- 學位論文
以ADABOOST和RST混合式模型於土石流災害預測
Using ADABOOST and Rough Set Theory for Predicting Debris Flow Disaster
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摘要
台灣因其地理環境特殊，每逢颱風挾帶豪雨侵台抑或是地震後的降雨皆直接或間接地造成土石流的發生並嚴重危害到人類的生命財產安全。因此，土石流議題已受到各領域學者的關注並嘗試使用各領域專業技術分析其導致爆發的原因，例如土木工程、環境科學等。本研究採用文獻中南投縣陳有蘭溪流域的土石流數據集並提出ADARST模型進行其土石流的分析與探討，另外，於分類預測階段亦使用SVM-SA模型比較其分類性能並且於各階段使用統計分析方法進行比較與驗證實驗結果。實驗結果顯示本研究提出的ADARST土石流分析與預測模型不僅優於SVM-SA模型與統計分析方法亦優於文獻的實驗結果，其主要原因為ADABOOST可以使用最少的記憶體空間卻獲得強大的分類結果，而約略集合理論則是能夠處理不確定和模糊的資訊並生成淺顯易懂的規則。此外，ADARST模型提供的規則包含正向與反向推導方式可以提供給決策者相關建議，因此，本研究建議ADARST模型於土石流分析是一個有效的替代方法。
並列摘要
Debris flow resulting from typhoons, heavy rainfall, tsunamis or other natural disasters is a matter of particular importance to Taiwan owing to the country’s unique geographical environment and exacerbated by poor slope management and global warming. With regard to these types of natural occurrences, recent global events have attracted the attention of experts in various fields, such as civil engineering, environmental engineering and information management. These experts have developed several techniques to study the various factors of debris flow. The ADABOOST and rough set theory (RST) are two emerging methods with regard to classification and rule provision. The ADABOOST, an adaptive boosting machine learning algorithm, uses very little memory during computation and can obtain robust classification results. RST is able to deal with uncertainties and vague information in generating rules for decision makers. Thus, this study develops an ADARST model which uses the unique strengths of the ADABOOST and RST in classification and rule generation and applies the proposed ADARST to analyze debris flow. In addition, compared the classification performance with the SVM-SA model in the classification stages and used statistical analysis method compared and verified the each stage of the experimental results. Specifically, data from previous studies were obtained and used for the purposes of this study. Experimental results have shown that the proposed ADARST model is able to generate better results than those in previous investigations in terms and statistical analysis methods of prediction accuracy. In addition, the designed ADARST model can provide rules including forward and backward reasoning ways for decision makers. Therefore, the proposed ADARST model is shown to be an effective methodology with which to analyze debris flow.
參考文獻
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