研究生: |
黃詩婷 Huang, Shih-Ting |
---|---|
論文名稱: |
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 Predicting meteorological droughts in Taiwan using quantile regression and autoregressive integrated moving average models |
指導教授: |
蕭政宗
Shiau, Jenq-Tzong |
學位類別: |
碩士 Master |
系所名稱: |
工學院 - 水利及海洋工程學系 Department of Hydraulic & Ocean Engineering |
論文出版年: | 2016 |
畢業學年度: | 104 |
語文別: | 中文 |
論文頁數: | 135 |
中文關鍵詞: | 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 |
外文關鍵詞: | Drought prediction, Standardized precipitation index, Autoregressive integrated moving average model, quantile regression |
相關次數: | 點閱:95 下載:8 |
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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。
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.
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