發(fā)布時(shí)間：2018-04-12 16:43

  本文選題：智能算法 + 滑坡災(zāi)害��； 參考：《中國(guó)礦業(yè)大學(xué)(北京)》2016年博士論文

【摘要】：滑坡災(zāi)害是一種常見的地質(zhì)災(zāi)害,它的發(fā)生,不僅給人類的生命、財(cái)產(chǎn)安全帶來(lái)嚴(yán)重威脅,同時(shí)給資源、環(huán)境、生態(tài)等各個(gè)方面帶來(lái)了巨大破壞。我國(guó)是世界上滑坡災(zāi)害最為嚴(yán)重的國(guó)家之一,據(jù)中國(guó)地質(zhì)環(huán)境信息網(wǎng)發(fā)布的中國(guó)地質(zhì)災(zāi)害通報(bào)顯示:2006至2015年10年間全國(guó)共發(fā)生地質(zhì)災(zāi)害260353起,其中滑坡發(fā)生的比例居于地質(zhì)災(zāi)害首位,占地質(zhì)災(zāi)害總數(shù)的73.79%,這10年期間由滑坡造成的傷亡、失蹤人數(shù)共計(jì)11281人,直接經(jīng)濟(jì)損失435.05億元,不僅如此,滑坡所帶來(lái)的次生災(zāi)害也是難以估計(jì)的。因此,采取必要的手段對(duì)其監(jiān)測(cè),進(jìn)而科學(xué)、有效地對(duì)滑坡災(zāi)害進(jìn)行預(yù)測(cè)預(yù)報(bào),具有重大的經(jīng)濟(jì)價(jià)值及社會(huì)意義。然而,由于滑坡發(fā)展演化的影響因素眾多(如地形地貌、地質(zhì)構(gòu)造、地層巖性、水文條件、降雨及人類工程活動(dòng)等),使得滑坡運(yùn)動(dòng)具有開放性、復(fù)雜性和不確定性等特點(diǎn),導(dǎo)致很難采用傳統(tǒng)方法對(duì)其進(jìn)行準(zhǔn)確預(yù)測(cè)預(yù)報(bào),為此,本文提出以“智能算法”為主要研究手段,并融入現(xiàn)代測(cè)繪數(shù)據(jù)處理、灰色預(yù)測(cè)、灰色決策等理論知識(shí),以我國(guó)境內(nèi)典型滑坡工程:鏈子崖滑坡、臥龍寺滑坡、古樹屋滑坡、新灘滑坡和重慶萬(wàn)州區(qū)滑坡為研究對(duì)象,圍繞“滑坡變形位移預(yù)測(cè)”和“滑坡危險(xiǎn)性等級(jí)區(qū)劃”,構(gòu)建了基于智能算法的滑坡災(zāi)害預(yù)測(cè)預(yù)報(bào)體系,主要研究?jī)?nèi)容與成果如下:(1)滑坡變形位移狀態(tài)辨識(shí)與曲線特征分類(1)自然界中賦存的滑坡體,由于其形成條件、誘發(fā)因素的不同,導(dǎo)致滑坡變形累積位移曲線形態(tài)各異,深入研究了滑坡變形累積位移曲線的特征,并按形態(tài)將其分為:減速-勻速型、勻速-增速型、減速-勻速-增速型、復(fù)合型四類;通過對(duì)鏈子崖、臥龍寺、古樹屋、新灘四個(gè)典型滑坡體的地形地貌、地質(zhì)構(gòu)造、影響因素等分析基礎(chǔ)上,對(duì)四個(gè)滑坡體的變形曲線特征進(jìn)行辨識(shí)歸類;(2)掌握滑坡變形位移曲線特征,對(duì)判斷滑坡的成因模式、變形發(fā)展階段、誘發(fā)因素影響程度,以及預(yù)測(cè)預(yù)報(bào)模型的選取,具有重要的指導(dǎo)作用。(2)基于經(jīng)典智能算法BP、RBF的滑坡變形位移預(yù)測(cè)(1)針對(duì)標(biāo)準(zhǔn)BP算法網(wǎng)絡(luò)收斂速度慢、易于陷入局部最小等缺陷,推導(dǎo)出了四類BP改進(jìn)算法,以鏈子崖、臥龍寺、古樹屋、新灘四類滑坡體為例,建立了基于BP改進(jìn)算法的滑坡變形位移預(yù)測(cè)模型,深入討論了BP算法建模時(shí)應(yīng)注意的若干問題,給出了BP網(wǎng)絡(luò)結(jié)構(gòu)參數(shù)優(yōu)化實(shí)施的具體流程,構(gòu)建了BP滑坡變形位移預(yù)測(cè)的最優(yōu)網(wǎng)絡(luò)拓?fù)浣Y(jié)構(gòu),算例表明,四種改進(jìn)算法在預(yù)測(cè)效果方面較標(biāo)準(zhǔn)BP算法有明顯改善,且LM-BP算法預(yù)測(cè)效果最優(yōu);(2)闡述了RBF神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)、訓(xùn)練算法及建模過程,提出了基于二維區(qū)間搜索的rbf網(wǎng)絡(luò)參數(shù)優(yōu)化方法;從隱含層傳遞函數(shù)、隱含層節(jié)點(diǎn)數(shù)量、訓(xùn)練算法、逼近方式等方面將rbf與lm-bp進(jìn)行比較,以鏈子崖、臥龍寺、古樹屋、新灘四類滑坡體為例,分析比較rbf、lm-bp算法用于滑坡變形位移預(yù)測(cè)的適用性,實(shí)驗(yàn)結(jié)果表明,rbf較bp算法,在網(wǎng)絡(luò)收斂速度、網(wǎng)絡(luò)泛化能力、外推預(yù)測(cè)方面有所改善。(3)基于新型智能算法elm的滑坡變形位移預(yù)測(cè)(1)將elm算法引入到滑坡變形預(yù)測(cè)中,深入剖析了elm算法的學(xué)習(xí)機(jī)理,指出了其與bp、rbf算法存在的本質(zhì)區(qū)別,elm克服了經(jīng)典算法bp、rbf采用梯度下降訓(xùn)練網(wǎng)絡(luò),導(dǎo)致網(wǎng)絡(luò)易局部最小化的缺點(diǎn);(2)基于誤差處理視角,對(duì)elm網(wǎng)絡(luò)輸出權(quán)重參數(shù)β?的求解進(jìn)行了推導(dǎo),發(fā)現(xiàn)其求解過程是基于最小二乘估計(jì),從而導(dǎo)致elm算法抵御粗差性能較差;為增強(qiáng)elm算法抵御粗差的能力,將廣義極大似然估計(jì)(m估計(jì))與之相融合,提出了基于m估計(jì)的robust-elm滑坡智能預(yù)測(cè)模型;(3)標(biāo)準(zhǔn)elm算法對(duì)滑坡數(shù)據(jù)中的粗差較為敏感,抗粗差性弱;基于m估計(jì)的robust-elm算法能較好的抵御滑坡數(shù)據(jù)中的單個(gè)、多個(gè)粗差,且預(yù)測(cè)精度較高。(4)基于智能耦合模型的滑坡變形位移預(yù)測(cè)針對(duì)采用單一智能算法進(jìn)行滑坡變形預(yù)測(cè)時(shí)所存在的問題,提出構(gòu)建滑坡耦合預(yù)測(cè)模型,基于三個(gè)視角,構(gòu)建了三種形式的耦合模型:基于“權(quán)重約束”的耦合模型,基于“算法融合”的耦合模型,“顧及誘發(fā)因素影響”的耦合模型:(1)根據(jù)權(quán)重求取的不同約束準(zhǔn)則,對(duì)“權(quán)重約束”耦合預(yù)測(cè)模型的構(gòu)建進(jìn)行研究,分別構(gòu)建了“最優(yōu)權(quán)”、“非最優(yōu)權(quán)”、“灰色綜合關(guān)聯(lián)度定權(quán)”、“熵權(quán)”、“elm非線性權(quán)”五種形式的耦合預(yù)測(cè)模型,討論了五類約束準(zhǔn)則權(quán)重的求取特點(diǎn),以古樹屋滑坡、新灘滑坡為例,比較了五種約束準(zhǔn)則下耦合預(yù)測(cè)的效果,算例表明,基于elm的非線性耦合預(yù)測(cè)具有良好的特性和較高的預(yù)測(cè)精度;(2)構(gòu)建“算法融合”耦合預(yù)測(cè)模型時(shí),首先,利用灰色累加生產(chǎn)算子,弱化滑坡變形位移數(shù)據(jù)的隨機(jī)性,構(gòu)建了基于灰化層處理的elm耦合預(yù)測(cè)模型;其次,顧及灰色模型群預(yù)測(cè)時(shí)所提供的有效信息,構(gòu)建了基于灰色模型群的耦合預(yù)測(cè)模型,以古樹屋滑坡、新灘滑坡為例,對(duì)兩種形式的“算法融合”耦合預(yù)測(cè)模型進(jìn)行了效果驗(yàn)證,其預(yù)測(cè)效果均較好;(3)以新灘滑坡、三峽庫(kù)區(qū)某滑坡為例,建立了顧及降雨、庫(kù)水位等誘發(fā)因素影響的多因子耦合預(yù)測(cè)模型。該模型首先將滑坡變形位移分解為趨勢(shì)項(xiàng)和隨機(jī)項(xiàng),利用gm(l,l)模型提取變形的趨勢(shì)項(xiàng),然后采用elm算法逼近誘發(fā)因素與位移隨機(jī)項(xiàng)間的非線性映射關(guān)系,算例表明,該耦合模型能結(jié)合滑坡監(jiān)測(cè)數(shù)據(jù)的特點(diǎn),從數(shù)據(jù)分解角度出發(fā),兼顧了數(shù)據(jù)的趨勢(shì)性與隨機(jī)性,同時(shí)考慮了滑坡體的誘發(fā)因素,從多角度充分利用了滑坡監(jiān)測(cè)數(shù)據(jù)的有效信息且預(yù)測(cè)精度較高。(5)基于多因素加權(quán)灰靶決策理論的ELM滑坡危險(xiǎn)性評(píng)價(jià)以重慶萬(wàn)州區(qū)滑坡為例,首次將多因素加權(quán)灰靶決策模型引入到滑坡危險(xiǎn)性評(píng)價(jià)中,并將其與新型智能ELM算法進(jìn)行耦合,構(gòu)建了顧及多影響因素加權(quán)灰靶ELM的滑坡危險(xiǎn)性評(píng)價(jià)模型:(1)以滑坡災(zāi)害形成條件、誘發(fā)因素為出發(fā)點(diǎn),選取高差、坡度、滑體物質(zhì)類型、降水、人類工程活動(dòng)等11項(xiàng)因素作為滑坡危險(xiǎn)性評(píng)價(jià)指標(biāo),通過灰色關(guān)聯(lián)分析得出影響因素權(quán)重的大小;(2)以灰靶決策理論為基礎(chǔ),根據(jù)所得靶心距的大小,對(duì)滑坡的危險(xiǎn)等級(jí)進(jìn)行量化評(píng)價(jià),將滑坡危險(xiǎn)等級(jí)劃分為高度、較高、中度、低度和較低5級(jí);進(jìn)而采用ELM算法對(duì)待評(píng)價(jià)滑坡體的靶心距進(jìn)行預(yù)測(cè),根據(jù)所得靶心距,實(shí)現(xiàn)待評(píng)價(jià)滑坡體的危險(xiǎn)等級(jí)區(qū)劃;(3)基于加權(quán)灰靶決策ELM模型進(jìn)行滑坡危險(xiǎn)等級(jí)區(qū)劃時(shí),較為全面的考慮了不同影響因素所提供的有效信息,能綜合考慮滑坡影響因素及權(quán)重的分配,通過靶心距的比較實(shí)現(xiàn)危險(xiǎn)等級(jí)的量化,根據(jù)評(píng)價(jià)數(shù)據(jù)靶心距與標(biāo)準(zhǔn)靶心的比較,劃分了滑坡危險(xiǎn)性等級(jí),實(shí)現(xiàn)了多因素定性與定量結(jié)合的預(yù)測(cè),提高了滑坡危險(xiǎn)預(yù)測(cè)的科學(xué)性與準(zhǔn)確性。
[Abstract]:Landslide is a common geological disaster, it happened, not only to human life and property safety brought serious threat to resources, environment, ecology and other aspects brought great destruction. China is the world's landslide is one of the most serious countries, according to the Chinese geological environment information network released Chinese geological disaster Bulletin shows: 2006 to 10 years in 2015 a total of 260353 geological disasters, which happened in the proportion of landslide geological disasters, geological disasters accounted for 73.79% of the total, for the 10 year period by the landslide caused casualties, missing a total number of 11281 people, the direct economic loss of 43 billion 505 million yuan, not only that, it is difficult to estimate is secondary the disaster caused by the landslide. Therefore, to take the necessary measures for the monitoring and scientific prediction of landslide disaster effectively, has great economic value and social significance However, because many factors affect Landslide