Solid waste generation prediction model framework using socioeconomic and demographic factors with real-time MSW...

Area of Science:

• Environmental Science
• Computer Science
• Operations Research

Background:

• Effective waste management strategies require accurate predictions of end-of-life product flows.
• Data availability and quality significantly impact the development of predictive waste models.
• Existing models may not adequately address source material heterogeneity and varying data scenarios.

Purpose of the Study:

• To propose a flexible framework for developing predictive models of end-of-life product flows.
• To guide the analysis of data and balance model complexity with development time.
• To enable accurate prediction of waste stream quantity and quality for informed municipal planning.

Main Methods:

• Development of a framework adaptable to heterogeneous source materials and data availability.
• Application of agent-based simulation models within a geographic information systems (GIS) environment.
• Modeling of municipal solid waste generation using socioeconomic, demographic, and collection data.

Main Results:

• A case study in Gatineau demonstrated the framework's applicability.
• The models accurately predicted municipal solid waste generation, quantity, and quality.
• The framework successfully adapted to scenarios with and without radio frequency identification (RFID) chips.

Conclusions:

• The proposed framework provides a robust approach to modeling end-of-life product flows.
• Accurate waste stream prediction supports better environmental impact assessments for waste management.
• The methodology facilitates data-driven decision-making for municipal waste management strategies.