本文選題：滑坡易發(fā)性評(píng)估 + 地震滑坡�。� 參考：《地球信息科學(xué)學(xué)報(bào)》2017年12期

【摘要】：Newmark位移模型是研究地震滑坡易發(fā)性的經(jīng)典模型,機(jī)器學(xué)習(xí)方法支持向量機(jī)模型也越來越多的應(yīng)用到滑坡易發(fā)性評(píng)估研究。本文將Newmark位移模型與支持向量機(jī)模型相結(jié)合,建立基于物理機(jī)理的地震滑坡易發(fā)性評(píng)估模型并應(yīng)用于2008年汶川地震重災(zāi)區(qū)汶川縣。從震后遙感影像目視解譯出汶川縣1900處地震誘發(fā)滑坡,并將其隨機(jī)劃分為70%的訓(xùn)練數(shù)據(jù)集和30%的驗(yàn)證數(shù)據(jù)集。選擇地形起伏度、坡度、地形曲率、與構(gòu)造斷裂帶距離、與水系距離、與道路距離6個(gè)因子與Newmark位移值共同作為地震滑坡易發(fā)性影響因素。利用ROC曲線和模型不確定性等指標(biāo)對(duì)模型結(jié)果進(jìn)行評(píng)估,并與二元統(tǒng)計(jì)模型頻率比和多元統(tǒng)計(jì)模型Logistic回歸的結(jié)果進(jìn)行對(duì)比。結(jié)果表明:與頻率比和Logistic回歸模型相比,支持向量機(jī)模型的正確率最高,訓(xùn)練集和驗(yàn)證集ROC曲線下的面積分別為0.876和0.851。將模型應(yīng)用于繪制汶川縣地震滑坡易發(fā)性圖,結(jié)果顯示滑坡易發(fā)性圖與實(shí)際的滑坡點(diǎn)位分布一致性較高,有80.4%的滑坡位于極高和高易發(fā)區(qū)。這說明支持向量機(jī)與Newmark位移方法結(jié)合建立的地震滑坡易發(fā)性評(píng)估模型有較高的預(yù)測(cè)價(jià)值,可以為滑坡風(fēng)險(xiǎn)評(píng)估和管理提供依據(jù)。
[Abstract]:Newmark displacement model is a classical model to study the vulnerability of earthquake landslide. The machine learning method support vector machine model is applied more and more to the evaluation of landslide vulnerability. In this paper, the Newmark displacement model and support vector machine model are combined to establish the earthquake landslide vulnerability assessment model based on physical mechanism and applied to Wenchuan county in Wenchuan earthquake disaster area in 2008. 1900 earthquake induced landslides in Wenchuan County were visually interpreted from the remote sensing images after the earthquake, and were randomly divided into 70% training data set and 30% validation data set. Six factors, such as terrain fluctuation, slope, topographic curvature, distance from tectonic fault zone, distance from water system, distance from road to road, and displacement value of Newmark, are selected as influencing factors of earthquake landslide susceptibility. ROC curve and model uncertainty were used to evaluate the model results, and the results were compared with the frequency ratio of binary statistical model and logistic regression of multivariate statistical model. The results show that the accuracy of SVM model is the highest compared with frequency ratio and logistic regression model. The area under ROC curve of training set and verification set are 0.876 and 0.851 respectively. The model is applied to draw the landslide susceptibility map of Wenchuan County. The results show that the landslide susceptibility map is consistent with the actual landslide location distribution, and 80.4% of the landslides are located in extremely high and high prone areas. This shows that the model of earthquake landslide vulnerability assessment based on support vector machine and Newmark displacement method has high predictive value and can provide basis for landslide risk assessment and management.
【作者單位】： 北京師范大學(xué)環(huán)境演變與自然災(zāi)害教育部重點(diǎn)實(shí)驗(yàn)室;北京師范大學(xué)減災(zāi)與應(yīng)急管理研究院;
【基金】：國家自然科學(xué)基金項(xiàng)目(41271544) 地表過程模型與模擬創(chuàng)新研究群體科學(xué)基金(41621061) 國家重點(diǎn)研發(fā)計(jì)劃專項(xiàng)項(xiàng)目(2016YFA0602403)
【分類號(hào)】：P642.22

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本文編號(hào)：2068401