請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98858完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 何昊哲 | zh_TW |
| dc.contributor.advisor | Hao-Che Ho | en |
| dc.contributor.author | 胡卡林 | zh_TW |
| dc.contributor.author | Abdikarim Hassan Hussein | en |
| dc.date.accessioned | 2025-08-19T16:28:22Z | - |
| dc.date.available | 2025-08-20 | - |
| dc.date.copyright | 2025-08-19 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-08-12 | - |
| dc.identifier.citation | Reference
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Investigation of the current situation and prospects for the development of rainwater harvesting as a tool to confront water scarcity worldwide. Water (Switzerland), 11(10), 1–16. https://doi.org/10.3390/w11102168 Yegizaw, E. S., Ejegu, M. A., Tolossa, A. T., Teka, A. H., Andualem, T. G., Tegegne, M. A., Walle, W. M., Shibeshie, S. E., & Dirar, T. M. (2022). Geospatial and AHP Approach Rainwater Harvesting Site Identification in Drought-Prone Areas, South Gonder Zone, Northwest Ethiopia. Journal of the Indian Society of Remote Sensing, 50(7), 1321–1331. https://doi.org/10.1007/s12524-022-01528-5 ZADEH, L. A., & Department. (1965). Fuzzy Sets*. Procedia Computer Science, 207, 4525–4534. https://doi.org/10.1016/j.procs.2022.09.516 Zhou, L. M., Jin, S. L., Liu, C. A., Xiong, Y. C., Si, J. T., Li, X. G., Gan, Y. T., & Li, F. M. (2012). Ridge-furrow and plastic-mulching tillage enhances maize-soil interactions: Opportunities and challenges in a semiarid agroecosystem. Field Crops Research, 126, 181–188. https://doi.org/10.1016/j.fcr.2011.10.010 Ziadat, F. M., Mazahreh, S. S., & Oweis, T. Y. (2006). A GIS-based Approach for Assessing Water Harvesting Suitability in a Badia Benchmark Watershed in Jordan. 2006(Isco), 1–4. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98858 | - |
| dc.description.abstract | Rainwater harvesting (RWH) is a sustainable solution for mitigating water scarcity, particularly in regions experiencing irregular and declining precipitation. This research focuses on the Southern Province of Zambia, a region that is becoming increasingly susceptible to climate fluctuations and the resulting challenges to sustained water security. Despite previous studies have explored RWH, there remains a significant shortfall in developing integrated frameworks that are spatially optimized to address the unique requirements of semi-arid regions. Here, the study developed a hybrid model integrating Geographic Information Systems (GIS) with Multi-Criteria Decision Analysis (MCDA) to delineate suitable zones for RWH implementation. In the evaluation, twelve criteria were taken into account, addressing physical, environmental, and socio-economic factors, with the Standardized Precipitation Index (SPI) serving as a climatic indicator. The Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) was applied to assess the relative weights of the criteria, identifying rainfall, curve number, and slope as the most influential factors. Suitability maps were generated and classified into five categories including not and low suitable regions, most, highly, and moderately suitable. Results from Fuzzy-AHP and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) indicated that 62% and 37.5% of the study area, respectively, are favorable for RWH. The northern and southwestern regions exhibited the highest suitability due to their advantageous physical and socio-economic conditions. Sensitivity analysis, conducted by varying the fuzziness degree (FD), confirmed the robustness of the model. Validation through TOPSIS revealed a 63% spatial agreement and 67% accuracy, supporting reliability of the model. This research presents a reproducible, affordable, and climate-adaptive approach for sustainable RWH site selection and informed water resource planning in drought-prone regions. | zh_TW |
