本文選題：滑坡　切入點：危險性預(yù)測　出處：《江西理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：滑坡災(zāi)害是我國乃至世界范圍內(nèi)發(fā)生次數(shù)最多的地質(zhì)災(zāi)害之一,它不僅對財產(chǎn)、環(huán)境和資源等產(chǎn)生破壞性,而且會給人類生命安全帶來嚴重威脅。滑坡災(zāi)害危險性預(yù)測研究是一個復(fù)雜的多源信息綜合分析過程,其中所含信息具有突變性、非線性、隨機性和不確定性等特點。這些特點給滑坡危險性預(yù)測帶來相應(yīng)的困難。由于滑坡的頻繁發(fā)生,所造成的危害形勢也日趨嚴峻,因此尋求一種科學(xué)有效的方法來提高滑坡預(yù)測的準(zhǔn)確性是一個很有意義的研究課題。數(shù)據(jù)挖掘的相關(guān)理論方法有較強處理非線性關(guān)系的能力,例如通過聚類方法可將高度相似的數(shù)據(jù)對象歸為在同一類中,高度相異的數(shù)據(jù)對象分在不同類中,從而依據(jù)已知規(guī)則對未知事物進行預(yù)測。因此本文采用數(shù)據(jù)挖掘中Chameleon聚類算法和滑坡信息的有關(guān)特點相結(jié)合,構(gòu)建滑坡危險性預(yù)測模型,設(shè)計聚類子集危險性等級劃分方法,進而對實例研究區(qū)滑坡危險性等級進行預(yù)測劃分。研究發(fā)現(xiàn)滑坡災(zāi)害是由坡高、坡型、和降雨等多種因素共同作用所引發(fā)的,其中與滑坡發(fā)生機制有著密切關(guān)系的降雨量取值在一個不確定區(qū)間內(nèi),具有不確定屬性,致使傳統(tǒng)Chameleon聚類算法很難準(zhǔn)確對其進行定量刻畫及有效處理。而且傳統(tǒng)的Chameleon聚類算法也存在構(gòu)建k-最近鄰圖kG時k值的確定與相似度函數(shù)閾值的選取都需要人工進行和處理大規(guī)模數(shù)據(jù)集的局限性等問題。為了解決上述所存在的相關(guān)問題,本文基于前人所研究的M-chameleon聚類算法基礎(chǔ)上,提出一種新的兩階段聚類整合算法(DM-chameleon)適用于處理較大規(guī)模數(shù)據(jù)集;引入不確定數(shù)據(jù)模型,有效的利用不確定屬性的特征刻畫降雨量;并對聚類技術(shù)中表征相似性的歐氏距離進行拓廣,使其適用于不確定數(shù)據(jù)之間相似性的計算;最后根據(jù)上述理論提出一種不確定DM-chameleon聚類算法并把其應(yīng)用在滑坡危險性預(yù)測模型中,以延安寶塔區(qū)為實例進行驗證。首先在已知數(shù)據(jù)集上實驗得到DM-chameleon算法比M-chameleon算法獲得了較好的聚類效果,并且聚類速度有了明顯提高,其次對比實例研究結(jié)果表明不確定DM-chameleon聚類模型取得了較高的預(yù)測精度,進而驗證了不確定DM-chameleon聚類算法應(yīng)用在滑坡危險性預(yù)測中的可行性。
[Abstract]:Landslide disaster is one of the most frequent geological disasters in China and the world. It not only destroys property, environment and resources. The study of landslide hazard prediction is a complex process of comprehensive analysis of multi-source information, in which the information contained in it is abrupt and nonlinear. The characteristics of randomness and uncertainty bring the corresponding difficulties to the prediction of landslide risk. Because of the frequent occurrence of landslide, the harmful situation caused by landslide is becoming more and more serious. Therefore, to find a scientific and effective method to improve the accuracy of landslide prediction is a meaningful research topic. For example, by clustering, highly similar data objects can be classified into the same class, and highly different data objects can be divided into different classes. So this paper combines the Chameleon clustering algorithm in data mining with the characteristics of landslide information, constructs the landslide hazard prediction model, and designs the method of classifying the risk levels of clustering subsets. The landslide hazard is caused by various factors, such as slope height, slope type, rainfall and so on. The rainfall which is closely related to the occurrence mechanism of landslide is in an uncertain range and has uncertain properties. It is very difficult for traditional Chameleon clustering algorithm to accurately characterize and deal with it effectively. Moreover, the traditional Chameleon clustering algorithm also needs to determine the k value and select the threshold of similarity function when constructing k-nearest neighbor graph KG. Problems such as the limitations of large data sets are carried out and dealt with. In order to solve the related problems mentioned above, Based on the M-chameleon clustering algorithm, a new two-stage clustering integration algorithm (DM-chameleon) is proposed to deal with large scale data sets, and an uncertain data model is introduced to characterize rainfall effectively. The Euclidean distance which represents the similarity in clustering technology is extended to make it applicable to the calculation of similarity between uncertain data. Finally, according to the above theory, an uncertain DM-chameleon clustering algorithm is proposed and applied to the landslide hazard prediction model. Taking Baota area of Yan'an as an example, the experimental results show that DM-chameleon algorithm has better clustering effect than M-chameleon algorithm, and the clustering speed is improved obviously. Secondly, the results of the case study show that the uncertain DM-chameleon clustering model has achieved high prediction accuracy, and the feasibility of applying the uncertain DM-chameleon clustering algorithm to landslide hazard prediction is verified.
【學(xué)位授予單位】：江西理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：P642.22;TP311.13

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本文編號：1613404