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Related Experiment Video

Updated: Jan 8, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.4K

Advanced predictive modeling of municipal solid waste management using robust machine learning.

Ka Yin Chau1,2, Massoud Moslehpour3,4, Shin-Hung Pan5

  • 1Centre for Quality Standard & Management, The Hang Seng University of Hong Kong, Hong Kong, China.

Scientific Reports
|December 15, 2025
PubMed
Summary

This study introduces Convolutional Neural Networks (CNN) for optimizing municipal solid waste management (MSWM) forecasting. CNN significantly outperforms other machine learning models, enabling more accurate waste generation predictions for efficient planning.

Keywords:
Convolutional neural networkMachine learningMunicipal solid waste managementPredictive modelingWaste optimization

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Last Updated: Jan 8, 2026

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.4K

Area of Science:

  • Environmental Science
  • Computer Science
  • Data Science

Background:

  • Rising urbanization and technology have increased municipal solid waste (MSW), demanding advanced predictive models for effective municipal solid waste management (MSWM).
  • Traditional waste management approaches are often reactive, lacking the foresight needed for sustainable and efficient operations.
  • Machine learning (ML) offers potential for developing proactive MSWM strategies.

Purpose of the Study:

  • To integrate and evaluate machine learning (ML) techniques, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Multilayer Perceptrons (MLP), and Logistic Regression (LR), for optimizing MSWM forecasting.
  • To pioneer the application of CNN for MSWM prediction, addressing a gap in current research.
  • To enhance strategic planning in MSWM through accurate waste generation predictions.

Main Methods:

  • A structured nine-step workflow was employed, including data collection, preprocessing, model development, and validation.
  • A Kaggle-sourced dataset of 4,341 records with 20 variables (e.g., population density, waste composition) was utilized for training, testing, and validation.
  • Performance was assessed using statistical metrics such as R-squared (R²) and Root Mean Squared Error (RMSE).

Main Results:

  • Convolutional Neural Networks (CNN) demonstrated superior accuracy across training, testing, and validation datasets, achieving R² values of 0.999, 0.996, and 0.996, respectively.
  • CNN outperformed SVM, MLP, and LR in predicting municipal solid waste generation.
  • The model's ability to handle nonlinear relationships and data irregularities led to precise predictions, improving route optimization and resource allocation.

Conclusions:

  • Machine learning, particularly CNN, offers a transformative approach to MSWM, enabling a shift from reactive to proactive management.
  • Accurate waste generation forecasting enhances operational efficiency and supports environmental sustainability goals.
  • The study highlights the novelty of using CNN for MSWM prediction and its regularization strategies to prevent overfitting.