發(fā)布時(shí)間：2018-06-13 04:04

  本文選題：藻類(lèi)水華 + 水華形成機(jī)理　； 參考：《浙江大學(xué)》2015年碩士論文

【摘要】：近年來(lái),隨著水體富營(yíng)養(yǎng)化程度加劇,藻類(lèi)水華頻繁暴發(fā)。藻類(lèi)水華不僅破壞水體生態(tài)環(huán)境,還威脅人類(lèi)身體健康,并且缺少短期內(nèi)的有效治理手段,因此,對(duì)水體藻類(lèi)濃度進(jìn)行實(shí)時(shí)預(yù)測(cè),在水華暴發(fā)之前采取應(yīng)急措施,降低治理成本,具有重要意義。本文在分析了國(guó)內(nèi)外預(yù)測(cè)方法的基礎(chǔ)上,根據(jù)生長(zhǎng)機(jī)理和通用信號(hào)處理方法各自的特點(diǎn),提出了一種基于藻類(lèi)水華形成機(jī)理的分段短期預(yù)測(cè)方法。同時(shí)針對(duì)國(guó)內(nèi)外水質(zhì)移動(dòng)檢測(cè)系統(tǒng)的不足,改進(jìn)了一種低成本、便攜性好、操作方便靈活、可在線(xiàn)檢測(cè)部分水質(zhì)參數(shù)的移動(dòng)檢測(cè)系統(tǒng)。并將基于藻類(lèi)水華形成機(jī)理的分段短期預(yù)測(cè)模型與水質(zhì)移動(dòng)檢測(cè)系統(tǒng)結(jié)合,實(shí)現(xiàn)對(duì)飲用水源地(地表水)的不同水域進(jìn)行藻類(lèi)水華實(shí)時(shí)預(yù)測(cè)。本文主要工作和特色如下：(1)建立了基于藻類(lèi)水華形成機(jī)理的分段短期預(yù)測(cè)模型。根據(jù)藻類(lèi)水華形成的過(guò)程,確定了藻類(lèi)水華形成關(guān)鍵影響因素,即水溫、光照和營(yíng)養(yǎng)鹽；參考藻類(lèi)水華形成“四階段理論”,本文將全年按月分為3個(gè)階段,選擇不同階段的影響因子,建立藻類(lèi)水華形成分段機(jī)理模型；并根據(jù)藻類(lèi)短期生長(zhǎng)趨勢(shì)預(yù)測(cè)未來(lái)某個(gè)時(shí)刻的生長(zhǎng)情況。(2)完成了藻類(lèi)分段短期機(jī)理預(yù)測(cè)模型的仿真實(shí)驗(yàn)。選用德國(guó)易北河2000年3月到10月的監(jiān)測(cè)數(shù)據(jù)用于模型驗(yàn)證,并用粒子群優(yōu)化算法動(dòng)態(tài)率定模型參數(shù)。從率定數(shù)據(jù)時(shí)間跨度、率定參數(shù)組合、參數(shù)動(dòng)態(tài)率定預(yù)測(cè)序列和未來(lái)三日預(yù)測(cè)序列四個(gè)角度分析了預(yù)測(cè)模型對(duì)葉綠素a濃度的預(yù)測(cè)情況,初步結(jié)論為：①選取率定數(shù)據(jù)時(shí)間跨度為7天時(shí),預(yù)測(cè)結(jié)果最優(yōu)；②光半飽和常數(shù)K,的率定對(duì)預(yù)測(cè)結(jié)果的影響要優(yōu)于光系數(shù)(?)；③預(yù)測(cè)序列誤差約為10%,說(shuō)明該預(yù)測(cè)模型能夠很好的應(yīng)用于德國(guó)易北河葉綠素a濃度預(yù)測(cè)；④從未來(lái)一日到未來(lái)三日,預(yù)測(cè)誤差依次增大。(3)完成了水質(zhì)移動(dòng)檢測(cè)系統(tǒng)的硬件和軟件改進(jìn)。該系統(tǒng)由移動(dòng)檢測(cè)平臺(tái)、監(jiān)控中心和手持終端三部分組成,移動(dòng)檢測(cè)平臺(tái)用于對(duì)目標(biāo)水域水質(zhì)信息在線(xiàn)檢測(cè),并將檢測(cè)結(jié)果發(fā)送到監(jiān)控中心和手持終端,本文實(shí)現(xiàn)了水溫和光照強(qiáng)度的遠(yuǎn)程檢測(cè)；監(jiān)控中心用于存儲(chǔ)水質(zhì)歷史數(shù)據(jù)和實(shí)現(xiàn)藻類(lèi)水華預(yù)測(cè)；手持終端用于發(fā)送相應(yīng)控制命令。改進(jìn)后的系統(tǒng)支持多種水質(zhì)參數(shù)在線(xiàn)檢測(cè),并在移動(dòng)檢測(cè)平臺(tái)上搭載圖像傳感模塊,采集水樣圖像,通過(guò)水樣顏色來(lái)快速判斷藻類(lèi)水華暴發(fā)情況。
[Abstract]:In recent years, algae Shui Hua outbreaks frequently with the increase of eutrophication. Algal Shui Hua not only destroys the ecological environment of water body, but also threatens human health, and lacks effective control measures in the short term. Therefore, the concentration of algae in water is predicted in real time, and emergency measures are taken before the outbreak of Shui Hua. Reduce management cost, have important meaning. Based on the analysis of the prediction methods at home and abroad and according to the characteristics of the growth mechanism and the general signal processing method, a segmented short-term prediction method based on algal Shui Hua formation mechanism is proposed in this paper. At the same time, a mobile detection system with low cost, good portability, convenient and flexible operation and on-line detection of some water quality parameters is improved in view of the shortage of domestic and foreign mobile water quality detection system. Combining the segmented short-term prediction model based on algal Shui Hua formation mechanism with the water quality moving detection system, the real-time prediction of algae Shui Hua in different water areas of drinking water source (surface water) is realized. The main work and characteristics of this paper are as follows: (1) A segmented short-term prediction model based on algal Shui Hua formation mechanism is established. According to the process of algal Shui Hua formation, the key influencing factors of algal Shui Hua formation, namely, water temperature, light and nutrient, are determined. With reference to the "four-stage theory" of algal Shui Hua formation, this paper divides the whole year into three stages by month. A segmental mechanism model of algal Shui Hua formation was established by selecting the influence factors of different stages, and the simulation experiment of algal segmented short-term mechanism prediction model was completed according to the short-term growth trend of algae at a certain time in the future. The monitoring data from March to October 2000 of the Elbe River in Germany were used to verify the model and the model parameters were determined by using the particle swarm optimization algorithm (PSO). The prediction model for the concentration of chlorophyll a was analyzed from four angles: the time span of rate data, the combination of rate and parameter, the prediction sequence of parameter dynamic rate and the prediction sequence of future three days. The preliminary conclusion is that when the data span is 7 days, the optimal optical half-saturation constant K is obtained, and the effect of the ratio determination on the prediction results is better than that on the optical coefficient. (3) the error of prediction sequence is about 10, which indicates that the prediction model can be applied to the prediction of chlorophyll a concentration in the Elbe River of Germany from the next day to the next three days. The prediction error increases in turn. 3) the hardware and software of the mobile water quality detection system are improved. The system consists of three parts: mobile detection platform, monitoring center and handheld terminal. The mobile detection platform is used for on-line detection of water quality information in target waters, and the results are sent to the monitoring center and handheld terminal. In this paper, the remote detection of water temperature and light intensity is realized; the monitoring center is used to store water quality history data and realize algal Shui Hua prediction; and the handheld terminal is used to send corresponding control commands. The improved system supports on-line detection of various water quality parameters and carries image sensing module on the mobile detection platform to collect water sample images and quickly judge algae Shui Hua outbreak by water sample color.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：X84;X832

