本文選題：貝葉斯推理 + 污染源反演　； 參考：《中國環(huán)境科學(xué)》2017年10期

【摘要】：基于貝葉斯理論,結(jié)合濃度時間序列方差假定和Adaptive Metropolis MCMC后驗采樣,建立了用于突發(fā)水污染應(yīng)急溯源的貝葉斯推理方法.該方法結(jié)合經(jīng)驗知識和監(jiān)測事實對源項參數(shù)的分布進行推理,直接對溯源結(jié)果的反向不確定性用概率分布形式進行表征.依據(jù)河流實地示蹤劑實驗案例,對Bayesian推理溯源的實際效果、后驗參數(shù)相關(guān)性和影響因素等方面進行了驗證和測試,結(jié)果表明源項參數(shù)和方差的后驗概率密度的偏度對方差假定敏感,且得到關(guān)鍵參數(shù)推薦值:使用RMSE為目標(biāo)函數(shù);異方差假定中穩(wěn)定化因子λ1為0,λ2為0.1~0.5;AM采樣建議比例因子sd選擇0.1~0.3.并對貝葉斯方法和傳統(tǒng)基于優(yōu)化的頻率法在求解思路、計算過程、溯源結(jié)果等角度進行了深層次的辨析.本研究相關(guān)結(jié)果為貝葉斯推理技術(shù)在污染溯源的實際應(yīng)用中提供了較為重要的參考價值.
[Abstract]:Based on Bayesian theory, the Bayesian inference method for traceability of sudden water pollution emergency is established by combining the variance assumption of concentration time series and Adaptive Metropolis MCMC posteriori sampling. In this method, the distribution of source parameters is inferred by combining empirical knowledge and monitoring facts, and the inverse uncertainty of traceability results is directly represented by probability distribution. Based on the experimental cases of river tracer, the actual effect of Bayesian reasoning tracing, the correlation of posterior parameters and the influencing factors are verified and tested. The results show that the bias difference of the posterior probability density of the source term and the variance is sensitive, and the recommended value of the key parameters is as follows: using RMSE as the objective function; in the heteroscedasticity assumption, the stabilization factor 位 _ 1 is 0, 位 _ 2 is 0.1 ~ 0.5am sampling ratio factor SD is 0.1 ~ 0.3. Furthermore, the Bayesian method and the traditional frequency method based on optimization are analyzed in the aspects of thinking, calculation process and traceability. The results of this study provide an important reference for Bayesian reasoning in the practical application of pollution traceability.
【作者單位】： 哈爾濱工業(yè)大學(xué)環(huán)境學(xué)院;南方科技大學(xué)環(huán)境科學(xué)與工程學(xué)院;
【基金】：國家水體污染控制與治理科技重大專項基金(2012ZX07205-005) 中國博士后科學(xué)基金(2014M551249)
【分類號】：X52
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本文編號：1956905