| dc.description.abstract | Rainwater harvesting (RWH) is a sustainable solution for mitigating water scarcity, particularly in regions experiencing irregular and declining precipitation. This research focuses on the Southern Province of Zambia, a region that is becoming increasingly susceptible to climate fluctuations and the resulting challenges to sustained water security. Despite previous studies have explored RWH, there remains a significant shortfall in developing integrated frameworks that are spatially optimized to address the unique requirements of semi-arid regions. Here, the study developed a hybrid model integrating Geographic Information Systems (GIS) with Multi-Criteria Decision Analysis (MCDA) to delineate suitable zones for RWH implementation. In the evaluation, twelve criteria were taken into account, addressing physical, environmental, and socio-economic factors, with the Standardized Precipitation Index (SPI) serving as a climatic indicator. The Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) was applied to assess the relative weights of the criteria, identifying rainfall, curve number, and slope as the most influential factors. Suitability maps were generated and classified into five categories including not and low suitable regions, most, highly, and moderately suitable. Results from Fuzzy-AHP and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) indicated that 62% and 37.5% of the study area, respectively, are favorable for RWH. The northern and southwestern regions exhibited the highest suitability due to their advantageous physical and socio-economic conditions. Sensitivity analysis, conducted by varying the fuzziness degree (FD), confirmed the robustness of the model. Validation through TOPSIS revealed a 63% spatial agreement and 67% accuracy, supporting reliability of the model. This research presents a reproducible, affordable, and climate-adaptive approach for sustainable RWH site selection and informed water resource planning in drought-prone regions. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-08-19T16:28:22Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-08-19T16:28:22Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Table of Contents
MASTER'S THESIS ACCEPTANCE CERTIFICATE I Acknowledgement II Abstract III Table of Contents V List of figures VII List of Tables IX Chapter 1: Introduction 1 1.1 Background 1 1.2 Objectives 3 1.2.1 Specific objectives 4 1.3 Thesis Organization 4 Chapter 2: Literature Review 5 2.1 Overview of Global and Regional Context of Water Scarcity 5 2.2 Global and Regional RWH Practices 7 2.3 Key Factors Influencing RWH Site Selection 9 2.3.1 Factors Considered in Previous Studies 9 2.3.2 Standard Precipitation Index (SPI) 11 2.4 GIS-Based MCDA for Identifying RWH Sites 12 Chapter 3: Study Area and Data 16 3.1 Study Area 16 3.2 Data Source 17 3.3 Design of Expert-Based Pairwise Comparison Framework 19 3.3 Preparation of Thematic Layers 23 3.3.1 Physical Factors 25 3.3.2 Environmental Factors 32 3.3.3 Socioeconomic Factors 36 3.4 Standardizing Decision Criteria 40 Chapter 4: Methodology 42 4.1 Fuzzy Analytical Hierarchy Process (Fuzzy-AHP) 42 4.1.1 Constructing Fuzzy Pairwise Matrix 42 4.1.2 Consistency Ratio 43 4.1.3 Fuzzification of Crip Value of AHP 43 4.1.4 Geometric Mean Calculation 44 4.1.5 Degree of Possibility and Weight Calculations 45 4.1.6 Normalizing Weights of Criteria 47 4.2 Sensitivity Analysis 48 4.3 Mapping of RWH Suitability 48 4.4 TOPSIS 49 4.4.1 Developing the Decision Matrix 49 4.4.2 Normalizing the Decision Matrix 50 4.4.3 Assigning Weights to Criteria 50 4.4.4 Identifying the Ideal Solution 50 4.4.5 Determination of the Euclidean Distance 51 4.4.6 Calculating Relative Closeness (CR) 52 4.5 Comparing Fuzzy-AHP and TOPSIS for Suitability Raster Maps 53 4.5.1 Change Detection Analysis 53 4.5.2 Confusion Matrix Analysis 53 4.5.3 Correlation Analysis of Fuzzy-AHP and TOPSIS 54 Chapter 5: Results and Discussions 58 5.1 Fuzzy-AHP Result 58 5.1.1 Determination of Relative Weights for Decision Criteria 58 5.1.2 Fuzzy-AHP-Based RWH Suitability Mapping 63 5.2 Sensitivity Analysis 65 5.3 TOPSIS Results and Alternative Rankings 70 5.3.1 Alternative Analysis 70 5.3.2 Application of the TOPSIS Method for Mapping RWH Suitability 72 5.4 Comparative Analysis of Fuzzy-AHP and TOPSIS Outputs 74 5.4.1 Change Detection 74 5.4.2 Confusion Matrix Analysis 78 5.4.3 Correlation Analysis 81 Chapter 6: Conclusions and Recommendations 86 6.1 Conclusion 86 6.2 Recommendations 87 Reference 90 Appendix A 99 List of figures Figure 1: Study area 16 Figure 2: Elevation map 26 Figure 3: Slope map of the study area 27 Figure 4: Soil texture map 28 Figure 5: Assigned RWH suitability