本文選題：相關(guān)向量機　切入點：遺傳算法　出處：《科技導(dǎo)報》2017年15期

【摘要】：地震滑坡敏感性分析是地震次生災(zāi)害研究的重點內(nèi)容之一。數(shù)據(jù)量大且致災(zāi)因素復(fù)雜是研究地震滑坡問題的難點。在對已有敏感性分析模型研究的基礎(chǔ)上,以蘆山地震為例,選取地面高程、坡度、坡向、地層、斜坡形態(tài)、斜坡結(jié)構(gòu)、距斷層平均距離、距水系平均距離、地震峰值加速度9個地震滑坡評價因子,建立基于遺傳算法的相關(guān)向量機(GA-RVM)敏感性分析模型,生成地震滑坡敏感性區(qū)劃圖,統(tǒng)計結(jié)果顯示滑坡正確率為99.74%,滑坡密度在極高敏感區(qū)達到27.4057個/km2。結(jié)果表明,相對于基于遺傳算法的支持向量機,GA-RVM獲得了更高的預(yù)測精度,可為進一步完成地震災(zāi)害預(yù)防提供依據(jù)。
[Abstract]:The sensitivity analysis of earthquake landslide is one of the important contents in the study of earthquake secondary disasters.It is difficult to study the problem of earthquake landslide with large amount of data and complicated factors.Based on the existing sensitivity analysis models, taking the Lushan earthquake as an example, the surface elevation, slope, slope direction, stratum, slope shape, slope structure, average distance from fault and average distance from water system are selected.Nine seismic landslide evaluation factors with peak acceleration of earthquake are established, and the sensitivity analysis model of correlation vector machine GA-RVM based on genetic algorithm is established, and the sensitivity map of seismic landslide is generated.The statistical results show that the correct rate of landslide is 99.74 and the density of landslide is 27.4057 / km2 in extremely sensitive area.The results show that the prediction accuracy of GA-RVM is higher than that of GA-RVM based on genetic algorithm, which can provide the basis for the further completion of earthquake disaster prevention.
【作者單位】： 中國地質(zhì)大學(xué)(武漢)地球物理與空間信息學(xué)院;武漢工程大學(xué)資源與土木工程學(xué)院;長安大學(xué)地質(zhì)工程與測繪學(xué)院;
【基金】：國家高技術(shù)研究發(fā)展計劃(863計劃)項目(2012AA121303) 國家自然科學(xué)基金青年基金項目(41301386)
【分類號】：P642.22

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本文編號：1699792