地球與環境科學

The cost of damage caused by hurricanes in 2017 is estimated to be over 200 billion dollars. Quick and accurate prediction of the path of a hurricane and its strength would be very valuable in alleviating these losses. Machine learning based prediction models, in contrast to models based on physics, have been developed successfully in many problem domains. A machine learning system infers the modeling function from a training dataset. This project developed machine learning based prediction models to forecast the path and strength of hurricanes in the North Atlantic basin. Feature analysis was performed on the HURDAT2 dataset, which contains paths and strengths of past hurricanes. Artificial Neural Networks (ANNs) and Generalized Linear Model (GLM) approaches such as Tikhonov regularization were investigated to develop nine hurricane prediction models. Prediction accuracy of these models was compared using a testing dataset, disjoint from the training dataset. The coefficient of determination and the mean squared error were used as performance metrics. Post-processing metrics, such as geodesic error in path prediction and the mean wind speed error, were also used to compare different models. TLS linear regression model performed the best of out the nine models for one and two time steps, while the ANNs made more accurate predictions for longer periods. All models predicted location and strength with greater than .95 coefficient of determination for up to two days. My models predicted hurricane path in under a second with accuracy comparable to that of current models.

台灣西南平原的測站紀錄中時常會出現明顯的短周期表面波，此類型表面波常常是因為震波在沖積層共振而產生。本研究利用表面波的低頻、長持續時間的特徵，量化分析2016年美濃地震、2010年甲仙地震與2012年霧台地震中發育的短週期表面波，並與相關圖資套疊，找出與場址特性的關聯。 本研究分析後發現，此三地震皆在西南平原發育出表面波，又以台南七股地區發育得最完整，討論後認為這與此區的地質構造與沖積層厚度有關。量化分析中，時頻圖與頻譜得到的主頻由東到西遞減，與表面波發育情形有近似的分布，持續時間則不明顯，但持續時間的分析因為測站資料不足而有困難，這部分仍待解決。而套疊圖資的結果中，沖積層等深線圖較能符合量化分析的趨勢，土壤液化潛勢圖則否，經確認後發現這與牽涉表面波的地層深度有關。根據本次研究強地動的結果，是能運用在快速掃描沉積地層，較各站鑽探快速且普及。

突堤效應是指人工建構物突出於海岸，延伸而出，阻擋原先海流和海岸積沙的路徑，造成沙子在上游堆積，而下游原先有沙岸的地區則因為沙子量逐漸減少，侵蝕大於堆積，逐漸出現海岸遭受破壞。突堤效應會造成堤前堆積、堤後侵蝕的情況。因為突堤效應造成的結果，頭城濱海森林公園（原頭城海水浴場），在這十多年中，原本的沙灘現在已完全被海水覆蓋。 本校位於頭城濱海森林公園附近，看到頭城濱海森林公園的海沙在烏石港建後己經流失不見，藉由本實驗瞭解突堤效應確實存在，壩堤長短、角度和洋流速度皆會影響壩堤前後堆積與侵蝕的速度，在實驗後我們對頭城烏石港所造成的突堤效應提出三大改善方案：一、縮短烏石港壩堤的長度100公尺；二、在原壩堤北側加蓋105度的壩堤；三、綜合後的方案會是減緩突堤效應的最佳策略。

台灣位於太平洋地震帶，80%強震發生於此處，且台灣老舊建築為數甚多，因此震後結構物之健康診斷相當重要。 本研究為解決目視監測之侷限與實驗模態分析面臨邊界與縮尺效應等問題，基於操作模態分析，發展一套快速、方便與準確之結構物健康診斷系統。 使用地震儀量測結構物之速度訊號，透過區域基線修正與極端值修剪等訊號前處理，再利用Lo et al （2015）提出之Multiscale Symbolic Entropy（MSSE）計算不同尺度之熵值，以建立結構物於健康狀態下之模型，地震發生後以相同之方式量測、分析多尺度熵趨勢，並比對地震前後之模型是否改變，藉以判斷結構物是否受損。 經過十九次於兩座橋、五個測站之測量，再經過數據前處理後，利用MSSE計算多尺度下之熵，而多尺度熵之趨勢有高度之一致性，證明此方法適合用於結構物健康診斷。 應用層面來說，此方法不需耗費長時間來完成，只需要幾次之測量來確定其數據之一致性，並於地震後再進行另外幾次的測量，此方法即可判斷該結構物是否受到地震之影響。

Wildfires have become increasingly frequent and severe due to global climatic change, demanding improved methodologies for wildfire modeling. Traditionally, wildfire severities are assessed through post-event, in-situ measurements. However, developing a reliable wildfire susceptibility model has been difficult due to failures in accounting for the dynamic components of wildfires (e.g. excessive winds). This study examined the feasibility of employing satellite observation technology in conjunction with artificial neural networking to devise a wildfire susceptibility modeling technique for two regions in California. Timeframes of investigation were July 16 to August 24, 2017, and June 25 to December 8, 2017, for the Detwiler and Salmon August Complex wildfires, respectively. NASA’s MODIS imagery was utilized to compute NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), land surface temperature, net evapotranspiration, and elevation values. Neural network and linear regression modeling were then conducted between these variables and ∆NBR (Normalized Burn Ratio), a measure of wildfire burn severity. The neural network model generated from the Detwiler wildfire region was subsequently applied to the Salmon August Complex wildfire. Results suggest that a significant degree of variability in ∆NBR can be attributed to variation in the tested environmental factors. Neural networking also proved to be significantly superior in modeling accuracy as compared to the linear regression. Furthermore, the neural network model generated from the Detwiler data predicted ∆NBR for the Salmon August Complex with high accuracy, suggesting that if fires share similar environmental conditions, one fire’s model can be applied to others without the need for localized training.

