灰色聚类法在湿地水体富营养化评价中的应用

将灰色聚类法应用到湿地水体富营养化评价中，旨在建立起一套比较完善、适合于湿地水体富营养化的评价体系。把湿地水体富营养化程度划分为6个级别，其阚值参照中国湖泊富营养化评价标准和湿地富营养化的特点。利用灰类白化权函数描述水体富营养化分级界限，正确地选择了各参评指标对于各等级的聚类权，在此基础上构建了湿地水体富营养化灰色聚类法综合评判模型。并以扎龙湿地为例进行了实例计算与分析，验证了此模型的准确性、可比性和实用性。     

基于灰色理论的湖库水体富营养化预测方法研究

在对湖库水体富营养化形成机理深入分析的基础上,将灰色理论引入到水体富营养化预测中.首先采用综合指数法对水体富营养化程度进行综合评价,将评价结果作为灰色预测模型的输入参量,以提高模型预测精度;同时针对传统灰色模型内在的缺陷进行改进,并将改进模型应用于对湖库水体富营养化的预测中,仿真结果表明改进模型相对于传统模型预测效果更好,关联度显著提高,进而为水体富营养化预测提供了一条有效途径.     

一种针对GAN生成的天文图像评价方法研究

为定量评价使用生成对抗网络生成的恒星和星系图像的质量,提出了感知损失函数与Mode score相结合的双样本评价方法。通过对几种常用的生成图像定量评价方法的优缺点的分析以及对感知损失函数进行的改进,得到了能够定量评价生成图像多样性和视觉质量的双样本评价模型。为了验证提出方法的有效性,分别采用Inception score,Kernal MMD,Wasse-rstein distance及双样本评价方法对生成的两种分辨率的恒星和星系图像进行评价,并将评价结果进行了对比分析。结果表明双样本评价方法能够全面且客观的评价生成的恒星和星系图像,对利用生成的天文图像预测未知的恒星和星系具有重要意义。     

改进赋权的水体富营养化多维云模型评价

针对现有水体富营养化评价方法中存在的不确定性,以及在确定多评价指标权重过程中无法进行科学解释的问题,本文结合主观赋权法AHP和客观赋权法CRITIC的优势,提出了一种多评价指标综合赋权法,并根据水体富营养化评价标准的非线性特性,将该方法应用于多维正态云模型中。首先,采用主观赋权法AHP参考专家意见,对各水质指标的重要性给出判定,并完成定性与定量之间的转换;然后,采用客观赋权法CRITIC在对大量数据分析的基础上,利用指标的相关性和对比强度显示指标重要性。将2种方法利用综合权重确定公式结合后,用于改进多维正态云模型的权重分配,并基于云模型的期望、熵和超熵的概念对湖库水体富营养化进行评价,进而准确获取水质的富营养化级别。该方法不但解决了水质评价中的随机性和模糊性两大问题,同时还能够充分考虑各评价因素所含信息对结果的影响,将本文方法应用至全国中小型湖库进行验证,结果表明评价等级与湖库实质水质状况相符。     

湖库水质监测与分析智能仪表设计与开发

关于湖库水质优质监测问题.针对目前水质监测落后和水体富营养化评价困难的现状,研发了GPRS的水质监测与分析智能仪表.仪表采用WinCE操作系统,用GPS地理定位、GPRS通用遥感数据通信于一体,实现了对现场水质信息的实时采集、历史查询与远程传输等功能.同时,根据水体富营养化形成机理特征,采用灰色关联分析方法综合多项水体富营养化相关因素,对水体富营养化程度进行评价和分级,进而为水环境保护提供了一种新的应用平台.     

前向神经网络参数估计中的进化规划

人工神经网络在很多领域有着成功的应用。神经网络参数估计有许多训练算法,BP算法是前向多层神经网络的典型算法,但BP算法有时会陷入局部最小解。进化规划是一种随机优化技术,它可以发现全局最优解。文章介绍了进化规划在前向多层神经网络参数估计中的应用,结合具体例子给出了算法实现的具体操作步骤和实验结果。实验数据表明采用进化规划得到的网络参数是最优的,神经网络的性能优于基于BP算法的神经网络性能。     

基于神经网络的热电偶特性数学模型

简单介绍了当前热电偶应用存在的问题 ,分析了建立热电偶特性的数学模型的各种方法 ,并提出了应用前向多层神经网络建立热电偶特性数学模型的方法及其优势。文中对前向多层神经网络及其算法进行了深入剖析 ,并给出了MATLAB源代码。最后以镍铬 -镍硅热电偶为例 ,阐述了其特性数学模型的建立过程及其应用。     

