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研究生：	陳俊源 Chen, Chun-Yuan
論文名稱：	邁向零排放：通過通用機器學習實現智慧垃圾掩埋場聚類、採礦選址及焚化底渣資源化 Towards zero emissions: Employing intelligent waste management, the case studies of landfills clustering, landfill mining sites selection, and recapturing bottom ash to resources via general machine learning
指導教授：	余騰鐸 Yu, Teng To
學位類別：	博士 Doctor
系所名稱：	工學院 - 資源工程學系 Department of Resources Engineering
論文出版年：	2023
畢業學年度：	112
語文別：	英文
論文頁數：	316
中文關鍵詞：	機械學習、掩埋場採礦、廢棄物管理、捲積式類神精網路、黑面琵鷺、多評準決策、焚化底渣、均勻流形近似及投影
外文關鍵詞：	machine learning, landfill mining, waste management, convolutional neural networks (CNNs), Platalea minor, multi-criteria decision-making (MCDM), municipal incineration bottom ash (MIBA), uniform manifold approximation and projection (UMAP)
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本論文呼應世界淨零排放趨勢所興起對溫室氣體排放的逆向工程概念，提出廢棄物管理的兩個逆向迴圈架構，兩者分別運用先進的機械學習技術的元素來進行操作，分別將廢棄物被丟棄前及丟棄後行資源化，從而實踐淨零排放目的。第一個逆向迴圈運用於當前掩埋場有關的領域管理，包含提出運用專家猶豫的模糊語言HFLTS，K-means及計量的場位模型(CPLM)演算法進行多評準決策(MCDM)掩埋場採礦選址框架，及提出運用均勻流形近似及投影(uniform manifold approximation and projection, UMAP)結合魯汶(Louvain)的社群模塊聚類演算法進行掩埋場風險管理聚類框架，將特徵相似的掩埋場進行聚類，以達到風險定位；此兩框架的提出可提供管理者對區域掩埋場相關管理決策參考，並更有系統的進行大範圍選址，且及時的決策工具；第二個逆向循環環圈則是運用卷積式類神經網路(CNNs)及隨機森林RF機械學習技術，來探討焚化底渣資源化的影像辨識，評估底渣被處理前將其中不可燃的電池、金屬、塑膠等物質提取，逆轉為資源並極小化掩埋的可能，並且提出「太極焚化底渣影像」(TaiChi_MIBA)數據集(dataset)，提供未來探討垃圾焚化底渣這議題的智慧影像辨識研究。以上兩個逆向循環對廢棄物領域提出全新的管理思維概念，並貢獻於決策框架開發及新穎之技術工具的提出，研究主要結論如下：
1、於第一個逆向迴圈
(1)所提出的CPLM基礎的選址框架，使用於臺灣的402處掩埋場案例，發現使用HFLTS專家多決策評準，人口密度是影響最重要的權重，其次是水體及海岸線，最後是坡度，另在台灣既有掩埋場分類中。套用於中南部114處掩埋場地區的選址結果，有17處需要移除，有45處可以轉為暫存設施，可以復育約66公頃土地，並且估計可貢獻新臺幣39.6到132億元土地經濟效益。
(2)另外UMAP為基礎的掩埋場聚類框架，使用於臺灣402處掩埋場可聚類為10類，其中具有2項以上之環境加乘效應，包含7.9%為海岸邊的河口掩埋場，8.2%為斷層及濱河掩埋場，17.6%高人口密度及濱河掩埋場，而受影響較少的"健康的"掩埋場11.2%，並且發現世界濱危物種黑面琵鷺棲地與海岸掩埋場距離有重疊。另將本研究提出的聚類框架套用到英格蘭的19,801處掩埋場中，可將其聚類為110類，並且亦有類似臺灣案例中之2項以上環境加乘情況。揭示全世界的掩埋場普遍已受地球及人類活動之變動影響其對環境之風險程度，建議當前掩埋場管理亟需導入有地球及人為變動策略及具氣候調適性之模式，來因應變動地球氣候變遷、地殼變動及人口遷移之諸多改變。
2、於第二個逆向迴圈，所提出的CNNs基礎的智慧分類框架，使用於焚化底渣之研究，由正常光組成5,060個及紅外光組成5,062個影像集，探討可見光、一般白光、紅外線及弱紅外線光源下，於不同粒徑-砂石大小(<2 mm)及礫石大小(4~19 mm)的焚化底渣對於不同類型之雜質10類的品質情境，比較於不同解析度及卷積網路的長度結構之辨識能力。所建立的模式影像辨識準確率為83.1~97.3%，其中雜物於礫石大小的底渣下較不易被分辨，與此同時，不同光源會影響辨識效能及整體辨識結果，金屬也由於表面氧化之顏色改變，相較於塑膠類較不易被分辨；另外第二階段比較CNNs及RF不同技術，於10,123影像7種類別的數據集中，發現CNNs相對於RF有明顯壓倒的辨識效能（準確度CNNs為96.7%對比RF為55.8%）。但如考慮二維之辨識，即不考慮雜質類別只考慮「合格」「不合格」情況下，RF對「不合格」有88.8%到90.5%辨識準確度，揭示RF對於簡易之辨別任務仍有發展潛力。
綜合以上結果，本論文所提出之兩個逆向迴圈結合機械學習技術提出的三個智慧管理框架，包含前端的掩埋場採礦選址決策框架及風險聚類框架，搭配後端的CNNs影像辨識整合框架（尤其是垃圾經過處理後在低對比環境下之影像辨識），對於實踐一個「無掩埋」及「無煙囪」的零排放世界，提供未來具體可行且具前瞻技術發展之策略及方法。

