發(fā)布時(shí)間：2018-05-31 23:27

  本文選題：氡潛勢(shì)預(yù)測(cè) + NB算法��； 參考：《中國(guó)地質(zhì)大學(xué)(北京)》2017年碩士論文

【摘要】：現(xiàn)如今人們愈來(lái)愈注重環(huán)境對(duì)人類(lèi)健康的影響,許多科研工作者已經(jīng)展開(kāi)了對(duì)環(huán)境方面的調(diào)查和研究。與人類(lèi)生命相關(guān)的空氣中,還存在著一些天然放射性的氣體,例如氡。氡是鈾衰變而來(lái)的,普遍存在于巖石,土壤和空氣中。氡對(duì)健康有危害,有可能導(dǎo)致肺癌,故在高氡水平地區(qū)全面研究氡風(fēng)險(xiǎn)是有必要進(jìn)行的。中國(guó)幅員遼闊,人口也比較稠密,若在所有地區(qū)進(jìn)行氡測(cè)量將會(huì)是一項(xiàng)浩大的工程,這在目前是難以實(shí)現(xiàn)的。樸素貝葉斯分類(lèi)器由于其算法簡(jiǎn)單易懂,分類(lèi)效率高效穩(wěn)定,并且有堅(jiān)實(shí)的理論基礎(chǔ),在研究中將樸素貝葉斯分類(lèi)器添加到氡潛勢(shì)預(yù)測(cè)中將有效減少測(cè)氡工作量。本論文以廣東中山地區(qū)56個(gè)有效測(cè)點(diǎn)的氡測(cè)量數(shù)據(jù)以及相關(guān)參數(shù)為研究范圍,首先對(duì)樸素貝葉斯算法(NB)和基于相關(guān)系數(shù)的加權(quán)樸素貝葉斯算法(WNB-CC)的理論進(jìn)行了深入研究,利用MATLAB軟件編寫(xiě)工作代碼并進(jìn)行了正確性驗(yàn)證;而后對(duì)56個(gè)有效測(cè)點(diǎn)的參數(shù)進(jìn)行了分步驟等級(jí)劃分,最后確定了巖性、土壤氡析出率、鈾含量、土壤氡濃度的等級(jí)劃分標(biāo)準(zhǔn),同時(shí)對(duì)NB算法和WNB-CC算法的預(yù)測(cè)概率進(jìn)行了比較,得到WNB-CC算法的預(yù)測(cè)概率總體上高于NB算法的預(yù)測(cè)概率;隨后對(duì)WNB-CC算法的單點(diǎn)預(yù)測(cè)結(jié)果的預(yù)測(cè)失敗點(diǎn)進(jìn)行了分析。最后論文對(duì)未知點(diǎn)預(yù)測(cè)的可接受性進(jìn)行了分析,結(jié)合NB算法和WNB-CC算法單點(diǎn)預(yù)測(cè)結(jié)果,總結(jié)了未知點(diǎn)預(yù)測(cè)的可接受標(biāo)準(zhǔn)。在此基礎(chǔ)上,論文對(duì)中山地區(qū)的未知點(diǎn)進(jìn)行了預(yù)測(cè),結(jié)果可接受。本文研究基于樸素貝葉斯算法的氡潛勢(shì)預(yù)測(cè)方法,旨在減少大量的氡實(shí)地調(diào)查工作,也為后來(lái)人提供一些參考。
[Abstract]:Nowadays, people pay more and more attention to the impact of environment on human health. There are also natural radioactive gases, such as radon, in the air associated with human life. Radon is the decay of uranium and is widespread in rocks, soil and air. Radon is harmful to health and may lead to lung cancer, so it is necessary to study radon risk in high radon level areas. China is a vast and densely populated country. Radon measurement in all regions would be a huge project, which is difficult to achieve at present. Since the naive Bayesian classifier is simple and easy to understand, the classification efficiency is efficient and stable, and has a solid theoretical foundation, adding naive Bayesian classifier to radon potential prediction will effectively reduce the radon measurement workload. In this paper, the radon measurement data and related parameters of 56 effective measuring points in Zhongshan area of Guangdong Province are taken as the research scope. Firstly, the theory of naive Bayes algorithm and weighted naive Bayesian algorithm based on correlation coefficient (WNB-CC) are studied in depth. The working code is compiled and verified by MATLAB software, and then the parameters of 56 effective measuring points are classified step by step, and the classification criteria of lithology, soil radon exhalation rate, uranium content and soil radon concentration are determined. At the same time, the prediction probabilities of NB algorithm and WNB-CC algorithm are compared, the prediction probability of WNB-CC algorithm is higher than that of NB algorithm, and the prediction failure point of single point prediction result of WNB-CC algorithm is analyzed. Finally, the acceptability of unknown point prediction is analyzed, and the acceptable criteria of unknown point prediction are summarized by combining the results of NB algorithm and WNB-CC algorithm. On this basis, the unknown points in Zhongshan area are predicted and the results are acceptable. In this paper, the prediction method of radon potential based on naive Bayes algorithm is studied in order to reduce a large number of radon field surveys and to provide some references for future generations.
【學(xué)位授予單位】：中國(guó)地質(zhì)大學(xué)(北京)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：X837;TP18

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本文編號(hào)：1961800