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本文系统地介绍了有关水环境主要的形态模型、表面络合模型、点源和非点源污染模型及其计算机模型软件,并着重阐述了生态模型的发展及应用等 相似文献
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本文将酸化模型分为指标评价模型、经验酸化模型和以湖泊-流域为基础的机理模型三大类。概述了几个典型的酸化模型的结构、对参数和过程的处理方法、存在的问题和应用范围并介绍了它们的一些具体应用。还以MAGIC模型为例说明了酸化模型的发展及其趋势。作为模型的一个有机组成部分,本文还阐述了酸化模型的不确定性分析。 相似文献
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非点源污染模型参数数字化及不确定性研究进展 总被引:1,自引:0,他引:1
非点源污染模型是非点源污染量化研究的重要内容.目前,非点源污染模型数量繁多,集总模型不考虑时空变异性,适用流域面积小;分布式模型利用网格划分流域,可以模拟时空变异性,但参数繁多、率定困难、精度达不到要求、难以收集与管理.而全球定位系统、地理信息系统和遥感(合称3S)技术的应用,可以解决参数的选择问题,减少模型中的不确定成分.因此,在未来的非点源污染模型研究中,应重点关注利用3S技术解决参数的选择问题,以及模型参数的敏感性分析和不确定性分析.概述了非点源污染模型的研究进展,重点介绍了3S技术在非点源污染模型中的应用和非点源污染模型中的不确定性分析. 相似文献
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基于空气质量数据不足及波动较大的情况,将灰色GM(1,1)模型与人工神经网络模型组合并改进,建立改进型灰色神经网络组合模型。利用天津市2001—2008年PM10、SO2和NO2年均值作为原始数据预测2009—2010年PM10、SO2和NO2的浓度以进行模型精度检验,最后利用该模型预测2011—2015年天津市空气质量状况。结果表明,与灰色GM(1,1)模型、传统灰色神经网络组合模型相比,所建立的改进型灰色神经网络组合模型相对模拟误差小,预测结果更为可靠,可以用于空气质量预测。 相似文献
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为探索河流水质模型参数新的求解方法,根据有限的实测数据,分别应用免疫进化优化算法和免疫进化优选的捕食搜索算法,对河流水质模型计算公式中的多参数进行优化。将优化得到的计算公式用于国内外若干河流的河段中DO浓度值的拟合,并与实测结果进行了比较。结果表明,将免疫进化优化算法或免疫进化优选的捕食搜索算法优化得到的水质模型参数精度不仅较高,而且相对稳定,从而为河流水质模型参数的优化提供了一种新方法。 相似文献
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针对水质预测中传统BP神经网络模型收敛速度慢,对隐层结点选取缺乏有效的手段等问题,引入了遗传算法优化BP网络的结构和隐层神经元阈值和连接权值,通过设计灵活的实数编码方案和新型交叉算子等,对实数编码遗传算法进行改进,在此基础上,提出了一种基于改进的实数编码遗传算法优化BP神经网络(IGA-BP)的水质预测新模型,并以安徽蚌埠蚌埠闸逐周水质监测的PH值数据为例,进行水质预测,通过与传统的GA-BP神经网络以及BP神经网络的水质预测模型对比,结果表明,这种预测方法训练的BP神经网络收敛速度快,样本逼近精度高且泛化能力强。 相似文献
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免疫粒子群算法优化的环境空气质量评价方法 总被引:1,自引:0,他引:1
为了提高免疫算法的收敛速度,将粒子群优化思想引入到免疫算法中,设计了一种免疫粒子群优化算法。采用该算法对大气污染损害公式的参数进行寻优,得到了适用于臭氧、PM2.5等6种大气污染物的环境空气质量评价的污染损害指数公式及环境空气质量评价模型。为了使评价结果更准确,采用了国家环保部最新发布的空气质量标准中给出的大气污染物种类、数目及各级浓度限值。将该评价方法应用于大气质量评价领域,实验结果表明,该方法评价结果准确,具有较好的灵活性、实用性和应用前景。 相似文献
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随着自动监测、网络通讯等技术的迅速发展,水质数据采集方式从原来的人工采集发展到现在的自动采集,技术上得到很大的进步,同时获得的水质数据也急剧增加。因此面对大量的水质数据,迫切需要一种能够处理大规模水质数据的预测方法。针对这一问题,基于k最近邻算法和分段线性表示算法,提出了分段线性表示k最近邻算法用于水质预测。为了验证所提出算法的有效性,利用该算法对2个水库进行水质浑浊度预测实验。实验结果表明,分段线性表示k最近邻算法处理大规模水质数据时可以有效减少计算量和运行时间,且预测效果令人满意。 相似文献
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Water quality forecasting in agricultural drainage river basins is difficult because of the complicated nonpoint source (NPS) pollution transport processes and river self-purification processes involved in highly nonlinear problems. Artificial neural network (ANN) and support vector model (SVM) were developed to predict total nitrogen (TN) and total phosphorus (TP) concentrations for any location of the river polluted by agricultural NPS pollution in eastern China. River flow, water temperature, flow travel time, rainfall, dissolved oxygen, and upstream TN or TP concentrations were selected as initial inputs of the two models. Monthly, bimonthly, and trimonthly datasets were selected to train the two models, respectively, and the same monthly dataset which had not been used for training was chosen to test the models in order to compare their generalization performance. Trial and error analysis and genetic algorisms (GA) were employed to optimize the parameters of ANN and SVM models, respectively. The results indicated that the proposed SVM models performed better generalization ability due to avoiding the occurrence of overtraining and optimizing fewer parameters based on structural risk minimization (SRM) principle. Furthermore, both TN and TP SVM models trained by trimonthly datasets achieved greater forecasting accuracy than corresponding ANN models. Thus, SVM models will be a powerful alternative method because it is an efficient and economic tool to accurately predict water quality with low risk. The sensitivity analyses of two models indicated that decreasing upstream input concentrations during the dry season and NPS emission along the reach during average or flood season should be an effective way to improve Changle River water quality. If the necessary water quality and hydrology data and even trimonthly data are available, the SVM methodology developed here can easily be applied to other NPS-polluted rivers. 相似文献
