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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 許少瑜 | zh_TW |
| dc.contributor.advisor | Shao-Yiu Hsu | en |
| dc.contributor.author | 馮博煜 | zh_TW |
| dc.contributor.author | Bo-Yu Fung | en |
| dc.date.accessioned | 2023-10-03T16:22:51Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-10-03 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-07-12 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/90503 | - |
| dc.description.abstract | 本研究以台灣中部農業土壤為範例，以土壤質地(砂、坋、黏含量)、總體密度與有機質含量作為解釋變數(或稱輸入特徵)，應用多變量線性迴歸、隨機森林、類神經網路三種不同架構建立台灣本土土壤保水曲線的轉換方程式(pedotransfer function，PTF)，並進一步分析PTF的輸入特徵重要性與不確定性。為了釐清不同輸入特徵對於PTF最終預測結果的影響力，本研究透過排列重要性的分析，得知PTF在基質勢能(matric potential)大於-0.1bar時，以總體密度作為最重要的輸入特徵；隨著基質勢能的減少總體密度重要性下降，坋粒與有機質含量重要性則逐漸上升。而為了評估非線性PTF的預測結果(不為常態分佈且具有較高偏度)之不確定性，本研究使用無母數自助法評估PTF的誤差95%信賴區間與95%預測區間。藉由衡量預測區間之實際涵蓋率，確認土壤含水量的實際涵蓋率皆介於95±1%，再次檢核無母數自助法可以有效評估非線性模型的不確定性。預測區間的建立除了提供PTF預測值的可靠度資訊外，此預測區間未來可用於檢驗土壤量測資料有效性的依據。最後，本研究成果也顯示三種不同架構的本土PTF，針對台灣土壤進行轉換的結果皆優於美國農業部開發之PTF─Rosetta3，再次確認建立台灣本土PTF的必要性。 | zh_TW |
| dc.description.abstract | This study takes agricultural soils in central Taiwan as an example and uses soil texture (sand, silt, clay content), bulk density, and organic matter content as explanatory variables (or input features) to develop pedotransfer function (PTF) for the soil water retention curve using three different frameworks: multiple linear regression, random forest, and artificial neural networks. The study further analyzes the uncertainty and feature importance of PTF. Through permutation importance analysis, the study reveals that bulk density is the most important feature when the matric potential is larger than -0.1 bar in PTF predictions. As the matric potential decreases, the importance of bulk density decreases while the importance of silt and organic matter content gradually increases. To evaluate the uncertainty of the nonlinear PTF predictions, which do not follow a normal distribution and have higher skewness, the study uses the nonparametric bootstrap method to assess the 95% confidence intervals of PTF’s error and 95% prediction intervals of PTF. By measuring the coverage probability of the prediction intervals, the study confirms that the coverage probability of soil water content is approximately 95±1%, validating the effectiveness of the nonparametric bootstrap method in assessing the uncertainty of nonlinear models. In addition to providing reliability information for PTF predictions, the establishment of prediction intervals can also be used as a basis for testing the validity of soil measurement data. Finally, the results of this study demonstrate that the three different frameworks of local PTF outperform the PTF developed by United States Department of Agriculture(USDA), Rosetta3, in converting Taiwanese soils, reaffirming the necessity of developing local PTF for Taiwan. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-10-03T16:22:51Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-10-03T16:22:51Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 #
謝誌 i 中文摘要 ii ABSTRACT iii 目錄 v 圖目錄 vii 表目錄 x 第1章 緒論 1 1.1 研究背景及動機 1 1.2 研究目的 2 第2章 文獻回顧 3 2.1 土壤保水曲線 3 2.1.1 土壤水總勢能 3 2.1.2 與土壤含水量之關係 3 2.1.3 VG model 5 2.2 土壤轉換方程式(Pedotransfer Function，PTF) 9 2.2.1 點型與連續型PTF 9 2.2.2 建立PTF的工具或方法 11 2.2.3 作為預測因子的土壤性質 14 2.2.4 轉換表現評估指標 17 2.2.5 不確定性分析 19 2.2.6 Rosetta 21 第3章 研究方法 23 3.1 研究架構及流程 23 3.1.1 土壤資料 24 3.1.2 土壤數據整理 28 3.1.3 Vw15bar補值 31 3.2 模型選擇與建置 33 3.2.1 多變量線性迴歸(Multiple Linear Regression，MLR) 33 3.2.2 隨機森林(Random Forest，RF) 34 3.2.3 類神經網路(Artificial Neural Network，ANN) 36 3.3 特徵重要性 38 3.3.1 基尼重要性(Gini importance) 38 3.3.2 排列重要性 40 3.4 不確定性分析 42 3.4.1 誤差期望值 42 3.4.2 預測區間 44 第4章 結果與討論 47 4.1 訓練、測試資料集探索與描述 47 4.2 模型訓練 56 4.2.1 多變量線性迴歸 56 4.2.2 隨機森林 59 4.2.3 類神經網路 60 4.2.4 土壤含水量訓練集預測結果 61 4.3 模型預測 62 4.3.1 土壤含水量測試集預測結果 62 4.3.2 參數重要性 62 4.3.3 誤差期望值之信賴區間 66 4.3.4 模型預測區間 67 4.4 與Rosetta3之比較 70 4.5 增加額外解釋變數的影響 73 4.6 點型與連續型PTF之不確定性比較 76 第5章 結論與建議 79 5.1 結論 79 5.2 建議 80 參考文獻 81 附錄 92 | - |
| dc.language.iso | zh_TW | - |
| dc.title | 應用機器學習建立台灣本土土壤保水曲線轉換方程式：以中部農業土壤為例 | zh_TW |
| dc.title | Establish machine learning-based localized pedotransfer functions of soil water retention curves:a case study of agricultural soils in central Taiwan. | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 許健輝;廖國偉;胡明哲 | zh_TW |
| dc.contributor.oralexamcommittee | Chien-Hui Syu;Kuo-Wei Liao;Ming-Che Hu | en |
| dc.subject.keyword | 土壤轉換方程式,土壤保水曲線,排列重要性,不確定性分析,無母數自助法, | zh_TW |
| dc.subject.keyword | pedotransfer function,soil water retention curve,permutation importance,uncertainty analysis,nonparametric bootstrap, | en |
| dc.relation.page | 104 | - |
| dc.identifier.doi | 10.6342/NTU202301530 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2023-07-13 | - |
| dc.contributor.author-college | 生物資源暨農學院 | - |
| dc.contributor.author-dept | 生物環境系統工程學系 | - |
| dc.date.embargo-lift | 2025-07-12 | - |
| 顯示於系所單位： | 生物環境系統工程學系 | |
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