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系統識別號 U0026-1108201617513100
論文名稱(中文) 應用分量迴歸與自迴歸整合移動平均模式於台灣地區氣象乾旱預測
論文名稱(英文) Predicting meteorological droughts in Taiwan using quantile regression and autoregressive integrated moving average models
校院名稱 成功大學
系所名稱(中) 水利及海洋工程學系
系所名稱(英) Department of Hydraulics & Ocean Engineering
學年度 104
學期 2
出版年 105
研究生(中文) 黃詩婷
研究生(英文) Shih-Ting Huang
學號 N86034097
學位類別 碩士
語文別 中文
論文頁數 135頁
口試委員 指導教授-蕭政宗
口試委員-張麗秋
口試委員-孫建平
中文關鍵字 乾旱預測  標準化降雨指數  自迴歸整合移動平均模式  分量迴歸 
英文關鍵字 Drought prediction  Standardized precipitation index  Autoregressive integrated moving average model  quantile regression 
學科別分類
中文摘要 乾旱為研究全球氣候變遷上極為重要的一環,近年來台灣地區乾旱發生事件和持續時間有逐漸增加趨勢,而台灣降雨時空分佈不均,各個區域乾旱發生原因及旱象解除因素較為複雜,因此在面臨穩定水源供應不足和缺乏調蓄水資源空問題,建立乾旱預測模式以事先預警降低乾旱風險,可提供水資源綜合管理的有效依據。
本研究使用標準化降雨指數(standardized precipitation index, SPI)定義氣象乾旱,以自迴歸整合移動平均(autoregressive integrated moving average, ARIMA)和分量迴歸(quantile regression)模式進行預測。本研究選用台灣地區北、中、南、東四區域各一個雨量站1947至2014年之日雨量紀錄,計算不同時間尺度(3、6、12個月)的月累積雨量資料,將之轉換為標準化降雨指數SPI-3、SPI-6和SPI-12後,使用1947至1999年建立預測模式,預測前置時間1至6個月之2000至2014年乾旱資料並與實際乾旱值比較。研究結果顯示,預測模式以大時間尺度較小時間尺度準確,在ARIMA與分量迴歸模式在預測SPI乾旱均方誤差(MSE)評估以台東測站最佳,而分量迴歸模式相對於ARIMA模式在預測較長之時間尺度大致上表現較佳,尤其是在SPI乾旱均方誤差(MSE)預測上。
英文摘要 Drought is an important issue in global climate change study. In recent years, drought frequency and duration has been increased gradually in Taiwan. Since uneven temporal and spatial distribution of rainfall, stable water supply is heavily fluctuating streamflow. Therefore, establishing drought forecast in order to reduce water-deficit risk is an effective and useful approach in water resources management. The standardized precipitation index (SPI) is used to define meteorological drought in this study. Drought prediction models are constructed by the autoregressive integrated moving average (ARIMA) model and quantile regression model. A total of 4 rainfall gauge stations located in northern, central, southern, and eastern Taiwan are selected in this study. Daily rainfall records of the 1947-2014 period are used to construct SPI series various time-scales (3, 6, and 12 months). The results show that longer time-scale ARIMA and quantile regression models have lower prediction error than the short time-scale models. In terms of mean square error, Taitung station has less errors in both ARIMA and quantile regression models. Quantile regression model generally outperforms the ARIMA model for the longer time-scale predictions.
論文目次 摘要 I
Extended Abstract II
謝誌 VII
目錄 VIII
表目錄 X
圖目錄 XII
第一章 緒論 1
1-1 研究動機 1
1-2 研究目的 3
1-3 文獻回顧 3
1-4 論文架構 6
第二章 研究方法 8
2-1 標準化降雨指數 8
2-2 自迴歸整合移動平均模式 12
2-3 分量迴歸 15
第三章 研究區域與資料 19
3-1 研究區域概述 19
3-2 雨量測站資料概述 20
3-3 乾旱資料概述 22
第四章 結果與討論 28
4-1 自迴歸移動平均模式分析 28
4-1-1 SPI-3 ARIMA模式預測結果分析 30
4-1-2 SPI-6 ARIMA模式預測結果分析 33
4-1-3 SPI-12 ARIMA模式預測結果分析 36
4-1-4 ARIMA模式SPI-3、SPI-6和SPI-12結果比較 39
4-2 分量迴歸模式結果分析 40
4-2-1 SPI-3 分量迴歸模式預測結果分析 45
4-2-2 SPI-6分量迴歸模式預測結果分析 48
4-2-3 SPI-12分量迴歸模式預測結果分析 52
4-2-4 分量迴歸最適模式SPI-3、SPI-6和SPI-12結果比較 55
4-3 自迴歸整合移動平均和分量迴歸模式預測結果討論 56
第五章 結論與建議 60
5-1 結論 60
5-2 建議 61
參考文獻 62
附錄A 各測站1947至1999年在各月份3、6和12個月累積雨量伽瑪分佈參數表 66
附錄B 台北測站1947至1999年各月份3、6和12個月累積雨量分佈與伽瑪分佈比較圖 69
附錄C 各測站1947至1999年在各月份3、6和12個月累積雨量K-S 檢定表 72
附錄D 台北測站1947至2014年各月份3、6和12個月累積雨量分佈與伽瑪分佈比較圖 75
附錄E 各測站1947至2014年在各月份3、6和12個月累積雨量K-S 檢定表 78
附錄F 各測站1947至1999年SPI-3、SPI-6和SPI-12在ARIMA模式擬合時序列圖 81
附錄G 各測站SPI-3、SPI-6和SPI-12 ARIMA模式2000至2014年前置時間1置6個月預測值與觀測值比較圖 85
附錄H 各測站2000至2014年SPI-3、SPI-6和SPI-12在分量迴歸最適模式預測前置時間1置6個月時序列圖 103
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