基于物联网的无人船水质采样系统

我国拥有众多河流湖泊,在工业化发展迅速的背景下,水污染成为了不可忽视的问题。为了有效地保护水资源,定期对河流湖泊等水域进行水体参数监测和水质样本采样研究尤为重要。目前,国内外在无人船水环境监测领域取得一些成果,通过安装在船体底部的检测装置周期性地收集传感数据,发送给地面基站。然而,针对某些水污染问题,需要在水域内采集多处样本进行专业研究。现有的无人船少有自动采水功能,且难以一次性采集多处水体样本。为此,本文设计研究了一种基于物联网的水体采样的无人船系统,可以实现多点水体样本采集,提高水体采样效率。     

富营养化水体环境中COD浓度遥感监测系统设计

对富营养化水体环境中COD浓度进行遥感监测,可以预防富营养化水体环境中COD浓度增加,提高水体水质,增加水循环次数,减少水体中有机物的污染;当前富营养化水体环境中COD浓度遥感监测系统设计方法,以Modis遥感影像数据为原理,依据富营养化水体环境中COD浓度的特征提取结果,对富营养化水体环境中COD浓度进行遥感监测,没有具体对遥感监测系统进行详细地设计,无法获取富营养化水体环境中COD浓度高精度的遥感监测信息,存在遥感监测结果偏差大的问题;提出了一种基于Zigbee的富营养化水体环境中COD浓度遥感监测系统设计方法;该方法先对Zigbee的富营养化水体环境中COD浓度遥感监测系统进行硬件设计,采用IMF对富营养化水体环境中COD浓度进行特征提取,以特征提取结果为基础,依据COD浓度指数时间序列实现富营养化水体环境中COD浓度遥感监测,最后利用Retinex法对COD浓度遥感监测的图像进行处理,完成对富营养化水体环境中COD浓度的遥感监测;仿真实验结果证明,所提系统设计方法可以精确地对富营养化水体环境中COD浓度进行安全快速的遥感监测。     

基于人工神经网络的主机安全量化评估研究

基于人工神经网络的方法对主机安全性能进行量化评估。分析了BP人工神经网络模型的网络结构及学习算法,分析了影响目标主机安全性能的可能因素,并应用BP神经网络模型对目标主机的安全性能进行样本训练及实际测试。基于人工神经网络的主机安全量化评估为评价目标主机的安全性能提供了可行的方法。     

基于模糊概念层核属性集确定算法及仿真研究

大型数据库中核属性集的确定是生成分类规则算法的重要步骤。该文在结合粗糙集理论和层次聚类方法的基础上,提出运用模糊概念层方法对原始数据进行模糊化处理,排除边缘数据的干扰作用并简化可区分矩阵的构造,从而得出核属性集。并以我国湖库在藻类繁殖高峰期实测浓度数据为例,快速、准确地得出湖库富营养化的主要指标。仿真试验结果表明,该算法简单,易于实现,在正确得出核属性集的基础上可以减少计算量,从而较为快速有效地得出推理规则。     

A parameterized model of heat storage by lake sediments

A model of seasonal heat storage by lake sediments is proposed oriented at applications in climate modeling and at lake parameterization in numerical weather prediction. The computational efficiency is achieved by reformulating of the heat transfer problem as a set of ordinary differential equations for evolution of the temperature wave inside the upper sediment layer. Arising temperature and depth scales completely replace the conductivity of the sediment in the heat transfer equation and can be easily achieved from the lake water temperature observations without any data on the sediment thermal properties. The method is proposed for the scales estimation from the inverse solution of the model equations in special case of the constant water-sediment heat flux in ice-covered lakes. The method is tested on data from sediments of Lake Krasnoye, North-Western Russia. The long-term (1961–2002) modeling of temperature in German lakes Müggelsee and Heiligensee with a coupled one-dimensional model of lake water column and sediments has demonstrated an appreciable effect of the sediment heat storage on near-bottom temperatures in both lakes. Thus, incorporation of the sediment layer into lake temperature models can essentially improve, at low computational costs, the model performance, especially for shallow lakes. In addition, a better forecast of near-bottom temperature evolution on climatic scales can provide a better understanding of the response of lake benthic communities to global warming.     

Correcting for the influence of frozen lakes in satellite microwave radiometer observations through application of a microwave emission model

The spatial resolution of passive microwave observations from space is of the order of tens of kilometers with currently available instruments, such as the Special Sensor Microwave/Imager (SSM/I) and Advanced Microwave Scanning Radiometer (AMSR-E). The large field of view of these instruments dictates that the observed brightness temperature can originate from heterogeneous land cover, with different vegetation and surface properties.In this study, we assess the influence of freshwater lakes on the observed brightness temperature of AMSR-E in winter conditions. The study focuses on the geographic region of Finland, where lakes account for 10% of the total terrestrial area. We present a method to mitigate for the influence of lakes through forward modeling of snow covered lakes, as a part of a microwave emission simulation scheme of space-borne observations. We apply a forward model to predict brightness temperatures of snow covered sceneries over several winter seasons, using available data on snow cover, vegetation and lake ice cover to set the forward model input parameters. Comparison of model estimates with space-borne observations shows that the modeling accuracy improves in the majority of examined cases when lakes are accounted for, with respect to the case where lakes are not included in the simulation. Moreover, we present a method for applying the correction to the retrieval of Snow Water Equivalent (SWE) in lake-rich areas, using a numerical inversion method of the forward model. In a comparison to available independent validation data on SWE, also the retrieval accuracy is seen to improve when applying the influence of snow covered lakes in the emission model.     

