Comparative analysis of machine learning and conventional methods for waste generation forecasting
Bibliographic record
Abstract
1. Abstract The escalating complexity of waste management systems, driven by rapid environmental and socio-economic shifts, necessitates the development of robust multi-parameter waste generation prediction models. Conventional forecasting methods such as system dynamics, time series, and linear regression have been widely utilized; however, they often fail to fully capture the nuanced dynamics of waste generation, which is influenced by various seasonal and demographic factors. Artificial intelligence (AI) models have emerged as alternative predictors that perform advanced computational techniques to generate accurate forecasts. Despite the extensive research conducted, few review articles have assessed the feasibility of utilizing multiple prediction methods for different waste streams. None has comprehensively assessed and compared the various prediction methods for different waste streams. This review summarizes and analyzes the prediction methodologies implemented for different waste types. This systematic literature review compiles 119 articles from 2000-2024. A thorough analysis of AI-based models and a summary of the most influential explanatory variables were provided. The review indicates a predominant focus on municipal waste, with considerable gaps in the forecasting of construction and medical waste streams. System dynamics models were found to excel in strategic waste management planning but can be complex to calibrate and validate. Time series and regression analyses, while useful for identifying trends and relationships, often failed to adapt to rapid or unpredictable changes. Alternatively, machine learning algorithms offer robust capabilities for modeling complex and nonlinear data, although they require substantial data quality and are prone to overfitting. It is concluded that a combined hybrid approach is recommended, leveraging the strengths of different methods to provide more accurate waste generation forecasts. The critical analyses presented can offer insights to decision-makers in the waste management sector by providing key aspects concerning the efficiency and limitations of these predictors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".