This dissertation introduces two reverse loops in waste management aligned with the global trend of zero emissions. The first loop focuses on landfill management, employing fuzzy language (HFLTS), K-means, and Capacitated Plant Location Model (CPLM) algorithms framework for multi-criteria decision-making (MCDM) sites selection. Besides, a uniform manifold approximation and projection (UMAP) combined with the Louvain clustering framework for landfill-risk management to aid this loop. The second loop utilizes convolutional neural networks (CNNs) and Random Forest (RF), which are part of machine learning to explore image recognition for resource recovery from municipal incineration bottom ash (MIBA), minimizing landfilling. The proposed frameworks offer decision support for landfill management and systematic site selection, along with real-time decision-making tools. These innovative concepts contribute to waste management for rethinking, decision framework development, and novel technical tools. The key summaries listed as follows:
In the first reverse loop
(1) The proposed CPLM based Framework, applied to over 402 landfill cases in Taiwan, it was found that using HFLTS expert multi-decision criteria, population density has the most significant impact, followed by water bodies and coastlines, and finally, slope. Applying this to 114 landfill areas in central and southern Taiwan resulted in the identification of 17 sites for removal, 45 sites suitable for conversion into temporary facilities, reclaiming about 66 hectares of land. Estimated economic benefits range from NT$3.96 to 13.2 billion.
(2) The proposed UMAP clustering Framework research on landfills in Taiwan categorized over 400 sites by using 17 noteworthy features that transform to Landfill Suitability Index (LSI). This study clustered landfills into 10 clusters and identified several clusters with significant extreme locations, types, revealing environmental risk synergies, including estuary landfills (7.9%), fault-water-body landfills (8.2%), and densely-populated-water-body landfills (17.6%). Furthermore, a critical discovery of endangered Platalea minor habitats near these estuary landfills was made. Additionally, this work identified “healthy” landfills (11.2%). Applying the same method to 19,801 landfills in England revealed 110 categories and similar environmental risk synergies. This underscores the global impact of environmental changes on landfills, urging the adoption of strategic adaptive management to address climate change, crustal movement, and population shifts.