Study on Lake Izvorul Muntelui rehabilitation

In the actual economical and ecological context the rehabilitation of aquatic ecosystems represents one of the most important concerns all over the world. The physical, chemical and biological processes that take place inside a lake are very complex. A simple analysis of the field data is not enough. Even if those dates offers an appreciation of the global functioning of the ecosystem under the influence of certain factors, it does not allow to predict the system evolution, the optimization of the water arrangement, in order to respect and to assure the water quality criteria or to propose solutions for diminishing the lakes eutrophication.In the recent years, Lake Izvorul Muntelui, which is the largest artificial lake on the interior waters of Romania, experienced eutrophication problems. This paper analyzes the apparition condition, its effects onto water quality in the lake and the solutions for the rehabilitation of the ecosystem. Since systematic measured data are not available for this ecosystem, the mathematical modelling it is used.The simulation is done by considering the ecosystem dynamic and the stipulation of the spatial and temporal variation domains for the natural processes to appear and act onto perturbation factors to modify the ecological succession.Thus, different scenarios will be created allowing us to offer solutions for the eutrophication phenomenon and, in the same time, to identify solutions for the rehabilitation of the Izvorul Muntelui Lake.     

青藏高原不同气候子区典型湖泊多时间尺度变化的遥感对比研究

青藏高原湖泊作为气候变化的重要指示器,监测高原湖泊水位变化对于准确评估该地区的气候及其对周围水文与环境的影响至关重要.而由于青藏高原地理环境复杂且恶劣,难以对湖泊进行长时间、连续的实地观测,但遥感技术的发展弥补了这个不足.利用多源测高卫星数据可以有效地监测湖泊水位长时序连续变化,促进对青藏高原湖泊气候变化响应特征的理解...     

湖泊藻类水华神经网络预测研究

在分析湖库藻类水华形成机理特征基础上,确定湖库水华形成关键影响因素,即总氮、总磷、化学需氧量、温度与叶绿素a等,结合关注区域的遥感监测信息,构建基于神经网络的藻类水华预测模型,实现对湖库藻类水华的有效预测,通过仿真验证该方法的有效性,为湖库藻类水华预测提供一种有效途径。     

Estimation of chlorophyll‐a concentration and trophic states for inland lakes in Northeast China from Landsat TM data and field spectral measurements

Landsat TM data and field spectral measurements were used to evaluate chlorophyll‐a (Chl‐a) concentration levels and trophic states for three inland lakes in Northeast China. Chl‐a levels were estimated applying regression analysis in the study. The results obtained from the field reflectance spectra indicate that the ratio between the reflectance peak at 700 nm and the reflectance minimum at 670 nm provides a relatively stable correlation with Chl‐a concentration. Their determination of coefficients R ² is 0.69 for three lakes in the area. From Landsat TM data, the results show that the most successful Chl‐a was estimated from TM3/TM2 with R ² = 0.63 for the two lakes on 26 July 2004, from TM4/TM3 with R ² = 0.89 for the two lakes on 14 October 2004, and from the average of TM2, TM3 and TM4 with R ² = 0.72 for the three lakes tested on 13 July 2005. These results are applicable to estimate Chl‐a from satellite‐based observations in the area. We also evaluate the trophic states of the three lakes in the region by employing Shu's modified trophic state index (TSI_M) for the Chinese lakes' eutrophication assessment. Our study presents the TSI_M from different TM data with R ² more than 0.73. The study shows that satellite observations are effectively applied to estimate Chl‐a levels and trophic states for inland lakes in the area.     

New unstructured mesh water quality model for coastal discharges

A new unstructured mesh coastal water and air quality model has been developed that includes species transport, nonlinear decay, byproduct formation and mass-exchange between sea and atmosphere. The model has been programmed with a graphical interface and is applicable to coastal seawater, lakes and rivers. Focused on species conversion and interaction with the atmosphere, the water and air quality model follows a modular approach. It is a compatible module which simulates distributions based on fluid dynamic field data of underlying existing hydrodynamic and atmospheric simulations. Nonlinear and spline approximations of decay and growth kinetics, byproduct formation and joint sea–atmosphere simulation have been embedded. The Windows application software includes functions allowing error analysis concerning mesh and finite volume approximation. In this work, an anoxic submerged process-water discharge has been simulated. An error analysis has been carried out by varying vertical meshing, time-steps and comparing results based on explicit and implicit finite volume approximation.