本論文利用中央氣象局的公開資料，同時整合測站降雨資訊及與降雨
趨勢息息相關的衛星雲圖和雷達回波圖資料，進行高解析度之降雨機率空
間預測。分析方法採用boosting 來篩選合適的時空資訊，並利用羅吉斯迴
歸建構空間降雨機率預測模型，最終建立台灣地區的降雨機率預測平面。
此外，將會利用ROC 曲線探討此模型對於未來6 小時的預測能力，結果
顯示，對於不同步數的預測，其模型選取的變數存在一些潛在的趨勢，在
降雨與否的預測上，引入boosting 後，模型複雜度節省80%、運算時間節
省88%，而預測能力不減反增，平均而言，AUC 約能提升0.04。最終，三
步以內的預測模型，AUC 可達到0.7 以上，擁有不錯的預測能力，而四步
以上的預測模型，預測能力則不佳。

Making use of the open data from the Central Weather Bureau in Taiwan, this
thesis develops a statistical model for multi-step rainfall probability forecasts. The
data considered include the rainfall gauge data at the monitoring sites, the satellite
cloud image and the radar reflectivity images around the Taiwan area, which
are naturally informative to the rainfall tendency. The data are further integrated
according to various spatial and temporal resolutions and summarized into different
statistic measures. Via a boosting technique, most effective spatial-temporal
summaries with predictive abilities (possibly with nonlinear effect) are explored
in a logistic regression framework. Accordingly, the spatial forecasting map for
rainfall probability can be generated. The proposed methodology is implemented
to the hourly data collected from May 19 to May 31 in 2015. The empirical result
shows that the proposed prediction model with integrated spatial and temporal
variables provides reasonable good multi-step rainfall probability forecasts for 3
hours in advance (with AUC greater than 0.7). In particular, the complexity of
selected model is reduced to 20% in total variables and saves about 88% of computational
time after introducing the boosting scheme in the modeling procedure,
while the reduced model remains a similar forecasting ability evaluated by AUC.

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