Artificial Intelligence Applications in Predictive Waste Management

Anna Müller

Artificial Intelligence Applications in Predictive Waste Management

Author(s)	Dr. Anna Müller
Country	United States
Abstract	Conventional waste-management systems frequently respond to waste after it has already accumulated, resulting in overflowing containers, inefficient collection schedules, unnecessary fuel consumption, inconsistent resource allocation, and avoidable disposal costs. Artificial intelligence enables a transition from reactive operations to predictive waste management by identifying temporal, demographic, socioeconomic, climatic, and spatial patterns associated with waste generation. This study develops and evaluates a simulation-based artificial intelligence framework for forecasting municipal solid waste generation and supporting collection and diversion planning. A hypothetical dataset containing 1,460 daily observations was designed around operationally relevant variables, including population density, household activity, commercial intensity, temperature, precipitation, holidays, previous waste volumes, recycling participation, and collection frequency. Four forecasting approaches—multiple linear regression, support vector regression, random forest, and an artificial neural network—were evaluated using mean absolute error, root mean square error, mean absolute percentage error, and the coefficient of determination. The simulated results indicate that the artificial neural network produced the strongest predictive performance, achieving a mean absolute percentage error of 5.7% and a coefficient of determination of 0.93. A predictive routing scenario also indicated potential reductions in collection distance, fuel consumption, missed pickups, and mixed-waste disposal. The study concludes that AI can strengthen waste-management planning when forecasting outputs are integrated with operational rules, source-separation programs, human oversight, and transparent data governance. Since the numerical results are simulated, they should be interpreted as a methodological demonstration rather than evidence from an actual municipality.
Keywords	artificial intelligence; predictive waste management; municipal solid waste; machine learning; waste forecasting; smart collection; route optimization; circular economy.
Field	Engineering
Published In	Volume 1, Issue 1, January-February 2019
Published On	2019-02-17

View / Download PDF File

About AIJRI Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+1-872-302-3851	editor@aijri.com

American International Journal of Research and Innovation

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Artificial Intelligence Applications in Predictive Waste Management

Share this