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Related Experiment Video

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Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

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Forecasting municipal solid waste generation using artificial intelligence modelling approaches.

Maryam Abbasi1, Ali El Hanandeh1

  • 1Griffith School of Engineering, Griffith University, Nathan, QLD, Australia.

Waste Management (New York, N.Y.)
|June 15, 2016
PubMed
Summary
This summary is machine-generated.

Accurate municipal solid waste (MSW) forecasting is crucial for effective waste management. Artificial intelligence models, particularly ANFIS and kNN, reliably predict future MSW generation, aiding local governments in planning and resource preservation.

Keywords:
Adaptive neuro-fuzzy inference systemArtificial intelligenceArtificial neural networkMunicipal solid wasteSupport vector machinek-nearest neighbours

Related Experiment Videos

Last Updated: Mar 19, 2026

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

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Area of Science:

  • Environmental Science
  • Computer Science
  • Data Science

Background:

  • Effective municipal solid waste (MSW) management is vital for public health, environmental protection, and resource conservation.
  • Accurate forecasting of future waste generation is essential for designing and operating efficient MSW management systems.

Purpose of the Study:

  • To develop and evaluate artificial intelligence (AI) models for accurate forecasting of monthly MSW generation.
  • To compare the predictive performance of Support Vector Machine (SVM), Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), and k-nearest neighbours (kNN) algorithms.

Main Methods:

  • Time series data of monthly MSW generation from Logan City Council, Queensland, Australia, was used for training and testing AI models.
  • Four intelligent system algorithms (SVM, ANFIS, ANN, kNN) were employed to predict MSW quantities.
  • Model performance was evaluated based on prediction accuracy for both average monthly waste and peak generation.

Main Results:

  • AI models demonstrated strong predictive performance for MSW generation.
  • ANFIS achieved the highest accuracy in forecasting peak monthly waste quantities.
  • kNN effectively predicted the average monthly MSW generation.
  • Projected annual MSW for Logan City to reach 9.4×10^7 kg by 2020, with peak monthly waste reaching 9.37×10^6 kg.

Conclusions:

  • Machine learning algorithms, trained on waste generation time series, reliably predict monthly MSW generation.
  • AI-based forecasting models can significantly enhance the design and operation of MSW management systems.
  • The study provides valuable insights for waste management organizations in Logan City and similar regions.