Development of Models for Forecasting Debris Flows

• Main Authors: Tzu-ChiehLin, 林子介
• Other Authors: Nang-Fei Pan
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/27234316391028634297

碩士 === 國立成功大學 === 土木工程學系碩博士班 === 98 === Due to abnormal climate changes and many man-made ecological damages, mudflows and landslides have been caused by cloudburst in Taiwan since recent years. For example, Typhoon Morakot hit southern Taiwan last year. It caused 600 casualties and nearly 20 billion NT dollars, especially Jiasian Township, which was the most seriously hit by mudflows and landslides. For the prevention of recurrence of similar disasters, we should investigate the real cause of mudflows and landslides by the survey of 88 flood disasters to analyze the influence on our environment. We also need to establish a more reliable and accurate prediction models to provide information for related organizations or applications. Using forecast and warning measures not only decreases the harm caused by mudflows and landslides but also reduces economic losses. Mudflows and landslides monitoring system can be controlled to dominate any potential mudflows and landslides, flow direction and size anytime. We can also operate early warning system of mudflows and landslides in coordination to inform residents in dangerous regions. Besides, the early warning system can adopt appropriate response or evacuation measures to reduce mudflows and landslides. It can enhance the reliability of mudflows and landslides monitoring system by accurate prediction on the occurrence of mudflows and landslides. It often depends on the assessment of expert because mudflows and landslides and its impact factor measurement errors exist. The assessment is usually a semantic ambiguity such as the potential of mudflows and landslides. Traditional regression analysis and other methods cannot effectively deal with such vague information. Fuzzy regression analysis has been considered to overcome this type of fuzzy data. Therefore, this paper uses the method to predict mudflows and landslides and verifies the occurrence of mudflows and landslides by historical data. This paper collects several factors which may result in mudflows and landslides and considers rainfall and physiographic conditions on the impact of mudflows and landslides. We also use fuzzy linear regression model to establish a prediction model of potential mudflows and landslides. Then, we use 2008-2009 mudflows and landslides data in Kaohsiung and the entire southern region data to confirm the combined influence of all factors. We finally make a comprehensive assessment to predict the occurrence of mudflows and landslides. Thus, we controlled important variables influenced on the system to provide the decision-making basis of issuing warning system of mudflows and landslides and proper evacuation time for related organizations.