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Cost-effective sampling network design for contaminant plume monitoring under general hydrogeological conditions 总被引:6,自引:0,他引:6
A new simulation-optimization methodology is developed for cost-effective sampling network design associated with long-term monitoring of large-scale contaminant plumes. The new methodology is similar in concept to the one presented by Reed et al. (Reed, P.M., Minsker, B.S., Valocchi, A.J., 2000a. Cost-effective long-term groundwater monitoring design using a genetic algorithm and global mass interpolation. Water Resour. Res. 36 (12), 3731-3741) in that an optimization model based on a genetic algorithm is coupled with a flow and transport simulator and a global mass estimator to search for optimal sampling strategies. However, this study introduces the first and second moments of a three-dimensional contaminant plume as new constraints in the optimization formulation, and demonstrates the proposed methodology through a real-world application. The new moment constraints significantly increase the accuracy of the plume interpolated from the sampled data relative to the plume simulated by the transport model. The plume interpolation approaches employed in this study are ordinary kriging (OK) and inverse distance weighting (IDW). The proposed methodology is applied to the monitoring of plume evolution during a pump-and-treat operation at a large field site. It is shown that potential cost savings up to 65.6% may be achieved without any significant loss of accuracy in mass and moment estimations. The IDW-based interpolation method is computationally more efficient than the OK-based method and results in more potential cost savings. However, the OK-based method leads to more accurate mass and moment estimations. A comparison of the sampling designs obtained with and without the moment constraints points to their importance in ensuring a robust long-term monitoring design that is both cost-effective and accurate in mass and moment estimations. Additional analysis demonstrates the sensitivity of the optimal sampling design to the various coefficients included in the objective function of the optimization model. 相似文献
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在含油污泥进行资源化处理过程中,针对处理目标受多个因素影响的实际,为了解决工艺之间的耦合问题,采用正交实验的方法来解决,并把主要参数作为优化对象,把含油污泥的脱水率作为评价目标,通过采用GA-BP算法对含油污泥耦合工艺正交实验参数进行了线性与非线性分析.在采用遗传算法优化神经网络的权值和阈值的基础上,用优化后的权值和阈值对测试样本和训练样本进行了预测.预测结果表明,预测误差都有明显减小,分别由0.34211减少到0.031549和0.15476减少到0.040682,可见耦合参数趋向于非线性优化. 相似文献
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Loughlin DH Ranjithan SR Baugh JW Brill ED 《Journal of the Air & Waste Management Association (1995)》2000,50(6):1050-1063
Designing air quality management strategies is complicated by the difficulty in simultaneously considering large amounts of relevant data, sophisticated air quality models, competing design objectives, and unquantifiable issues. For many problems, mathematical optimization can be used to simplify the design process by identifying cost-effective solutions. Optimization applications for controlling nonlinearly reactive pollutants such as tropospheric ozone, however, have been lacking because of the difficulty in representing nonlinear chemistry in mathematical programming models. We discuss the use of genetic algorithms (GAs) as an alternative optimization approach for developing ozone control strategies. A GA formulation is described and demonstrated for an urban-scale ozone control problem in which controls are considered for thousands of pollutant sources simultaneously. A simple air quality model is integrated into the GA to represent ozone transport and chemistry. Variations of the GA formulation for multiobjective and chance-constrained optimization are also presented. The paper concludes with a discussion of the practically of using more sophisticated, regulatory-scale air quality models with the GA. We anticipate that such an approach will be practical in the near term for supporting regulatory decision-making. 相似文献