| DC 欄位 |
值 |
語言 |
| DC.contributor | 應用地質研究所 | zh_TW |
| DC.creator | 林彥享 | zh_TW |
| DC.creator | Yen-Hsiang Lin | en_US |
| dc.date.accessioned | 2003-7-18T07:39:07Z | |
| dc.date.available | 2003-7-18T07:39:07Z | |
| dc.date.issued | 2003 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=90624009 | |
| dc.contributor.department | 應用地質研究所 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 在許多不同的山崩潛感分析法中何者最優？目前尚無定論，且各有其優缺點。本研究嘗試利用倒傳遞類神經網路的自我學習功能，學習山崩的發生與各潛在因子及促崩因子間的相互關係，再利用其回想功能推估研究區各網眼的山崩潛感值及繪製地震誘發山崩之潛感分級圖。
研究區域選在台灣中部大里至國姓一帶，東西寬約23公里，南北長約30公里。崩塌地圈繪的方法，是利用本研究室先前以集集地震前後衛星影像完成的山崩圖層，及工研院能資所利用航空照片數化的崩塌地位置進行比對，並在災後五千分之一像片基本圖上進行確認，去除過小之山崩及不確定者。同時，為避免堆積區錯誤資訊影響類神經網路學習效果，也將山崩堆積判釋出來，並予以剔除。
本研究利用Erdas Imagine及MapInfo二套地理資訊系統進行空間資訊處理，輔以自行開發的程式萃取山崩潛感因子，並以二變量統計方法選取重要因子，作為類神經網路訓練的輸入層變數值。對於訓練區則按坡度及山崩情況分為三級：第一類為坡度小於10％的穩定區，第二類為未崩區，第三類為地震誘發山崩區，在分析系統中並同時引入模糊隸屬函數（fuzzy membership function）的觀念，將山崩分類模糊化，以作為類神經網路訓練的輸出層變數值。
分析結果顯示：（1）類神經網路隱藏層設定以一層隱藏層十八個神經元的正確率最佳。（2）類神經網路隱藏層設定為二層雖有較佳的誤差函數收斂值，但卻較一層隱藏層的正確率略差，有過度複雜化的現象。（3）類神經網路回想預測山崩之準確率最高達93.7％。（4）引用模糊隸屬函數觀念，藉由函數分佈換算山崩潛感值，可有效解決網路輸出中僅有破壞與否的結果。 | zh_TW |
| dc.description.abstract | There is no common agreement on the question, that is, which is the best method among various landslide susceptibility analyses, since they all have their own advantages and disadvantages. Our research tried to utilize the self-learning capability of back propagation neural network and to establish the relationship between landslides and the factors that potentially affecting landslides. Landslide susceptibility index of each cell of study area were then calculated by the recalling process of neural network to prepare the final landslide susceptibility map.
The study area of 23km in width and 30km in length is located in Central Taiwan and between the Da-Li town and the Kuo-Shing town. Landslide inventory has been mapped and digitized from SPOT satellite images prior and after the Chi-Chi earthquake. Landslide inventory based on aerial photos from ERL/ITRI has been used for comparison and validation. The 1/5,000 topographic maps published after the Chi-Chi earthquake were also used for validation. Landslide which is too small or questionable was removed during this process. Moreover, the deposit part at the toe of landslide which will confuse the neural network leaning has also being removed.
Erdas Imagine and Mapinfo were used for spatial data analysis in this study. We also developed some Fortran programs for extracting and processing the landslide susceptibility factors. Bivariate statistical method was used to select the relative important factors for the input layer of neural network. We classified our training data into three groups, the first group is the stable area which has slope less than 10%, the second group is the non-landslide pixels, and the third ones is the earthquake triggered landslide pixels. Concept of fuzzy logic was then adopted to design a membership function for each group and the membership function accepted as the output layer of neural network.
The results of this study are: (1) The combination of one-hidden-layer and 18 neuron have resulted in the highest accuracy rate. (2) Two-hidden-layer has better convergence in erron function than one-hidden-layer, but it is worse in accuracy rate than one one-hidden-layer. This implies that the two-hidden-layer setting may have over-complicated the problem. (3) The best accuracy rate for predicting the landslide using the neural network is 93.7%. (4) Landside susceptibility value can be calculated from fuzzy membership function, and then be used to construct a map. | en_US |
| DC.subject | 山崩潛感分析 | zh_TW |
| DC.subject | 類神經網路 | zh_TW |
| DC.subject | neural network | en_US |
| DC.subject | Landslide Susceptibility Analysis | en_US |
| DC.title | 運用類神經網路進行地震誘發山崩之潛感分析 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | Application of Neural Network to Landslide Susceptibility Analysis | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |