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Enhancing door-to-door waste collection forecasting through ML.

Luca Pasa1, Giuseppe Angelini2, Michele Ballarin2

  • 1Department of Mathematics, University of Padova, Via Trieste, 63, Padova, 35121, Italy.

Waste Management (New York, N.Y.)
|January 8, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) models can forecast door-to-door waste collection needs for efficient municipal solid waste (MSW) management. This approach optimizes collection routes and resource allocation in urban areas.

Keywords:
AI for sustainabilityGreenMachine learningMunicipal solid wasteWaste collection forecasting

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Area of Science:

  • Environmental Science
  • Computer Science
  • Urban Planning

Background:

  • Municipal solid waste (MSW) management faces challenges due to increasing urban populations and waste generation.
  • Optimizing door-to-door waste collection is crucial for efficiency and sustainability.
  • Existing waste management strategies often lack predictive capabilities for dynamic collection needs.

Purpose of the Study:

  • To investigate the application of machine learning (ML) techniques for forecasting door-to-door waste collection.
  • To predict household waste collection requirements at a user level.
  • To assess ML algorithms' efficacy in optimizing waste collection operations.

Main Methods:

  • Utilized comprehensive waste data from a northeastern Italian municipality, including various waste types.
  • Developed ML models to predict daily waste exposure likelihood and forecast fulfilled pickups (weekly/monthly).
  • Employed temporal data splitting for model training and evaluation, forecasting future user behavior.

Main Results:

  • ML models demonstrated significant potential in predicting waste collection needs.
  • The study successfully forecasted user behavior for waste collection at a granular level.
  • Identified the effectiveness of ML in enhancing waste collection efficiency and route planning.

Conclusions:

  • Machine learning offers a powerful tool for optimizing municipal solid waste collection.
  • Tailoring strategies to specific waste categories and pickup frequencies is essential for optimal ML performance.
  • The findings support improved resource allocation and environmental sustainability in urban waste management.