In the second reverse loop
The proposed of the Intelligent Waste-to-Resources Framework, employing to the MIBA, utilizing 5,060 images from visible/normal light and 5,062 images from infrared light, the research investigates the quality scenarios of 10 categories of quality, including different impure items remain in MIBA of different particle sizes (soil <2 mm and gravel, 4~19mm) under visible, white, infrared, and weak infrared light sources (called TaiChi_MIBA dataset). Models reach an accuracy ranging 83.1~97.3%. It compares the recognition performance under different resolutions (80, 160, 320 square pixels) and CNNs depths. It was observed that impurities in gravel-sized bottom ash are less distinguishable, and different light sources affect recognition capabilities and overall results. Metals, due to surface oxidation color changes, are relatively harder to distinguish compared to plastic. In the second phase comparing CNNs and RF technologies on a dataset of 10,123 images across 7 categories (called Chency_MIBA dataset), CNNs exhibited significantly superior recognition performance with an accuracy of 96.7%, while RF ranged from 55.8%. However, considering two-dimensional recognition, focusing only on "Qualified" or "Unqualified" without specific impurity categories, RF showed a recognition accuracy of 88.8% to 90.5% for "Unqualified," indicating potential for RF in simpler identification tasks.
In summary, the two reverse loops proposed in this dissertation, combining machine learning techniques, encompass a front-end framework for landfill site selection decisions and risk clustering. This is complemented by a back-end integration of CNN-based intelligent image recognition, especially in low-contrast environments post-landfilling. Together, they present a feasible and forward-looking technological development strategy for achieving a "Landfill-free" and "Stack-free" zero emission world.

CHAPTER 1 Introduction 1
1. Outline of this Dissertation 4
2. Motivation of Proposing an Intelligent LMSS Decision-making Framework 7
3. Motivation of Proposing a High-dimensional Landfills Clustering Framework 11
4. Motivation of Proposing a Reversing Waste-to-Resources Framework 14
CHAPTER 2 Literature review 19
1. Landfills 19
2. From Open Dump Sites to Modern Sanitary Landfills 20
3. Landfills Life Cycle 21
4. Historical/legacy Landfills 23
5. Landfill Mining and Driver Force 24
6. Landfill Suitability Index (LSI) 29
7. Multi-Criteria Implemented for LSS 30
7.1. Distance to Surface Water Bodies 32
7.10. Visibility 40
7.2. Distance to Coastline 33
7.3. Topographic Slope 34
7.4. Elevation 35
7.5. Distance from Fault Zones 36
7.6. Population Density 37
7.7. Distance to Schools and Hospitals 38
7.8. Distance to Environmentally Protected and Sensitive Areas 39
7.9. Groundwater level 39
8. Integrating Landfill-mining and Landfill-siting 40
8.1 Landfill-mining: The Material Flows and Assessment Method 43
8.2. Explore Existing Landfills to Mining Phase 48
8.3. The Approach of MCDM Combine GIS to LMSS 49
8.4. Landfill Mining Site Selection (LMSS) Decision Framework 52
8.4.1. Distance between a landfill and a surface water body (WB) 52
8.4.2. Distance between a landfill and the coastline (CL) 53
8.4.3. Population density (PD) 53
8.4.4. Topographic slope (SP) 56
8.4.5. Other criteria regarding LMSS 56
9. Location and Allocation Techniques 65
10. Multiple-criteria decision-making (MCDM) 66
10.1. HFLTS (Hesitant Fuzzy Linguistic Term Set) 66
10.2. Shannon's Entropy 68
10.3. Analytical Hierarchy Process (AHP) 68
10.4. Technique of Order Preference Similarity to the Ideal Solution (TOPSIS) 72
11. Machine Learning Algorithm 73
11.1. K-mean Algorithm 73
11.2. High-dimensional clustering 74
11.2.1. Principal component analysis (PAC) 74
11.2.2. t-distributed Stochastic Neighbor Embedding (t-SNE) 75
11.2.3. Uniform Manifold Approximation and Projection (UMAP) 76
