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研究生:徐韻如
研究生(外文):XU, YUN-RU
論文名稱:以機器學習理論建置氣候因子和天氣災害因子與潛在水稻損失預測模型
論文名稱(外文):Establishing the Potential Rice Loss Prediction Model of Climate and Nature Disaster Factors Based on Machine Learning Theory
指導教授:曾昭衡曾昭衡引用關係陳伶伶陳伶伶引用關係
指導教授(外文):TSENG, CHAO-HANGCHEN, LIN-LIN
口試委員:李堅明曾昭衡陳伶伶
口試委員(外文):LEE, CHIEN-MINGTSENG, CHAO-HANGCHEN, LIN-LIN
口試日期:2021-01-27
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:環境工程與管理研究所
學門:工程學門
學類:環境工程學類
論文種類:學術論文
論文出版年:2021
畢業學年度:109
語文別:中文
論文頁數:338
中文關鍵詞:氣候變遷人工智慧機械學習水稻損失社會科學統計軟體(SPSS)
外文關鍵詞:Climate changeArtificial Intelligence (AI)Mechanical Learning (ML)Crop LossStatistical Product and Service Solutions (SPSS)
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聯合國指出21世紀極端的氣候將會更頻繁與廣泛,氣候變遷所造成的災害已成為全人類的安全問題。近年人工智慧(AI)的興起及機器學習(ML)應用,在環境應用方面的影響也不遑多讓。在農產業水稻方面,雖有天氣預測支援農民進行相關防護措施,但突如其來的天災降臨時,造成的水稻損失是逐年增加。若能提早得知天災造成的水稻潛在損失,即能提早做好災前的應變,減少人民財產損失。
本研究旨在導入ML之概念,利用ML軟體,如:SPSS,做環境衝擊因數對水稻造成的災害預測及氣候模擬分析。將氣象因子及天然災害因子列為變數,再藉由獨立樣本T檢定及類神經網路進行變數篩選。利用機器學習理論基礎之決策樹進行模型訓練,並使用特徵曲線(ROC)圖及曲線下面積(AUC)衡量模型的準確度及預測價值,再利用IPCC RCP 4.5預測值進行長短期預測。
本研究所得決策樹結果共有四種模式,做為水稻潛在損失之長短期預測使用。在短期預測之結果方面單一月份預測成果和實際值差異過大,但單一事件的預測結果卻很精準,水害可使用模式一;寒害可使用模式三;病蟲害可使用模式二。而在長期預測分析結果共有兩部分:第一部分(2020)結果得知,預測值與實際值差異率為83%。相較於以觀測值進行水稻潛在損失金額的預測結果優,表示此模式適合以未來氣象預測值進行水稻潛在損失的預測。第二部份(2017-2019)結果得知,預測值與實際值差異率為68%。兩部分之結果相差15%,代表以一個時間區段進行水稻潛在損失的預測結果較佳。農委會或農糧署等相關單位應可根據不同目的(短期、長期預測)進行本論文模式一至四之選擇。短期(月預測)可依災害類別如:水害使用模式一、寒害使用模式三、病蟲害使用模式二;而長期(年預測)預測亦可使用模式一。
Intergovernmental Panel on Climate Change (IPCC) pointed out that the extreme climate in the 21st century will be more frequent and widespread, and disasters caused by climate change have become a common security problem for all mankind. In recent years, the rise of Artificial Intelligence (AI) and the application of Machine Learning (ML) have had an impact on environmental applications. In terms of crop, although the weather forecast provided by the Central Meather Bureau supports farmers in relevant protective measures, the loss of crop caused by sudden natural disasters has increased year by year. Therefore, if we can learn about the potential loss of crop caused by future weather or natural disasters in advance based on meteorological data and past natural disasters, we can make early response before disasters and reduce people's property losses.
This research aims to introduce the concept of ML and use ML software, such as SPSS, to do disaster prediction and climate simulation analysis on crop caused by environmental impact factors.
