology) generated the most precise outcomes when we evaluated our models because it forecasted organic waste with a coefficient of determination (r²) of 0.91 and inorganic waste with r² = 0.91, surpassing traditional linear regression models that reported organic waste prediction accuracy at r² = 0.867 and inorganic waste prediction at r² = 0.577. the temporal evaluation revealed seasonal patterns. cultural events generated waste volumes that were 30% higher than normal yearly rates. the spatial assessment results show that zones 1 and 4 are waste generation priority areas requiring a 75% capacity expansion during the next decade. waste production in madurai is projected to reach 1,000 metric tons each day by 2030, representing an increase of 54% from the present levels. an assessment of the findings indicates that the processing capacity should be expanded to 350 tons per day (tpd), and waste segregation policies must implement zone-based rules while optimizing delivery routes to decrease vehicle fuel usage by 15-20%. the data-based structure provides the groundwork for constructing enduring waste management facilities throughout madurai and similar fast-developing urban territories.