Municipal solid waste management forecasting using neural networks at discharge point scale.

Area of Science:

• Environmental Science
• Urban Planning
• Data Science

Background:

• Accelerating urbanization and population growth increase Municipal Solid Waste (MSW) generation, creating significant environmental and logistical challenges.
• Current waste management often relies on aggregated data, lacking the granularity needed for localized, dynamic decision-making.

Purpose of the Study:

• To develop a predictive framework for forecasting the behavior of individual Waste Discharge Points (DPs).
• To enhance urban waste management decision-making through localized and granular predictions.
• To enable proactive waste collection planning by providing accurate short-term forecasts.

Main Methods:

• Development of a data-driven predictive framework.
• Incorporation of contextual and temporal information for localized forecasting.
• Utilizing AI-based predictive models for waste behavior analysis.

Main Results:

• The framework successfully forecasts the behavior of individual DPs, offering finer predictive granularity.
• Data-driven approaches incorporating contextual and temporal data enhance waste collection planning.
• Accurate short-term forecasts enable a shift from reactive to proactive waste collection strategies.

Conclusions:

• The proposed approach facilitates more efficient, scalable, and intelligent waste collection systems.
• AI-based predictive models are crucial for advancing sustainable MSW management.
• This research supports the transition to proactive urban waste management, reducing costs and environmental footprints.