在本研究中，我們在正壓模式裡植入渦旋，當作實驗中的颱風眼，以不同的渦旋結構類型、渦度環及渦度比值進行實驗並觀察其變化。實驗結果顯示當渦旋結構的徑向渦度梯度變率存在正負的變化時，有機會導致正壓不穩定的發生，促進擾動而使渦旋形成多邊形結構。當渦旋內部的渦度梯度越大，則多邊形的形成越快但邊數越少；若渦旋外圈的強渦度區越薄，多邊形的形成越快且邊數越多。多邊形渦旋結構的演化則有可能發生「邊數合併」或「混合內縮」的情形，最終達到穩定或均質化的狀態。2017年泰利颱風行經台灣東北方海域時，我們從衛星雲圖中可觀察到颱風眼輪廓有多邊形變化，本文闡述此現象變化的歷程與可能的機制，進而希望在未來能利用此特點在颱風預報之領域中做出貢獻。

本研究主要探討颱風強度對於海洋表層葉綠素a的濃度影響的程度。 颱風資料由Joint Typhoon Warning Center資料庫中選取2008~2012年之西北太平洋颱風，共104個颱風。每天中相隔12小時取兩組資料，包含颱風經緯度、近中心最大風速與氣壓；葉綠素a濃度資料則取自NASA Ocean Color資料庫，對應颱風路徑中每天兩個取樣時間，找出以颱風為中心，長寬各150km範圍內的葉綠素a濃度，並利用Matlab程式輔助處理大量數據。 為清楚了解颱風對海表面葉綠素a濃度的影響，研究中將影響時間分為短期及長期兩組分別進行分析。所謂「短期」是指颱風經過前、後一天內，而「長期」則是颱風經過的前一周到後兩周內的時間。 分析結果顯示颱風過後一天海表面葉綠素a濃度平均增加近50%，且颱風近中心最大風速與中心氣壓在短期對於葉綠素a濃度分別呈現正相關與負相關；而長期來看，無論颱風近中心最大風速或是中心氣壓和葉綠素a濃度的關係較不明顯，但是颱風過後10天內，海表面葉綠素a濃度明顯高於颱風來臨前。

共伴效應發生的颱風時常帶來嚴重災情，促使我們投入對秋颱共伴效應的分析。利用地面風場、輻合輻散場、衛星雲圖、測站氣壓雨量等統計資料以及颱風路徑等資料分析後，顯示在中央氣象局分類的五號、六號、九號路徑的颱風，發生共伴效應的比例較其他路經之颱風高。在風場與輻合輻散場之比較得知共伴會隨著颱風環流與東北季風相對位置的接近而產生增強的現象，再用地面天氣圖與雨量統計資料確認高壓靠海導致東北季風含水氣量的增加也會加強共伴雲帶降水。利用數十個颱風與六個測站雨量與風向的變化結果相互比較後，得知地形風向效應也會導致當地瞬時及總降雨量增減，而風向與地形走向之夾角越趨近於垂直影響雨量的效果則越顯著，而颱風路徑與共伴地形效應也有極大關聯性。最後利用高空比濕場驗證垂直上空氣舉升效應，確認風向輻合確實會造成氣塊舉升。

My thesis focuses on studying Cretaceous microfossil specimens from the excavation of former brickworks in Štíty, especially foraminifera. In the theoretical part, I have covered the structure of the Bohemian Cretaceous Basin area, especially Bystřice Lithofacial Development. I have also processed previous paleontological researches from the locality. Emphasis was placed on field research and subsequently on laboratory research of the site. I have examined the present state of the location and gathered samples of silt clay containing a wide variety of fossils. I have acquired the microfossils, determined them, and ordered them systematically. The most important part of the thesis is the systematic and palaeoecological processing of the collection of microfossils from the locality. The thesis continues the research of the last year of SOČ, where I have gathered a collection of fossil macrofauna, flora, and ichnofauna. My collection is supplemented mainly by benthic and planktonic foraminifers. I have confirmed that the specimens found are typical representatives of marine fauna belonging to the Upper Cretaceous Coniacian. The paleoecological characteristics of the locality correspond to a nutrient-rich shallow-water environment, occasionally disturbed by storm waves.

本研究分析北極震盪指數(AOI)、北大西洋震盪指數(NAOI)、北太平洋震盪指數(NPOI)、南方震盪指數(SOI)、與南極震盪指數(AAOI)相關性，並與海溫距平值進行相關係數計算，進而推求區域洋流與大氣指數間的連動性。 研究發現各指數間具一定程度關聯，如AOI與NAOI指數全年12個月均達中度相關，可能與指數計算方式有關。海溫距平與指數相關性探討，各指數可影響區域海溫，有些呈現大規模面積變化，且連續數月，甚至影響跨越半球。部分大氣指數與海溫距平值相關性具有季節差異，如反聖嬰時太平洋西岸(黑潮區域)冬季較正常低溫、夏季較正常高溫。祕魯涼流則受SOI影響主要在秋、冬季(9、10、11、12、1月)，反聖嬰時祕魯涼流有增強趨勢，兩項推論均符合聖嬰現象理論。本研究目前僅用統計方式，呈現指數與海溫、海流關係，但指數與海溫間是否相關，還需要更多學理驗證。