Published Paper Details:

Abstract Rapid urbanization and population growth have significantly increased the volume of municipal solid waste, placing pressure on existing waste management systems [1], [13]. Efficient segregation and timely collection of wet and dry waste are essential for sustainable urban development. However, conventional waste management practices rely heavily on historical averages and manual estimation, leading to inefficiencies in collection planning and resource utilization. This paper proposes EcoForecast AI, an Artificial Intelligence-based predictive analytics framework designed to anticipate wet and dry waste generation patterns. The framework analyzes historical waste data, seasonal variations, population dynamics, and consumption behavior to forecast future waste quantities. By integrating machine learning models with predictive analytics, the proposed system enables proactive waste collection planning, optimized resource allocation, and improved segregation strategies. Furthermore, Power BI is utilized as a dynamic visualization and reporting tool within the framework. By creating interactive dashboards, Power BI allows municipal authorities to monitor real-time waste generation trends, identify high-risk areas, and evaluate the effectiveness of collection strategies. The visualizations also support scenario analysis, enabling stakeholders to simulate the impact of policy changes, population growth, or seasonal variations on waste generation. The combination of AI-based forecasting with Power BI's intuitive dashboards enhances transparency, informed decision-making, and citizen engagement in smart waste management initiatives. Experimental evaluation using simulated municipal datasets demonstrates improved prediction accuracy, reduced forecasting error, and enhanced decision-making capabilities. EcoForecast AI, complemented by Power BI's visualization capabilities, offers a scalable and intelligent solution for smart and sustainable urban waste management systems.