本文選題：暴雨型泥石流 + GIS空間分析　； 參考：《中國(guó)地質(zhì)大學(xué)（北京）》2013年博士論文

【摘要】：泥石流是山區(qū)常見(jiàn)的自然災(zāi)害，具有很強(qiáng)的破壞性，直接威脅人民生命和財(cái)產(chǎn)安全，嚴(yán)重影響經(jīng)濟(jì)的可持續(xù)發(fā)展。本文基于GIS空間分析技術(shù)和計(jì)算智能理論建立模型，分析評(píng)價(jià)泥石流災(zāi)害風(fēng)險(xiǎn)，為泥石流預(yù)測(cè)預(yù)報(bào)提供科學(xué)依據(jù)和技術(shù)支持。 泥石流災(zāi)害系統(tǒng)屬于復(fù)雜非線性系統(tǒng)，存在模糊性和不確定性。本文依據(jù)泥石流孕育、發(fā)展過(guò)程不同階段的特點(diǎn)，以計(jì)算智能理論和GIS技術(shù)集成泥石流災(zāi)害的多元影響因子，建立泥石流災(zāi)害的風(fēng)險(xiǎn)評(píng)價(jià)預(yù)測(cè)模型。運(yùn)用研究區(qū)觀測(cè)數(shù)據(jù)進(jìn)行仿真，驗(yàn)證了模型的有效性。本文的研究工作與主要結(jié)論如下： (1)根據(jù)泥石流發(fā)展過(guò)程中的一系列動(dòng)態(tài)過(guò)程事件，以事件樹(shù)理論為基礎(chǔ)構(gòu)建了泥石流災(zāi)害發(fā)展過(guò)程的事件樹(shù)模型，用模糊語(yǔ)言表達(dá)事件樹(shù)節(jié)點(diǎn)事件的發(fā)生概率，，并對(duì)泥石流災(zāi)害的發(fā)生概率進(jìn)行模糊評(píng)價(jià)，最后通過(guò)解模糊方法得到泥石流災(zāi)害風(fēng)險(xiǎn)概率。事件樹(shù)模型體現(xiàn)了泥石流孕災(zāi)過(guò)程的階段性，計(jì)算出的泥石流災(zāi)害事件的發(fā)生概率與實(shí)際吻合。 (2)將坡度、相對(duì)高差、植被覆蓋度、沿溝松散物儲(chǔ)量、前5天累計(jì)降雨量、最大小時(shí)雨強(qiáng)和當(dāng)日雨量作為泥石流災(zāi)害預(yù)警模型的評(píng)價(jià)指標(biāo)，根據(jù)評(píng)價(jià)指標(biāo)特點(diǎn)制定相應(yīng)的關(guān)聯(lián)函數(shù)來(lái)計(jì)算關(guān)聯(lián)度，運(yùn)用可拓學(xué)理論建立泥石流預(yù)警模型，為泥石流災(zāi)害評(píng)價(jià)提出了形式化的理論方法。 (3)利用模擬退火遺傳算法改進(jìn)GMDH網(wǎng)絡(luò)模型，并用改進(jìn)的GMDH模型預(yù)測(cè)泥石流災(zāi)害。用KLDA判別分析方法選取關(guān)聯(lián)度大的致災(zāi)因子，將其作為輸入?yún)?shù)，泥石流一次最大沖出量作為輸出參數(shù)，運(yùn)用改進(jìn)的GMDH網(wǎng)絡(luò)模型進(jìn)行泥石流災(zāi)害預(yù)測(cè)，模型精度比其他模型（如BP和ANFIS）高。 (4)提出了基于本地搜索策略的混合蟻群優(yōu)化方法來(lái)優(yōu)化貝葉斯網(wǎng)絡(luò)結(jié)構(gòu)學(xué)習(xí)，以改進(jìn)貝葉斯網(wǎng)絡(luò)模型，并將改進(jìn)的模型用于泥石流災(zāi)害風(fēng)險(xiǎn)評(píng)估，計(jì)算出的泥石流災(zāi)害危險(xiǎn)度與實(shí)際情況吻合。該方法為地學(xué)分析中不確定性問(wèn)題、數(shù)據(jù)不完整問(wèn)題的研究提供了一種新技術(shù)。
[Abstract]:Debris flow is a common natural disaster in mountainous area, which is very destructive and directly threatens the safety of people's life and property, and seriously affects the sustainable development of economy. Based on GIS spatial analysis technology and computational intelligence theory, this paper establishes a model to analyze and evaluate the risk of debris flow disaster, and provides scientific basis and technical support for debris flow prediction and prediction. Debris flow disaster system is a complex nonlinear system with fuzzy and uncertainty. According to the characteristics of different stages of debris flow gestation and development, the risk assessment and prediction model of debris flow disaster is established by integrating the multiple influencing factors of debris flow disaster with intelligent theory and GIS technology. The validity of the model is verified by using the observed data in the research area. The research work and main conclusions are as follows: 1) according to a series of dynamic events in the development of debris flow, the event tree model of debris flow disaster development process is constructed based on event tree theory, and the occurrence probability of event tree node event is expressed by fuzzy language. The probability of debris flow hazard is evaluated by fuzzy evaluation. Finally, the probability of debris flow disaster risk is obtained by solving fuzzy method. The event tree model reflects the stages of debris flow disaster occurrence process, and the calculated probability of debris flow disaster event is consistent with the actual situation. (2) the slope, relative height difference, vegetation coverage, reserves of loose materials along gullies, accumulated rainfall in the first 5 days, maximum hourly rain intensity and daily rainfall are taken as the evaluation indexes of debris flow disaster warning model. According to the characteristics of the evaluation index, the correlation function is formulated to calculate the correlation degree, and the early warning model of debris flow is established by using extension theory, which provides a formal theoretical method for debris flow disaster evaluation. The simulated annealing genetic algorithm is used to improve the GMDH network model and the improved GMDH model is used to predict the debris flow. The KLDA discriminant analysis method is used to select the disaster factors with high correlation degree, which is regarded as the input parameter, and the maximum flow amount of debris flow is taken as the output parameter. The improved GMDH network model is used to predict the debris flow disaster. The accuracy of the model is higher than that of other models, such as BP and ANFIS. (4) A hybrid ant colony optimization method based on local search strategy is proposed to optimize Bayesian network structure learning to improve the Bayesian network model, and the improved model is applied to debris flow risk assessment. The calculated hazard degree of debris flow is in agreement with the actual situation. This method provides a new technique for the study of uncertainty and incomplete data in geoscientific analysis.
【學(xué)位授予單位】：中國(guó)地質(zhì)大學(xué)（北京）
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2013
【分類號(hào)】：P208;P642.23

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