Forecasting residential and nonresidential solid waste generation, disposal, and diversion using three machine learning approaches
Bibliographic record
Abstract
Abstract Accurate forecasting of solid waste quantities is essential for sustainable waste management planning, yet limited research exists in this area. This study develops a framework to forecast solid waste generation, disposal, and diversion quantities using three machine learning (ML) approaches: artificial neural networks (ANNs), support vector machines (SVMs), and multiple linear regression (MLR) models. The forecasting framework is based on 12 socioeconomic variables, the values of which were derived from publicly available data sources. Projections for 2023 to 2050 were developed considering data preprocessing, training, and testing, to create reliable datasets. Correlation analysis was used to rank predictor and response variables, and statistical tests were conducted to identify heteroscedasticity and linear relationships. A case study was conducted for Canada and four provinces: Alberta (AB), British Columbia (BC), Ontario (ON), and Quebec (QC). The results show that ML algorithms predict solid waste effectively, achieving coefficients of determination ( R 2 ) of 99.9% with ANNs and 98.6% with SVMs. The total waste generation for Canada, forecast through ANNs, SVMs, and MLRs, increased by 18.29%, 22.45%, and 22.61%, respectively, in the 28 years from 2023 to 2050. In 2050, the projected values of waste generation using the three methods were 43.67, 45.14, and 44.47 million tonnes, respectively, in Canada. ANN forecasts for 2050 project 7.75 million tonnes in AB, 5.36 in BC, 17.85 in ON, and 8.81 in QC. Waste generation is increasing with increasing population size. The method developed here can be used globally with appropriate data adjustments. The results can help in policy development and decision making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".