Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/34675
標題: 以適應性網路模糊推論系統推估台灣氣候週期性之研究
Study on Climate Cycle in Taiwan by Adaptive Network-Based Fuzzy Inference System
作者: 曹鎮
Chao, Chen
關鍵字: Climate Changes
氣候變遷
Principal Component Analysis
Spectral Analysis
ANFIS
Kriging Grid
Evapotranspiration
主成份分析
頻譜分析
適應性網路模糊推論系統
克利金網格
蒸發散量
出版社: 水土保持學系所
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摘要: 近年來由於工商業持續發展,造成全球氣候相當程度的暖化,間接也影響了水文循環。為瞭解台灣之情形,本研究嘗試以主成份分析法,進行氣候因素間的簡化,再以頻譜分析法找出降雨量及氣溫兩因素之規則性,並進行模糊推論系統訓練學習,找出兩者與蒸發量間之模糊關聯。再利用克利金網格內插,使降雨量與氣溫由點資料轉為面資料,代回原先訓練學習之模糊資料庫,可得到區域性蒸發量值,最後配合土地利用圖層套疊,即可進行區域性蒸發散量之推估。另外,結合本研究導出之降雨量與氣溫規則函數,更可進一步達到蒸發量預估之效果。 本研究發現在十項氣候因素中,二氧化碳之排放量與氣溫;蒸發量與日照時數;雲量與相對溼度間均具有高相關性,且北部測站如台北、鞍部、竹子湖、基隆、宜蘭等站之年最大月降雨量均呈現三年,中南部之測站則多呈現三至四年之短週期,但台中與台南站卻呈現九年之長週期,而且降雨主週期相同之測站,其年最大月均溫亦有相同之變化趨勢。本研究將相同降雨週期之測站視為同一群組,以月降雨量與月均溫為輸入值,以月蒸發量為輸出值,進行模糊推論系統訓練學習,均有良好之訓練結果與誤差收斂。 本研究推導出鞍部、竹子湖測站之年最大月降雨量與年最大月均溫規則函數,並發現雨量具有復現性。另外,利用MATLAB程式撰寫模糊推論系統訓練學習資料庫,結合克利金空間網格分佈,套疊淡水河流域之農林土地利用圖層,可推估區域性蒸發散量,其結果可供水資源利用規劃之參考。
Recent years, the greenhouse effect due to industry and commerce prolonged development has influenced hydrological circulation. In order to realize the situation of Taiwan, this study intends to find the correlations through the principal component analysis, thus, the obvious correlations has found in CO2 emission and temperature; evaporation and sunshine duration; cloud amount and relative humidity. Furthermore, this study calculates the regularized major period in rainfall based on time-series spectral analysis. The maximum monthly rainfall shows approximately three-year major period in northern Taiwan, meteorological stations such as Taipei, Anbu, Jhuzihhu, Keelung and Yilan, four-year major period in central and southern Taiwan, but nine-year period happens in Taichung and Tainan station. There is a same temperature tendency with similar major rainfall period. This study applies adaptive network-based fuzzy inference system (ANFIS) employed by mapping meteorological data with calculated occurring period of rainfall, temperature and evaporation. The results after training show that the fuzzy inference system (FIS) is able to capture the nonlinear feature for evaporation. Moreover, the trained FIS is integrated into geographical statistical technology, Kriging and ArcGIS for the estimation of the regional evapotranspiration, forest and farmland in Dan-Shui Basin as an example. This research used MATLAB to develop ANFIS simulation, then it combined Kriging space griding to represent effectively space distributions of regional evapotranspiration. The estimated evapotranspiration could be gained by supporting ANFIS model based on the database of historical data functional rainfall and temperature in Anbu and Jhuzihhu stations. The useful information in this study can be referred for water resource planning.
URI: http://hdl.handle.net/11455/34675
其他識別: U0005-2001200917212500
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