Advanced predictive modeling of municipal solid waste management using robust machine learning
Ka Yin Chau, Massoud Moslehpour, Shin-Hung Pan, Abed Akhundzada · 2025
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Description
Chau and co-authors apply robust machine learning to forecasting city solid waste for better planning. Urban growth has pushed waste volumes up sharply. Old collect-and-dump planning cannot keep pace. The team compared four learning methods, from neural networks to logistic regression. A nine-step workflow covered data prep, model building, and checks. The dataset held 4,341 records with 20 factors, from density to waste mix.
Most records trained and tested the models, with 1,041 held for validation. The convolutional network gave the most accurate forecasts. Error and fit measures backed that ranking. The study argues data-driven forecasts can anchor smarter waste strategy for growing cities.
Catalog record
| CREATOR | Ka Yin Chau, Massoud Moslehpour, Shin-Hung Pan, Abed Akhundzada |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 50 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |