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灰色系统GM(1,1)残差模型在水质预测中的应用与探讨 总被引:1,自引:0,他引:1
近年来,灰色系统理论在环境预测方面已被较为广泛地应用,本文以青格达湖水质污染指标COD近十余年的监测数据作为原始数据.预测来来水质污染指标浓度值,探讨GM(1,1)模型在水质预测中的适用条件。1方法概述按X(0)的原始数据列得到下述GM(1,1)模型的时间响应函数。从模型得到的还原数据列为已知原始数据列为定义残差为如果取k—i.i+1…·.n,便有残差列q()一(q(0)(i).q(0(i+l)…·,q’0’(n》·对十”建立GM(1,1)模型.得时!司响应函数2计算步骤2.1建立GM(1,1)模型对原始数据列X”’作一次累加主成… 相似文献
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以北京市某典型区域作为研究对象,在收集大量相关资料与实测历史噪声数据的基础上,对研究区域内的声环境质量影响因素进行灰色关联度分析,并运用灰色理论建立GM(1,1)模型进行预测。结果表明,影响城市区域声环境质量因素从大到小的排序依次为:机动车辆﹥常住人口数量﹥平均车流量﹥地区生产总值﹥城市道路桥梁﹥基础设施投资﹥治理噪声环保投资;以研究区域内噪声污染实测历史数据建立的GM(1,1)模型精度符合要求标准,根据GM(1,1)模型预测北京市“十二五”期间声环境质量达标且有轻微下降趋势。 相似文献
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应用GM(1,1)模型,对大同市各功能区环境噪声进行综合预测,得出了大同市未来几年城市噪声的变化趋势。 相似文献
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利用唐山市1976-2005年各县年降水序列,分析了该市降水的空间分布规律和时间变化特点。采用灰色系统的灾变预测方法,对各县分别建立了GM(1,1)模型,进行未来25年唐山市各站的干旱年预测。利用残差检验、后验差检验和关联度检验对各模型分别进行了精度检验。结果表明,预测模型精度较高,可以对唐山市各县未来的干旱年进行预测,从而为科学决策提供依据。 相似文献
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从GM(1,1)的建模原理和模型结构特征出发,通过一系列的推导、变换、给出了直接从原始序列出发求解模型的方法,通过对某市废水排放量预测的应用建模计算比较,表明该方法是切实可行的。 相似文献
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建立了鱼类组织样品中有机磷阻燃剂(OPFRs)的分析方法,优化了萃取条件、净化条件、液相色谱条件和质谱检测参数。确定了生物样品经冷冻干燥后,以1∶1(V/V)的正己烷/二氯甲烷混合溶液进行加速溶剂萃取,利用氨基固相萃取柱进行净化,以1∶1(V/V)的正己烷/二氯甲烷、二氯甲烷和9∶1(V/V)的二氯甲烷/甲醇为洗脱液,最后用超高效液相色谱-串联质谱同时对9种OPFRs进行定性定量检测。结果表明,各物质的基质加标回收率均为56.5%~108%,方法的测定下限为0.016~0.104 ng/g (以脂重计),满足了生物样品中OPFRs的分析检测要求。利用该方法测定了北京和广州市养殖和野生鲫鱼肌肉组织样品,主要的OPFRs同族体为磷酸三正丁酯(TNBP)、磷酸三乙基己基酯(TEHP)、磷酸三(1-氯-2-丙基)酯(TCIPP)和磷酸三氯乙基酯(TCEP),质量比为5.94~33.7 ng/g(以脂重计),显示出良好的适用性。 相似文献
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2012年开展南通市1∶50000水文地质调查,对监测网内地下水进行采样分析,建立了基于液相色谱-电感耦合等离子体质谱联用测定地下水中砷形态的方法,该方法检出限为0.1μg/L。样品分析表明,南通地区地下水中砷的形态以三价无机砷为主,有机砷以砷胆碱(AsB)和乙基砷(DMA)为主,样品中未检出五价无机砷。通过砷形态研究表明,南通近海地区地下水中砷的质量浓度较低(<0.5μg/L),主要以 As(Ⅲ)为主,不存在其他砷的形态;近长江地区地下水中砷的质量浓度较高(>1.0μg/L),砷形态以 As(Ⅲ)和 DMA为主。 相似文献
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“3S”技术的城市环境监测与管理系统研究 总被引:11,自引:0,他引:11
基于遥感(RS)、地理信息系统(GIS)、全球定位系统(GPS)(简称“3S”技术),辅以常规环境监测与分析手段建立的城市环境监测缠环境保护与区域可持续发展强有力的技术支持,对以“3S”技术建立的城市环境监测与管理系统的构成及其应用进行了探讨。 相似文献
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对GM(1,1)模型经差分形式化及令定写成直线方程形式。据最小二乘法原理,原方程的参数辨识可借助线性回归方法来实现。从而使GM(1,1)模型的应用更显普及和实用化。 相似文献
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When a domestic wastewater treatment plant (DWWTP) is put into operation, variations of the wastewater quantity and quality must be predicted using mathematical models to assist in operating the wastewater treatment plant such that the treated effluent will be controlled and meet discharge standards. In this study, three types of gray model (GM) including GM (1, N), GM (1, 1), and rolling GM (1, 1) were used to predict the effluent biochemical oxygen demand (BOD), chemical oxygen demand (COD), and suspended solids (SS) from the DWWTP of conventional activated sludge process. The predicted results were compared with those obtained using backpropagation neural network (BPNN). The simulation results indicated that the minimum mean absolute percentage errors of 43.79%, 16.21%, and 30.11% for BOD, COD, and SS could be achieved. The fitness was higher when using BPNN for prediction of BOD (34.77%), but it required a large quantity of data for constructing model. Contrarily, GM only required a small amount of data (at least four data) and the prediction results were analogous to those of BPNN, even lower than that of BPNN when predicting COD (16.21%) and SS (30.11%). According to the prediction, results suggested that GM could predict the domestic effluent variation when its effluent data were insufficient. 相似文献
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This study compared three forecasting models based on the mean absolute percentage errors (MAPE) of their accuracy in forecasting
air pollution in a traffic tunnel: the Grey model (GM), the combination model used four sample point and five sample point
