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008			251103s20259999th u ms t 000 eng d
035			_a.b12467042
099		9	_aAIT Thesis no.EV-25-01
100	1		_aTesfamarian, Abraham Habtemichael
245	1	0	_aAssessment of solid waste generation and compositions in AIT : _ba machine learning approach
260			_aPathum Thani, Thailand : _bAsian Institute of Technology, _c2025
300			_a105 leaves : _bill.+ _e1 online resource
490	1		_aThesis ; _vno. EV-25-01
500			_aA thesis submitted in partial fulfillment of the requirements for the degree of Master of Engineering in Environmental Engineering and Management
502			_aThesis (M. Eng.) - Asian Institute of Technology, 2025
520			_aThis 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.
650		0	_aWaste management _zThailand
650		0	_aIntegrated solid waste management _zThailand
650		0	_aMachine learning
700	0		_aThammarat Koottatep, _eChairperson
700	1		_aGhimire, Anish, _eExamination Committee
700	0		_aSarawut Ninsawat, _eExamination Committee
710	2		_aMinistry of Agriculture, Eritrea, _eScholarship Donor
710	2		_aAIT Scholarship, _eScholarship Donor
810	2		_aAsian Institute of Technology. _tThesis ; _vno. EV-25-01
856	4	0	_3Full-Text _uhttp://203.159.5.9/ait-thesis/detail.php?q=B22989
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