scores based on soil texture 29 Figure 6:Drainage density map 30 Figure 7: LULC classification map 31 Figure 8: Stepwise function for LULC 32 Figure 9: Map of the rainfall 2023 33 Figure 10: Curve number map 34 Figure 11:Standard precipitation index (SPI) map 36 Figure 12: Proximity to roads map 37 Figure 13:Proximity to agriculture map 38 Figure 14:Distance to settlement map 39 Figure 15:Distance to streams orders 40 Figure 16: Standardizing decision criteria map 41 Figure 17:Possibility of M2≥M1 for Triangular Fuzzy Numbers (Chang, 1996) 47 Figure 18: Spatial Mapping Workflow of TOPSIS Results 52 Figure 19: Flowchart of the Methodological Framework 57 Figure 20: Criteria weights obtained from experts' survey 59 Figure 21: Consistency ratio of seven experts' survey 59 Figure 22: Spatial classification map obtained through Fuzzy-AHP 64 Figure 23: Suitability class distribution: (a) Area, (b) Proportion 64 Figure 24: (a) Criteria Weight Trends, (b) Weight Variation Across FD Levels 67 Figure 25: RWH Suitability Maps Generated Under Varying FD Levels 68 Figure 26: Variation in Suitability Class Areas Across Different FD 69 Figure 27: (a) Relative closeness values; (b) Percentage of categories in Alternatives 71 Figure 28: (a) Potential mapping for RWH sites, (b) Area of different classes and (c) percentage distribution of area through TOPSIS 73 Figure 29: Change Detection Map Comparing Suitability Classifications from Fuzzy-AHP and TOPSIS 75 Figure 30: Binary classification of TOPSIS and Fuzzy-AHP suitability maps 79 Figure 31: Accuracy results of confusion matrix 80 Figure 32: normality testing for data extracted from Fuzzy-AHP map 83 Figure 33: Normality test for data extracted from TOPSIS map 84 Figure 34:Workflow for Raster-Based Change Detection Between Fuzzy-AHP and TOPSIS Outputs 99 List of Tables Table 1: Description of data sources 18 Table 2: Profiles of Experts Involved in the Pairwise Comparison 19 Table 3: Saaty’s Scale of relative importance 20 Table 4: Pairwise Comparison Matrix by Expert 1 20 Table 5: Pairwise Comparison Matrix by Expert 2 21 Table 6: Pairwise Comparison Matrix by Expert 3 21 Table 7: Pairwise Comparison Matrix by Expert 4 21 Table 8: Pairwise Comparison Matrix by Expert 5 22 Table 9: Pairwise Comparison Matrix by Expert 6 22 Table 10: Pairwise Comparison Matrix by Expert 7 23 Table 11: Selected Criteria for RWH Site Selection with References 24 Table 12: Classification of drought conditions based on SPI ranges 35 Table 13: RI of AHP 43 Table 14: Saaty’s Scale Used in AHP and Fuzzy-AHP Judgments 44 Table 15: Input parameters for compute change raster 53 Table 16: Fuzzy-AHP Output Weights 60 Table 17: Computed weights and priority rankings of decision factors 61 Table 18: Sensitivity of criteria weights across varying fuzziness degrees 66 Table 19: Obtained RC through TOPSIS analysis 70 Table 20: Spatial Agreement Analysis of Suitability Scores 76 Table 21: Spatial Discrepancies in Suitability Classifications 77 Table 22: Descriptive Statistics for ANOVA 81 Table 23: Descriptive Statistics 82 Table 24: Spearman’s rho coefficient 84 | - |
| dc.language.iso | en | - |
| dc.subject | none | zh_TW |
| dc.subject | Fuzzy-AHP | en |
| dc.subject | Rainwater Harvesting (RWH) | en |
| dc.subject | Semi-Arid and Arid Regions (SARs) | en |
| dc.subject | Multi-Criteria Decision Analysis (MCDA) | en |
| dc.subject | TOPSIS | en |
| dc.subject | Geographic Information Systems (GIS) | en |
| dc.title | 半乾旱與乾旱地區永續雨水收集適址之最佳化方法 | zh_TW |
| dc.title | An Optimized Method for Sustainable Rainwater Harvesting Sites in Semi-Arid and Arid regions | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 李鴻源;葉克家 | zh_TW |
| dc.contributor.oralexamcommittee | Hong-Yuan Lee;Keh-Chia Yeh | en |
| dc.subject.keyword | none, | zh_TW |
| dc.subject.keyword | Rainwater Harvesting (RWH),Geographic Information Systems (GIS),Fuzzy-AHP,TOPSIS,Multi-Criteria Decision Analysis (MCDA),Semi-Arid and Arid Regions (SARs), | en |
| dc.relation.page | 99 | - |
| dc.identifier.doi | 10.6342/NTU202503692 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-08-14 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 土木工程學系 | - |
| dc.date.embargo-lift | 2025-08-20 | - |
| 顯示於系所單位： | 土木工程學系 | |
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