Meteorological factors and natural disaster factors were listed as variables, and then variables were screened by independent sample t-test and neural network respectively. Then, Chi-square Automatic Interaction Detector (CHAID) and Classification and Regression Tree (CART) are used to train the model, and Receiver Operating Characteristic curve (ROC) and Area Under the Curve of ROC (AUC) are used to measure the accuracy and predictive value of the model.
There are four decision tree models in this study, which can be used to predict the long-term and short-term potential loss of crop. In terms of short-term prediction results, there is a big difference between the single month prediction results and the actual value, but the single event prediction results are very accurate. Model 1 can be used for flooding injury; model 3 can be used for chilling injury; model 2 can be used for disease and pest. In the long-term forecast analysis, there are two parts: the first part (2020) shows that the difference rate between the predicted value and the actual value is 83%. The results show that the model is more suitable for the prediction of crop potential loss based on the future meteorological forecast. The second part (2017-2019) shows that the difference rate between the predicted value and the actual value is 68%. The difference between the results of the two parts is 15%, which means that it is better to predict the potential loss of crop in one time of period.
According to different purposes (short-term and long-term forecasting), the relevant units such as the Council of Agriculture or the Agriculture and Food Agency Council of Agriculture should choose one to four models of this paper. The short-term (monthly) forecast can be based on the types of disasters, such as: flooding injury use mode 1, chilling injury use mode 3, pest use mode 2; and the long-term (annual) forecast can also use mode 1.

摘要 i
ABSTRACT iii
致謝 vi
目錄 vii
表目錄 x
圖目錄 xiii
第一章 前言 1
1.1 研究緣起 1
1.2 研究目的 3
1.3 研究範疇 4
1.4 研究限制 6
1.5 研究價值 6
第二章 文獻回顧 8
2.1 工業 4.0 (Industry 4.0) 8
2.1.1 工業 4.0 發展現況 8
2.1.2 工業 4.0 應用於供應鏈系統 10
2.1.3 工業 4.0 與農業供應鏈系統 12
2.1.4 小結 14
2.2 人工智慧 (Artificial Intelligence 簡稱 AI) 15
2.2.1 人工智慧發展現況 16
2.2.2 人工智慧範疇與工具 19
2.2.3 人工智慧應用於供應鏈系統 32
2.2.4 人工智慧與農業供應鏈系統 55
2.2.5 小結 56
2.3 機器學習理論 59
2.3.1 機器學習領域 60
2.3.2 機器學習類型 62
2.3.3 監督式學習演算法 65
2.3.4 小結 75
2.4 農產業供應鏈系統 78
2.4.1 農業供應鏈系統界定與架構 78
2.4.2 氣候變遷之衝擊與風險 80
2.4.3 機器學習理論應用於農業供應鏈系統 83
2.4.4 小結 88
第三章 研究方法 89
3.1 研究範圍與架構 89
3.2 研究設計 92
3.2.1 資料採集 93
3.2.2 變數分類 108
3.2.3 機器學習 119
3.2.3 模型驗證 122
3.2.4 預測模式 124
3.3 機器學習 模型建構 126
3.3.1 資料預處理 126
3.3.2 模型訓練 128
3.3.3 模型評估 129
3.3.4 模型預測 130
第四章 研究結果與討論 131
4.1 歷史趨勢分析結果 131
4.1.1 氣象因子 131
4.1.2 天然災害因子 135
4.1.3 水稻 140
4.1.4 氣象因子和天然災 害因子與水稻之關係 144
4.2 重要變數篩選結果 166
4.2.1 類神經網路選取結果 166
4.2.2 獨立樣本 T 檢定選取結果 172
4.2.3 重要變數選取討論 176
4.2.4 小結 180
4.3 農業產業鏈調適政策 182
4.3.1 模型訓練與評估 182
4.3.2 長短期預測分析 191
4.3.3 調適政策與行動 205
4.3.4 小結 211
第五章 結論與建議 213
5.1 結論 213
5.1.1 機器學習導入農業產業鏈 2 13
5.1.2 模式研究成果 213
5.2 建議 215
5.2.1 理論建議 215
5.2.2 實質建議 216
5.2.3 實務建議 216
參考文獻 217
附件 247

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