本文選題：人工神經(jīng)網(wǎng)絡(luò) + 地質(zhì)災(zāi)害危險(xiǎn)性評(píng)價(jià)��； 參考：《成都理工大學(xué)》2017年碩士論文

【摘要】：臥龍自然保護(hù)區(qū)是我國(guó)國(guó)家級(jí)第三大自然保護(hù)區(qū),保護(hù)區(qū)內(nèi)珍稀動(dòng)植物豐富,特別是我國(guó)特有珍稀動(dòng)物大熊貓。臥龍自然保護(hù)區(qū)位于龍門(mén)山中南段,是四川盆地向川西高原的過(guò)渡帶。2008年汶川地震和2013年蘆山地震對(duì)臥龍自然保護(hù)區(qū)造成嚴(yán)重破壞。臥龍自然保護(hù)區(qū)內(nèi)地質(zhì)災(zāi)害頻發(fā),且對(duì)人民生命財(cái)產(chǎn)安全以及動(dòng)植物生態(tài)環(huán)境造成嚴(yán)重威脅。所以,對(duì)臥龍自然保護(hù)區(qū)地質(zhì)災(zāi)害進(jìn)行危險(xiǎn)性評(píng)價(jià),對(duì)保護(hù)區(qū)內(nèi)展開(kāi)地質(zhì)災(zāi)害防治工作具有重要意義。本文依托中國(guó)科學(xué)院數(shù)字地球重點(diǎn)實(shí)驗(yàn)室開(kāi)放基金項(xiàng)目“以遙感為主要手段的臥龍大熊貓自然保護(hù)區(qū)自然災(zāi)害與遺產(chǎn)地生境評(píng)價(jià)”,以及成都理工大學(xué)空間信息技術(shù)研究所數(shù)字地球技術(shù)平臺(tái)進(jìn)行課題研究。本文通過(guò)對(duì)研究區(qū)域Landsat8遙感圖像進(jìn)行數(shù)字圖像處理,完成臥龍自然保護(hù)區(qū)地質(zhì)災(zāi)害解譯后提取有效地質(zhì)災(zāi)害信息。并對(duì)基礎(chǔ)地理信息進(jìn)行數(shù)字化,建立臥龍自然保護(hù)區(qū)空間信息數(shù)據(jù)庫(kù)。根據(jù)研究區(qū)域特點(diǎn)本文選取海拔高度、地形坡度、地形坡向、地層巖性、河流水系、人類工程活動(dòng)作為臥龍自然保護(hù)區(qū)地質(zhì)災(zāi)害危險(xiǎn)性評(píng)價(jià)指標(biāo)。并基于人工神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)建立臥龍自然保護(hù)區(qū)地質(zhì)災(zāi)害危險(xiǎn)性評(píng)價(jià)模型,對(duì)臥龍自然保護(hù)區(qū)地質(zhì)災(zāi)害進(jìn)行危險(xiǎn)性評(píng)價(jià),得到臥龍自然保護(hù)區(qū)地質(zhì)災(zāi)害危險(xiǎn)性區(qū)劃圖。本文針對(duì)標(biāo)準(zhǔn)BP神經(jīng)網(wǎng)絡(luò)存在的缺陷,以臥龍自然保護(hù)區(qū)為例,建立基于附加動(dòng)量改進(jìn)算法的地質(zhì)災(zāi)害危險(xiǎn)性評(píng)價(jià)模型、基于自適應(yīng)調(diào)整參數(shù)改進(jìn)算法的地質(zhì)災(zāi)害危險(xiǎn)性評(píng)價(jià)模型、基于共軛梯度改進(jìn)算法的地質(zhì)災(zāi)害危險(xiǎn)性評(píng)價(jià)模型、基于Levenberg-Marquardt改進(jìn)算法的地質(zhì)災(zāi)害危險(xiǎn)性評(píng)價(jià)模型。對(duì)比地質(zhì)災(zāi)害危險(xiǎn)性評(píng)價(jià)結(jié)果發(fā)現(xiàn),四種模型均訓(xùn)練穩(wěn)定且達(dá)到一定精度,基于四種模型下的評(píng)價(jià)結(jié)果相似性較強(qiáng),危險(xiǎn)性分區(qū)結(jié)果大致相似。但同時(shí)在訓(xùn)練時(shí)間、迭代次數(shù)、危險(xiǎn)性區(qū)域面積及分布存在細(xì)微區(qū)別。本文提出對(duì)四種基于BP改進(jìn)算法的地質(zhì)災(zāi)害危險(xiǎn)性評(píng)價(jià)模型下的評(píng)價(jià)結(jié)果進(jìn)行綜合解釋,得到臥龍自然保護(hù)區(qū)地質(zhì)災(zāi)害危險(xiǎn)性評(píng)價(jià)的綜合評(píng)價(jià)結(jié)果。該評(píng)價(jià)結(jié)果顯示與基于四種改進(jìn)算法模型下的評(píng)價(jià)結(jié)果相關(guān)性較好,且評(píng)價(jià)結(jié)果適用性較好。且基于共軛梯度改進(jìn)算法的神經(jīng)網(wǎng)絡(luò)模型在臥龍自然保護(hù)區(qū)地質(zhì)災(zāi)害危險(xiǎn)性評(píng)價(jià)中較為適用。為臥龍地質(zhì)災(zāi)害防治工作的規(guī)劃、部署提供理論支撐。
[Abstract]:Wolong nature reserve is the third Nature Reserve in China. The rare animals and plants are rich in the protected area, especially the rare animal pandas in China. The Wolong nature reserve is located in the southern section of the Longmen mountain. It is the Wenchuan earthquake of the Sichuan basin to the West Sichuan Plateau in.2008 and the Lushan earthquake in 2013 to the Wolong nature reserve. The geological disasters in Wolong natural reserve are frequent, and it has serious threat to the safety of people's life and property and the ecological environment of animals and plants. Therefore, it is of great significance to evaluate the geological hazards in the Wolong nature reserve and to prevent and control the geological disasters in the protected areas. The open