Comparative Analysis of Machine Learning Algorithms to Predict Municipal Solid Waste

Zenodo (CERN European Organization for Nuclear Research) 2026 Dataset Open access

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Authors: Pedro Aguilar, Pedro Peñafiel-Arcos, Marcos Barahona, Wilson Gustavo Chango
DOI: 10.5281/zenodo.19008987
Cited by: 0

Abstract

This repository provides the curated dataset and full reproducible workflow for the study Comparative Analysis of Machine Learning Algorithms to Predict Municipal Solid Waste. It includes the cleaned source--day panel dataset, preprocessing, feature engineering, model training, temporal backtesting, baselines, uncertainty estimation, and scripts/notebooks to reproduce the reported tables and figures. The study compares fourteen machine learning algorithms for predicting daily organic and inorganic waste generation in La Joya de los Sachas, Ecuador.

Fields of Study & Subjects

Municipal Solid Waste Management Forecasting Techniques and Applications Waste Management and Recycling

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
13 March 2026
Language
EN
Document type
Dataset