AI SearchAssessment of solid waste generation and compositions in AIT : a machine learning approach
Call Number: AIT Thesis no.EV-25-01 Material type:
SeriesSeries: Asian Institute of Technology. Thesis ; no. EV-25-01Publication details: Pathum Thani, Thailand : Asian Institute of Technology, 2025Description: 105 leaves : ill.+ 1 online resourceSubject(s): Online resources: Dissertation note: Thesis (M. Eng.) - Asian Institute of Technology, 2025 Summary: This study explored the generation and composition of solid waste at the Asian Institute of Technology (AIT) in Thailand by applying an integrated approach that combines empirical waste analysis with machine learning (ML) techniques. Data was gathered through field sampling, online surveys, interviews, institutional sources (OFAM and OSA), and prior thesis work. Waste composition was examined using the quartering method across academic, residential, and commercial areas, while recyclable potential was analyzed to assess opportunities for resource recovery. A seasonal study (Dec 2024{u2013}Mar 2025) showed that food waste made up the largest portion (51.24%), followed by plastics (17.58%). In the current year, AIT generated 2.08 tons/day, averaging 0.8 kg/person. In major events (e.g., graduation), waste level exceeded 80 kg/collector/trip, 5 times the baseline, but contributed only 8% of the total annual volume. Regular weekdays cumulatively contributed 92%, suggesting targeted interventions. Per capita waste generation rose to 0.9 kg/day during COVID-19 but stabilized post-pandemic. Eight ML models were tested: regression (RF, SVR, GB, XGBoost) and classification (RF, J48, LR, XGBoost). Models used an 80/20 training-test split and were evaluated using five-fold cross-validation. SVR (R²=0.90, MAE=0.06) and RF (accuracy=82%) outperformed others. SHAP analysis revealed that kitchen access, household size, and age range were significant factors influencing waste generation. This research improved upon D. Zhang et al. (2020) by using zone-wide quartering instead of single-zone sampling. Also advancing Cha et al. (2023), by combining SHAP values with heat maps, pairing SVR with RF, and reducing MSE by 90.3% (from 0.31 to 0.03). The R² was also 45% higher than Cha{u2019}s AE-ANN model.The framework supports SDGs 11 and 12, showing potential to reduce landfill dependence by 35%, cut paper waste by 50% through two-sided printing and an electronic mailing policy, and divert 72% of event waste via targeted segregation. The findings offer interpretable, ML-based campus waste solutions, potentially scalable to other institutions.
| Cover image | Item type | Current library | Home library | Collection | Shelving location | Call number | Materials specified | Vol info | URL | Copy number | Status | Notes | Date due | Barcode | Item holds | Item hold queue priority | Course reserves | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
40-Archives
|
Asian Institute of Technology Library Archives | AIT Thesis no.EV-25-01 (Browse shelf(Opens below)) | 1 | Available | 30050120423800 | |||||||||||||
67-Electronic Resource
|
Asian Institute of Technology Library Archives | AIT Thesis no.EV-25-01 (Browse shelf(Opens below)) | 1 | Not for loan |
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Environmental Engineering and Management
Thesis (M. Eng.) - Asian Institute of Technology, 2025
This study explored the generation and composition of solid waste at the Asian Institute of Technology (AIT) in Thailand by applying an integrated approach that combines empirical waste analysis with machine learning (ML) techniques. Data was gathered through field sampling, online surveys, interviews, institutional sources (OFAM and OSA), and prior thesis work. Waste composition was examined using the quartering method across academic, residential, and commercial areas, while recyclable potential was analyzed to assess opportunities for resource recovery. A seasonal study (Dec 2024{u2013}Mar 2025) showed that food waste made up the largest portion (51.24%), followed by plastics (17.58%). In the current year, AIT generated 2.08 tons/day, averaging 0.8 kg/person. In major events (e.g., graduation), waste level exceeded 80 kg/collector/trip, 5 times the baseline, but contributed only 8% of the total annual volume. Regular weekdays cumulatively contributed 92%, suggesting targeted interventions. Per capita waste generation rose to 0.9 kg/day during COVID-19 but stabilized post-pandemic. Eight ML models were tested: regression (RF, SVR, GB, XGBoost) and classification (RF, J48, LR, XGBoost). Models used an 80/20 training-test split and were evaluated using five-fold cross-validation. SVR (R²=0.90, MAE=0.06) and RF (accuracy=82%) outperformed others. SHAP analysis revealed that kitchen access, household size, and age range were significant factors influencing waste generation. This research improved upon D. Zhang et al. (2020) by using zone-wide quartering instead of single-zone sampling. Also advancing Cha et al. (2023), by combining SHAP values with heat maps, pairing SVR with RF, and reducing MSE by 90.3% (from 0.31 to 0.03). The R² was also 45% higher than Cha{u2019}s AE-ANN model.The framework supports SDGs 11 and 12, showing potential to reduce landfill dependence by 35%, cut paper waste by 50% through two-sided printing and an electronic mailing policy, and divert 72% of event waste via targeted segregation. The findings offer interpretable, ML-based campus waste solutions, potentially scalable to other institutions.
There are no comments on this title.