fund project of the Key Laboratory of the earth is "the assessment of natural disasters and heritage sites in Wolong Giant Panda Nature Reserve, which is the main means of remote sensing", and the digital earth technology platform of the Institute of space information technology of Chengdu University of Technology. This paper carries out digital images of the Landsat8 remote sensing image of the research area. After the interpretation of geological disaster in Wolong nature reserve, the effective geological disaster information is extracted. The basic geographic information is digitized and the spatial information database of the Wolong nature reserve is set up. According to the characteristics of the study area, the altitude, terrain slope, topographic slope, stratigraphic lithology, river system and human engineering activities are selected. It is the risk assessment index of geological hazard in Wolong nature reserve. Based on the artificial neural network structure, the risk assessment model of geological hazard in Wolong nature reserve is established. The hazard assessment of geological disasters in Wolong nature reserve is evaluated, and the geological hazard zoning map of the Wolong natural reserve is obtained. This paper aims at the standard BP neural network. The existing defects of the collaterals, taking the Wolong nature reserve as an example, set up a geological hazard assessment model based on the additional momentum improvement algorithm, based on the adaptive adjustment parameter improvement of the geological hazard risk assessment model, based on the conjugate gradient improved algorithm of geological hazard risk assessment model, based on the Levenberg-Marquardt improvement. The result of the evaluation of geological hazard risk assessment shows that the four models are both trained and achieved a certain precision. The results of the evaluation results based on the four models are more similar, and the results of the hazard zoning are roughly similar, but at the same time, the training time, the number of iterations, the area and distribution of the danger area and the distribution exist. This paper gives a comprehensive interpretation of the evaluation results of four geological hazard assessment models based on the BP improved algorithm, and obtains the comprehensive evaluation results of the geological hazard assessment of the Wolong nature reserve. The results show that the results are well correlated with the evaluation results based on the four improved algorithm models, and the evaluation results are good. The applicability of the price results is better. And the neural network model based on the conjugate gradient improvement algorithm is more suitable for the evaluation of geological hazard in Wolong nature reserve. It provides theoretical support for the planning of Wolong geological disaster prevention and control work.

【學(xué)位授予單位】：成都理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：P694

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