11.3. Random Forest (RF) 77
11.4. Convolutional Neural Network (CNN) 78
11.4.1. CNN Architecture and basic principles 78
11.4.2. CNN general application for engineering and mineral science 82
11.4.3. CNN apply to Remote Sensing and hyperspectral 83
11.4.4. CNN apply to waste 87
12. Research Gaps 92
12.1. Gap to intelligent landfill mining decision-making 92
12.2. Gap to intelligent landfill management 95
12.3. Gap to reverse waste to resources 95
CHAPTER 3 Materials, Methods, and Proposed Frameworks 99
1. Proposed an Intelligent LMSS Decision-making Framework 99
1.1. Methodology of proposed decision framework 99
1.2. GIS-based MCDM 102
1.3. Multi-criteria HFLTS 104
1.4. K-means 106
1.5. Capacitated plant location model (CPLM) 107
2. Proposed a High-dimensional Landfills Clustering Framework 109
2.1 Methodology of proposed clustering framework 109
2.2. Uniform manifold approximation and projection (UMAP) 112
2.3. Louvain Algorithm 114
3. Propose a Reversing Waste-to-Resources Framework 116
3.1. Methodology of reversing waste to resources framework 116
3.2. MIBA Quality Recognition 118
3.3. Image collection 118
3.4. CNNs architecture, hyper-parameters, and training processing 119
CHAPTER 4 Results and Discussion 137
1 Intelligent Landfill-mining Site Selection Decision-making Framework 137
1.1. Study area 137
1.2. Considered criteria for LMSS 138
1.3. Results 142
1.3.1. HFLTS and MCDM 142
1.3.2. Questionnaire for three experts 146
1.3.3. Classification of current landfill sites 148
1.3.4. Capacitated Plant Location Model (CPLM) 149
1.3.5. Scenario analysis 151
1.4. Discussion 155
1.4.1. Intelligent LMSS Decision-making Framework 156
1.5. Summary 161
2. High-dimensional Landfills Clustering Framework 164
2.1. Employing uniform manifold approximation and projection (UMAP) 164
2.2. Study landfills, datasets and leading cases embedded 164
2.3. Considered criteria 165
2.3.1. Distance to a surface water body 165
2.3.2. Distance to coastline 165
2.3.3. Population density 165
2.3.4. Topographic slope 166
2.3.5. Elevation 166
2.3.6. Groundwater level 166
2.3.7. Distance to a fault zone 166
2.3.8. Distance to an environmentally protected and sensitive area 167
2.3.9. Distance to a school or a hospital 168
2.4. Results 169
2.4.1. Datasets for the proposed framework 169
2.4.2. UMAP application for geographical and environmental features 172
2.4.2.1. Original features 172
2.4.2.2. LSI features 173
2.4.3. UMAP applied to original feature clustering 179
2.4.4. UMAP applied to LSI clustering 181
2.4.5. Determine the geographical features of clustered landfills 182
2.4.5. Leading cases 184
2.4.6. Pending cases 184
2.4.7. Extended lifespan cases 184
2.4.8. Land reclamation cases 185
2.5. Discussion 186
2.6. Summary 190
3. Intelligent Waste-to-Resources Framework 192
3.1. Quality Scenarios of MIBA in Taiwan 192
3.2. Performance Evaluation 196
3.3. Results and The Training History 197
3.4. Recognition for Considered Quality 204
3.4.1. Multi-class task 204
3.4.2. Confusion matrices 207
3.5. Visualization task 208
3.6. Softmax values analysis of test images 212
3.7. Discussion 214
3.8. Summary: Application to MIBA Plants in Taiwan 218
4. The Comparison of Different CNN, RF and Connection Layers 224
4.1. Simulate quality scenarios of MIBA 224
4.2. Random Forest (RF) 225
4.3. Proposed AI models 231
4.4. Proposed CNN network's depth 232
4.5. Proposed CNNs training history 233
4.6. Multiple classification 234
4.7. Confusion matrices 234
4.8. Binary classification task 234
4.9. Visual images features 234
4.10. Summary 234
4.11. Real pictures of hardwares 234
CHAPTER 5 Conclusions and Outlooks 251
1. Conclude this Dissertation 251
2. Outlooks 254
Bibliography 257
Appendix A. The main codes used in my dissertation 280
Appendix B. Elsevier policies of copyright statement	299
                                    

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