Evolution (such as topography, geological structure, lithology, hydrology, rainfall and human engineering activities, etc.), the landslide movement has openness, complexity and uncertainty, it is difficult using traditional methods to accurately predict, therefore, is proposed in this paper. In "intelligent algorithm" is the main research means, and integrate into the modern surveying data processing, grey forecasting, grey decision-making theory knowledge, to our country the typical landslide: Lianziya landslide, Wolong Temple Landslide, gushuwu landslide, Xintan Landslide and landslide in Wanzhou District of Chongqing as the research object, around the landslide deformation prediction "and the" division of landslide risk rating, constructed the forecasting system of landslide hazard prediction based on intelligent algorithm, the main research contents and results are as follows: (1) the deformation of the landslide displacement state identification and song Line feature classification (1) landslide occurrence in nature, because of its formation conditions, induced by different factors, lead to accumulative deformation shapes of displacement curve of landslide, in-depth study of the deformation characteristics of the landslide accumulative displacement curve, and according to the form which can be divided into: speed uniform, uniform growth - type, uniform deceleration - growth type, composite type four; through the Lianziya, Wolong temple, the ancient house, four typical Xintan Landslide topography, geological structure, based on the analysis of influencing factors, characteristics of deformation curve of four landslide identification classification; (2) Master landslide displacement curve. The causes of the landslide deformation mode, development stage, the influence degree of predisposing factors, and the selection of forecast model, has an important guiding role. (2) the classic intelligent algorithm based on BP, RBF landslide displacement prediction (1) according to the standard BP algorithm for network The slow convergence speed, easy to fall into local minimum defects, derived four kinds of improved BP algorithm in Lianziya, Wolong temple, old house, the new four beach landslide as an example, to establish a prediction model of deformation of landslide based on BP algorithm, discusses some problems should be paid attention to when modeling the BP algorithm the specific process, BP network optimization of structural parameters of the given optimal network topology construction deformation of BP landslide displacement prediction, numerical examples show that the four algorithms in prediction results than the standard BP algorithm is improved, and the LM-BP algorithm to predict the optimal effect; (2) describes the structure of RBF neural network, training algorithm and modeling process, puts forward RBF network parameter optimization method based on two-dimensional interval search; transfer function in the hidden