利用灰色理論進行礁溪溫泉區溫泉水位資料補遺之研究

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標題:	利用灰色理論進行礁溪溫泉區溫泉水位資料補遺之研究 Applied the gray theory for groundwater level supplement in Jiaosi hot spring area
作者:	康麗惠
貢獻者:	觀光事業管理系陳忠偉
關鍵字:	灰預測灰色理論資料補遺溫泉水位 Gray Theory Data Supplement Hot Spring Level Gray Prediction
日期:	2014
上傳時間:	2015-10-26 20:25:19 (UTC+8)
摘要:	台灣溫泉主管機關目前已逐年監測與紀錄溫泉水位資料，但由於溫泉泉溫與泉性特殊，常導致儀器紀錄資料不完整，為便於溫泉水位資料分析，本研究利用灰色理論進行溫泉水位之資料補遺。本研究首先利用溫泉水位資料完整部分，重複測試與訓練灰預測礁溪溫泉水位之最佳模式，待最佳推估模式確定後，針對無完整觀測溫泉水位資料部分進行補遺，以建置完整連續之溫泉水位資料。本研究以礁溪溫泉2010年溫泉水位資料，進行最佳模式推估，2009與2010年之溫泉水位資料進行補遺。由本研究推估結果顯示，相異多筆資料若都是預測值，預測偏差隨著時間增加而加大，原因為後項預測資料包含前項資料誤差，其所產生之偏差，為灰預測系統具有資料遞增或遞減趨勢所致。因此需搭配相異間距法以減少溫泉水位預測誤差，由測試結果顯示，溫泉公園站以相異12筆與相異間距7日為最佳灰預測模式；國小站以相異8筆與相異間距7日為最佳灰預測模式；太子站則以相異8筆與相異間距5日為最佳灰預測模式。本研究利用各溫泉監測站之最佳模式，分別完成溫泉公園站、國小站及太子站，2009年至2010年共4段遺漏溫泉水位資料，補遺成果對礁溪溫泉水位缺漏現象可趨於完整。 The administration of hot spring in Taiwan has collected and supervised the data of hot spring level. However, the data is usually collected incompletely due to the special temperature and characteristics of hot spring, which influenced the function of instruments. For the purpose of the data analysis of hot spring level, the gray theory was conducted in this study to supplement the data of hot spring level.First, the complete part data of hot spring level were tested and practiced to predict the best model with gray prediction, and then the fragmented part of data were accomplished by the best model, which were arranged to build the complete and continues data. In this study, the data of hot spring level in Jiaosi in 2010 was inspected, which supplemented the data during 2009~2010. The results showed that the predicted deviation would increase if the batches of data were predictions. It was caused by the error of posterior predictions would be contained by the anterior, and the trend of the deviation would increase or decrease progressively in the system of gray prediction. Thus it was needed to operate with the divergence distance method to decrease the error. The results of test exhibited that there's the best model with 12 batches of data and 7 days of divergence distance in Hot Spring Park station, 8 batches and 7 days in Guo-Xiao station and 8 batches and 5 days in Tai-Tzu station.This study used the best models in different administrations of hot spring in the study to supplement the data of Hot Spring Park station, Guo-Xiao station and Tai-Tzu station during 2009~2010 respectively, totally 4 batches of losing data. The fragmented data of hot spring level in Jiaosi will be supplemented by this study.
關聯:	網際網路公開：2014-01-15，學年度：102，98頁
显示于类别:	[觀光事業管理系(含溫泉所)] 博碩士論文

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