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Updated: Sep 13, 2025

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
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ProWaste for proactive urban waste management using IoT and machine learning.

Thompson Stephan1, S M Hari Krishna2, Chia-Chen Lin3

  • 1Thumbay College of Management and AI in Healthcare, Gulf Medical University, Ajman, UAE.

Scientific Reports
|July 30, 2025
PubMed
Summary

ProWaste, a smart waste management platform, uses IoT and machine learning to predict overflowing waste collection centers. This data-driven approach prioritizes servicing, improving urban sustainability and public health.

Keywords:
ClassificationFeature selectionInternet of thingsSmart waste managementUrban waste collectionWrapper-based optimization

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Area of Science:

  • Environmental Science and Engineering
  • Computer Science and Artificial Intelligence
  • Urban Planning and Sustainability

Background:

  • Urban waste collection centers (WCCs) frequently overflow due to reactive maintenance schedules, leading to public health risks and hindering smart city sustainability.
  • Overspill, odor, and leachate from WCCs pose significant challenges to rapidly growing urban environments.

Purpose of the Study:

  • To introduce ProWaste, an Internet-of-Things (IoT) and machine learning platform designed for proactive prioritization of WCC servicing.
  • To develop a data-driven forecasting system to optimize waste management operations in smart cities.

Main Methods:

  • Collected data from sensors, public APIs, and a mobile app, including population density, weather, maintenance history, and waste build-up.
  • Benchmarked 25 classifiers, selecting a Decision Tree Classifier for its balance of interpretability and accuracy.
  • Utilized Binary Particle Swarm Optimization (BPSO) for feature selection and SHAP analysis for model interpretability.

Main Results:

  • A three-feature model, identified through BPSO, achieved over 99% accuracy in predicting WCC criticality on a hold-out test set.
  • The ProWaste platform successfully integrates data streams and machine learning predictions into a Sustainable Smart Waste Management (SSWM) application.
  • SHAP analysis confirmed the interpretability of the predictive model, enhancing trust and understanding.

Conclusions:

  • ProWaste offers a proactive, data-driven solution to urban waste management, significantly improving efficiency and reducing operational costs.
  • The platform can eliminate missed pickups, reduce on-road inspections, and decrease data bandwidth requirements compared to traditional methods.
  • The ProWaste architecture is transferable to other cities and adaptable for managing recycling and composting streams, promoting broader sustainable waste management.