layer, hidden layer node number, training algorithm, approximation methods aspects of RBF and LM-BP were compared in lianziya, Wolong temple, the ancient house, four kinds of Xintan landslide as an example, analysis and comparison of RBF, LM-BP algorithm for landslide displacement prediction of applicability, the experimental results show that RBF is in the BP algorithm, the convergence rate of network, the network generalization ability, extrapolation improves. (3) a new intelligent algorithm of elm landslide deformation displacement prediction based on (1) the elm algorithm is introduced to the landslide deformation prediction, in-depth analysis of the mechanism of elm learning algorithm, and points out the essential difference between BP, in the RBF algorithm, elm overcomes the classical algorithm BP, RBF uses gradient descent training network, resulting in network local minimum defects (2); error processing based on the perspective of the output weights of elm network parameter? Solution is derived, the solution process is based on the least squares estimation, which leads to poor performance of the elm algorithm against outliers; to enhance the ability to resist gross error elm algorithm, generalized The maximum likelihood estimation (M estimation) and the integration of the proposed intelligent prediction model of landslide robust-elm estimation based on M; (3) the standard elm algorithm is sensitive to outliers in the landslide data, outlier is weak; single robust-elm M estimation algorithm can resist landslide data better based on the multiple the gross error, and prediction accuracy. (4) predict problems existed when the landslide deformation prediction based on single intelligent algorithm intelligent displacement coupling model is proposed based on the landslide, landslide coupling prediction model, based on three perspectives, a coupling model is designed in three ways: Based on the "weight constraint" coupling model based on the fusion algorithm, the coupling model of "coupling model" for inducing factors: (1) according to the different weight constraint criterion, the research of "constructing weight constraint" coupling prediction model, respectively. The construction of "optimal", "non optimal weight", "grey comprehensive relationship right", "entropy", "prediction model elm coupled nonlinear power" five forms, the paper also discusses the characteristics of five kinds of constraints and criteria weights, to gushuwu landslide, Xintan landslide as an example, comparison five kinds of constraints under the criterion of coupling prediction results, numerical examples show that the prediction accuracy of nonlinear coupled elm has good characteristics and high based; (2) to construct fusion coupling prediction model algorithm, firstly, using the grey cumulative production operator, weakening the randomness of landslide deformation displacement data, construct the prediction model of elm coupling ashable layer based