碩士 / 國立清華大學 / 統計學研究所 / 104 / 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.
| Identifer | oai:union.ndltd.org:TW/104NTHU5337016 |
| Date | January 2016 |
| Creators | Chen, Sin Jhih, 陳信志 |
| Contributors | Hsu, Nan Jung, 徐南蓉 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 47 |
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