Preliminary Study of Rainfall Induced Deep-seated Landslide Reaction Using Artificial Neural Network
| Autor: | CHOU,HSIN-CHEN, 周昕成 |
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| Rok vydání: | 2019 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 107 The geological conditions in the mountain areas of Taiwan are generally bad. Every year when typhoon strikes, it will lead to a wide-range debris flow. In recent years, the frequency of extreme weather has increased. Which caused the deep-seated landslide that rarely occurred in the past come to the fore. Since the Siaolin Village incident, such issues have gotten more attention by the society, relevant disaster prevention technique and early-warning research have been accelerated to reduce the loss of resident's lives and properties. This study uses the northern slope of Lushan, which located in Nantou County as the research area to analyze the monitoring data. First, organizing various monitoring instruments data into discrete rain field form, and find the groundwater level fluctuation also the ground displacements corresponding to the rain field. Secondly build a three-layer back propagation neural network structure by trial and error, which have cumulative rainfall, maximum rainfall, and rainfall duration as input values and groundwater level fluctuation or ground displacement as output value. Under the consideration of the neural network verification problem caused by the small sample size, besides the commonly used root mean square error, the ‘prediction interval’ is used as an auxiliary method to reduce the effect of randomly selected training samples to maintain the general interpretation ability of neural network models. Finally, by comparing the results of the neural network with the traditional linear regression method, we can discover that the neural network can obtain more accurate prediction than the linear regression in terms of the slope stability problem, and it could have a better reliability in the application of the early-warning system. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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