processing; secondly, the effective information provided for grey model forecast, prediction model is built based on the coupled grey model group, to gushuwu landslide, Xintan landslide as an example, the fusion algorithm for the two kinds of "" Coupling prediction model verified, the prediction results are better; (3) the Xintan Landslide in Three Gorges Reservoir area, landslide as an example, established for rainfall forecasting model of multi factor coupling effect of reservoir level induced factors. Firstly, the landslide deformation is decomposed into trend item and random item by GM (L, l) model to extract the trend of deformation, and then use the elm algorithm and the random displacement approximation factor between the nonlinear mapping relation. Examples show that the coupled model can be combined with the characteristics of the landslide monitoring data, from the point of data decomposition, taking into account the trend and random data, considering the induced the factors of the landslide, from many angles to make full use of the high effective information and the prediction precision of landslide monitoring data. (5) evaluation of ELM landslide risk factors weighted grey target theory based on Chongqing Wanzhou District landslide as an example, the first A multi factor weighted grey target decision model is introduced into the landslide hazard assessment, which is coupled with the new intelligent ELM algorithm, constructed the landslide hazard evaluation for multi factor weighted grey target model of ELM: (1) to the landslide disaster forming conditions, inducing factors as a starting point, select the elevation, slope the sliding body, material types, rainfall and human engineering activities as the 11 factors of landslide risk assessment index, by using gray correlation analysis the factors that affect the size of the weight; (2) the grey target decision theory, according to the target distance, the quantitative evaluation for risk grade of the landslide, the landslide risk rating divided into high, moderate, low height and low level of 5; then ELM algorithm is used to evaluate landslide - target distance forecast, according to the target distance, realize the risk zoning for the evaluation of landslide; (3) based on the The ELM model of grey target decision-making of landslide hazard zoning, effective information comprehensively considering different factors that can provide, considering influence of distribution factors and weights, quantified by a comparison of off target distance to the level of risk, according to the evaluation data from target compared with the standard target, the division of landslide hazard the level, prediction of a combination of qualitative and quantitative factors, improve the scientificity and accuracy of landslide hazard prediction.

【學(xué)位授予單位】：中國(guó)礦業(yè)大學(xué)(北京)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：P642.22

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