prediction with GM (1,1)(GM(1,1)4 + 5), and the modified grey model (MGM). An MGM was combined using the four points of the original sequence using the original
grey prediction GM (1,1) for short-term forecasting. The proposed method cannot only enhance the prediction accuracy of the
original grey model, but can also solve the jump data forecasting problem something for which the original grey model is inappropriate.
The MAPE was applied to the models, and the MGM found the proposed method to be simple and efficient. The MAPE of MGM, calculated
over 3 h of forecasts, were as follows: 10.12 (Upwind), 10.07 (Middle) and 7.68 (Downwind) for CO; 10.79 (Upwind), 6.05 (Middle)
and 5.98 (Downwind) for NO
x
, and 11.67 (Upwind), 7.32 (Middle) and 4.56 (Downwind) for NMHC. The MGM model results reveal that the combined forecasts
can significantly decrease the overall forecasting error. Results of this demonstrate that MGM can accurately forecast air
pollution in the Kaohsiung Chung–Cheng Tunnel. 相似文献
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In this study, Grey model (GM) and artificial neural network (ANN) were employed to predict suspended solids (SSeff) and chemical oxygen demand (CODeff) in the effluent from a wastewater treatment plant in industrial park of Taiwan. When constructing model or predicting, the influent quality or online monitoring parameters were adopted as the input variables. ANN was also adopted for comparison. The results indicated that the minimum MAPEs of 16.13 and 9.85% for SSeff and CODeff could be achieved using GMs when online monitoring parameters were taken as the input variables. Although a good fitness could be achieved using ANN, they required a large quantity of data. Contrarily, GM only required a small amount of data (at least four data) and the prediction results were even better than those of ANN. Therefore, GM could be applied successfully in predicting effluent when the information was not sufficient. The results also indicated that these simple online monitoring parameters could be applied on prediction of effluent quality well. 相似文献
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Christina L. L?jtnant Birte Boelt Sabine K. Clausen Christian Damgaard Per Kryger Ari Novy Marianne Philipp Cathrine H. Ingvordsen Rikke Bagger J?rgensen 《Environmental Modeling and Assessment》2012,17(4):421-430
The portion-dilution model is a parametric restatement of the conventional view of animal pollination; it predicts the level of pollinator-mediated gene dispersal. In this study, the model was applied to white clover (Trifolium repens) and its most frequent pollinator, the honeybee (Apis mellifera). One of the three parameters in the portion-dilution model is the mean number of flowers a pollinator visits in one foraging bout. An alternative method to estimate this parameter was developed that was not depending on pollinator hive-seeking behaviour. The new estimation method, based on nectar collection, seems to be a good alternative, when reliable observation on visiting behaviour of pollinators is not possible. The gene flow in white clover was modelled. Where fields were assumed to be well separated, and only a low fraction of bees travelled between fields, the gene flow was estimated to be 0.7%, but subjected to large uncertainty. In a worst case scenario with adjacent fields—one with a genetically modified (GM) T. repens cultivar and the other with a conventional T. repens cultivar—and where all arriving bees were expected to transfer GM pollen, the median gene flow was modelled to be 7% with an estimated 95% percentile of 70%. The results show that the European Union threshold limit of 0.9% GM admixture for food and feed will likely be exceeded at times and especially organic farmers that do not accept GM admixture and often have clover and clover–grass fields might face challenges with admixture of GM. 相似文献
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环境预测中的GAM模型 总被引:2,自引:0,他引:2
当原始序列属代数曲线型时,应用GAM模型可以提高精度,从而避免进行GM(1,1)模型的残差修正二次建模。从分析GM(1,1)模型的缺陷着手,结合实例详细介绍了GAM模型的思想形成和计算方法。 相似文献