湖庫藻類水華智能識(shí)別與預(yù)測研究
發(fā)布時(shí)間：2019-07-05 20:56
【摘要】：當(dāng)前,我國大多數(shù)湖庫水體富營養(yǎng)化現(xiàn)象較為突出。由于水體中積聚了大量的氮、磷等營養(yǎng)物質(zhì),導(dǎo)致一些藻類異常繁殖,不斷積聚而形成不同程度的藍(lán)藻水華,如何對藍(lán)藻水華這一水環(huán)境污染進(jìn)行識(shí)別與預(yù)測預(yù)警,已經(jīng)成為當(dāng)今水環(huán)境領(lǐng)域研究的重點(diǎn)之一。本文綜合分析了國內(nèi)外湖庫藻類水華識(shí)別與預(yù)測的研究現(xiàn)狀,對湖庫藻類水華的智能識(shí)別與預(yù)測方法進(jìn)行了深入研究。首先,在對湖庫水體遙感反演方法深入研究的基礎(chǔ)上,提出了基于D-S證據(jù)理論的湖庫站點(diǎn)監(jiān)測與遙感監(jiān)測的信息融合方法,實(shí)現(xiàn)了對關(guān)注區(qū)域內(nèi)的藍(lán)藻水華的有效識(shí)別;其次,通過對湖庫水體富營養(yǎng)化評價(jià)指標(biāo)的綜合分析,采用核主成分分析法確定了藍(lán)藻水華形成與暴發(fā)的關(guān)鍵影響因素,構(gòu)建了基于誤差補(bǔ)償?shù)乃{(lán)藻水華時(shí)序綜合預(yù)測模型;在此基礎(chǔ)上,考慮到自然湖庫中環(huán)境因素對藍(lán)藻水華形成的影響特征,采用自適應(yīng)模糊推理專家系統(tǒng)對影響藍(lán)藻水華暴發(fā)的表征因素葉綠素a進(jìn)行預(yù)測,一定程度上解決了在環(huán)境突變情況下藍(lán)藻水華預(yù)測精度不高的問題;最后,將研究成果嵌入到湖庫水質(zhì)監(jiān)測與藍(lán)藻水華預(yù)測預(yù)警系統(tǒng)中,并將其應(yīng)用到實(shí)際湖庫中,為環(huán)保部門進(jìn)行湖庫水環(huán)境監(jiān)測和信息管理提供了輔助決策平臺(tái)。
[Abstract]:At present, the eutrophication of most lakes and reservoirs in China is more prominent. Due to the accumulation of a large number of nitrogen, phosphorus and other nutrients in the water body, some algae reproduce abnormally and accumulate constantly to form different degrees of cyanobacteria blooms. How to identify, predict and warn the water environment pollution of cyanobacteria blooms has become one of the key points in the field of water environment. In this paper, the research status of algae bloom identification and prediction in lake and reservoir at home and abroad is comprehensively analyzed, and the intelligent recognition and prediction method of algae bloom in lake and reservoir is deeply studied. Firstly, based on the in-depth study of remote sensing inversion method of lake and reservoir water body, the information fusion method of lake reservoir site monitoring and remote sensing monitoring based on D / S evidence theory is proposed, and the effective identification of blue algae blooms in the area of concern is realized. Secondly, through the comprehensive analysis of the evaluation index of eutrophication in lake and reservoir, the key influencing factors of the formation and outbreak of cyanobacteria bloom were determined by nuclear principal component analysis, and the comprehensive prediction model of cyanobacteria bloom time series based on error compensation was constructed. On this basis, considering the influence of environmental factors on the formation of cyanobacteria blooms in natural lakes, the adaptive fuzzy reasoning expert system is used to predict the characterization factor chlorophyll a, which can solve the problem that the prediction accuracy of cyanobacteria blooms is not high in the case of environmental mutation. Finally, the research results are embedded in the lake reservoir water quality monitoring and blue algae bloom prediction and early warning system, and applied to the actual lake reservoir, which provides an auxiliary decision-making platform for environmental protection departments to carry out lake and reservoir water environment monitoring and information management.
【學(xué)位授予單位】：北京工商大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：X524
本文編號：2510806
[Abstract]:At present, the eutrophication of most lakes and reservoirs in China is more prominent. Due to the accumulation of a large number of nitrogen, phosphorus and other nutrients in the water body, some algae reproduce abnormally and accumulate constantly to form different degrees of cyanobacteria blooms. How to identify, predict and warn the water environment pollution of cyanobacteria blooms has become one of the key points in the field of water environment. In this paper, the research status of algae bloom identification and prediction in lake and reservoir at home and abroad is comprehensively analyzed, and the intelligent recognition and prediction method of algae bloom in lake and reservoir is deeply studied. Firstly, based on the in-depth study of remote sensing inversion method of lake and reservoir water body, the information fusion method of lake reservoir site monitoring and remote sensing monitoring based on D / S evidence theory is proposed, and the effective identification of blue algae blooms in the area of concern is realized. Secondly, through the comprehensive analysis of the evaluation index of eutrophication in lake and reservoir, the key influencing factors of the formation and outbreak of cyanobacteria bloom were determined by nuclear principal component analysis, and the comprehensive prediction model of cyanobacteria bloom time series based on error compensation was constructed. On this basis, considering the influence of environmental factors on the formation of cyanobacteria blooms in natural lakes, the adaptive fuzzy reasoning expert system is used to predict the characterization factor chlorophyll a, which can solve the problem that the prediction accuracy of cyanobacteria blooms is not high in the case of environmental mutation. Finally, the research results are embedded in the lake reservoir water quality monitoring and blue algae bloom prediction and early warning system, and applied to the actual lake reservoir, which provides an auxiliary decision-making platform for environmental protection departments to carry out lake and reservoir water environment monitoring and information management.
【學(xué)位授予單位】：北京工商大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：X524
【參考文獻(xiàn)】
相關(guān)期刊論文 前1條
1 楊寧;冀德剛;李雙金;;Pearson相關(guān)分析法在京津冀空氣質(zhì)量分析中的應(yīng)用（英文）[J];Agricultural Science & Technology;2015年03期
，本文編號：2510806
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