【參考文獻(xiàn)】

相關(guān)期刊論文 前9條

1 孫大朋;張祖陸;梁春玲;;水源富營(yíng)養(yǎng)化及藻類(lèi)控制技術(shù)[J];能源與環(huán)境;2006年03期

2 王小藝;唐麗娜;劉載文;崔莉鳳;許繼平;趙曉平;;城市湖庫(kù)藍(lán)藻水華形成機(jī)理[J];化工學(xué)報(bào);2012年05期

3 ;Application of Bayesian regularized BP neural network model for analysis of aquatic ecological data—A case study of chlorophyll-a prediction in Nanzui water area of Dongting Lake[J];Journal of Environmental Sciences;2005年06期

4 張勤;田增山;;INS/GPS/電子羅盤(pán)組合導(dǎo)航系統(tǒng)研究[J];計(jì)算機(jī)測(cè)量與控制;2010年05期

5 吳利斌,尚士友,岳海軍,馬清艷;利用模糊神經(jīng)網(wǎng)絡(luò)對(duì)湖泊富營(yíng)養(yǎng)化程度進(jìn)行評(píng)價(jià)的研究[J];內(nèi)蒙古農(nóng)業(yè)大學(xué)學(xué)報(bào)(自然科學(xué)版);2004年04期

6 徐禮強(qiáng),顧正華,樓章華,李紅仙;基于GIS的并行和分布式處理技術(shù)在水信息領(lǐng)域中的應(yīng)用[J];水利水電技術(shù);2005年10期

7 曾勇;楊志峰;劉靜玲;;城市湖泊水華預(yù)警模型研究——以北京“六�！睘槔齕J];水科學(xué)進(jìn)展;2007年01期

8 裴洪平,羅妮娜,蔣勇;利用BP神經(jīng)網(wǎng)絡(luò)方法預(yù)測(cè)西湖葉綠素a的濃度[J];生態(tài)學(xué)報(bào);2004年02期

9 張玉超;錢(qián)新;錢(qián)瑜;劉建萍;孔繁翔;;支持向量機(jī)在太湖葉綠素a非線(xiàn)性反演中的應(yīng)用[J];中國(guó)環(huán)境科學(xué);2009年01期

相關(guān)博士學(xué)位論文 前1條

1 趙曉東;河流藻類(lèi)葉綠素a濃度短時(shí)間尺度預(yù)測(cè)方法研究和應(yīng)用[D];浙江大學(xué);2014年

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