本文關(guān)鍵詞： 隨機(jī)森林 邊坡穩(wěn)定性 離子型稀土 預(yù)測(cè)模型　出處：《礦業(yè)研究與開(kāi)發(fā)》2017年04期 　論文類(lèi)型：期刊論文

【摘要】：為對(duì)離子型稀土原地浸礦邊坡進(jìn)行穩(wěn)定性預(yù)測(cè),結(jié)合贛南離子型稀土礦山42個(gè)邊坡實(shí)例,選取重度、黏聚力、內(nèi)摩擦角、邊坡角、孔隙壓力比5個(gè)影響因子作為輸入,邊坡?tīng)顟B(tài)作為輸出,通過(guò)隨機(jī)森林算法建立邊坡穩(wěn)定性影響因素與邊坡穩(wěn)定狀態(tài)之間的非線性關(guān)系。利用30組邊坡穩(wěn)定性數(shù)據(jù)作為隨機(jī)森林預(yù)測(cè)模型的訓(xùn)練數(shù)據(jù)集,進(jìn)行模型的學(xué)習(xí)訓(xùn)練;用另12組邊坡穩(wěn)定性數(shù)據(jù)作為預(yù)測(cè)模型的測(cè)試數(shù)據(jù),通過(guò)訓(xùn)練好的邊坡穩(wěn)定性預(yù)測(cè)模型進(jìn)行測(cè)試。結(jié)果表明,隨機(jī)森林預(yù)測(cè)模型精度高,能夠?yàn)殡x子型稀土原地浸礦邊坡的災(zāi)害防治工作提供指導(dǎo)。
[Abstract]:In order to predict the stability of ion rare earth in-situ leaching slope, combined with 42 slope examples of ion type rare earth mine in south Jiangxi Province, the heavy, cohesion, internal friction angle and slope angle were selected. The pore pressure ratio of 5 factors is taken as the input and the slope state as the output. The nonlinear relationship between the influencing factors of slope stability and slope stability was established by stochastic forest algorithm, and 30 groups of slope stability data were used as the training data set of stochastic forest prediction model. Carry on the study training of the model; The other 12 groups of slope stability data are used as the test data of the prediction model, and the trained slope stability prediction model is tested. The results show that the stochastic forest prediction model has high accuracy. It can provide guidance for disaster prevention and treatment of ionized rare earth in situ leaching slope.
【作者單位】： 江西理工大學(xué)建筑與測(cè)繪工程學(xué)院;
【分類(lèi)號(hào)】：TD865;TD854.6
【正文快照】： 離子型稀土原地浸礦具有保護(hù)地表植被、不占用地表空間、較高的資源回收利用率等優(yōu)點(diǎn),被國(guó)家強(qiáng)制要求使用[1-2]。在浸礦過(guò)程中,采場(chǎng)滑坡事故時(shí)有發(fā)生,對(duì)礦區(qū)的經(jīng)濟(jì)和環(huán)境造成了嚴(yán)重的損失[3]。離子型稀土原地浸礦邊坡穩(wěn)定性受多種因素的影響,其中重要的因素有重度、黏聚力、內(nèi)

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