1) atmospheric pollution prediction
大气污染预报
1.
An SVM regression (SVMR) model for atmospheric pollution prediction is developed according to seven forecast factors, including the daily average pollutant concentration of previous day,daily average wind speed of previous day,etc.
利用前一天该污染物的日均浓度、前一天地面平均风速等7个预报因子建立了基于RBF核函数支持向量回归法的大气污染预报模型,并利用十重交叉验证和网格搜索法寻找模型最优参数。
2.
According to the deficiency of traditional BP model in the slow training speed, the local minimum and the uncertainty on the best structure of neural network, BP model is improved and applied to urban atmospheric pollution prediction.
计算结果表明 ,应用改进的BP模型进行大气污染预报能够得到更好的预测结果 ,具有很强的实用
2) air pollution forecasting
空气污染预报
1.
With the development of society and economy and the population expansion of the city, air pollution becomes more and more serious, therefore, it is significant to study air pollution forecasting so as to understand environmental variation trend, prevent and control air pollution.
本论文将人工神经网络技术应用到空气污染预报领域中,借助神经网络的非线性处理能力和容噪能力,设计出基于人工神经网络空气污染预报方法,为环境管理决策提供及时、准确、全面的环境质量信息。
3) air pollution forecast
空气污染预报
1.
A design idea on establishing a kind of applicable air pollution forecast system is proposed according to the existing bases and conditions and objectives.
在空气污染影响因素及其与环境空气质量定性关系分析基础上,通过对环境空气质量变化特征及统计、数值两种预报模型方法的对比分析,结合实际应用的目的要求及现有基础条件,提出了立足于业务化应用的空气污染预报模型系统的总体构想,并进行了实例分析。
2.
Based on the Regional Atmospheric Modeling System and by means of Grid Nesting technique, a nested numerical forecasting model system, the City-scale Meteorology Model(C-βM) nested to the Regional-scale Meteorology Model(R-αM), has been developed, which can supply meteorology data for air pollution forecast.
基于区域大气模拟系统 ,采用网格嵌套方法 ,发展了由区域尺度气象模式 (R αM )和城市尺度气象模式 (C βM )组成的嵌套模式预报系统 ,为空气污染预报提供必需的气象要素场。
3.
By the analysis of air pollutants (SO 2,NOx,TSP) data measured by the air pollution monitoring system in last three years and the meteorological data collected by Qingdao Meteorological Station in the corresponding period, the method of air pollution forecast was investigated.
利用青岛市空气自动监测系统近三年监测资料与同期气象资料,对青岛市空气污染预报方法进行了研究,通过因子初选和相关性分析,针对不同季节和不同污染物,应用逐步回归法,分别建立了青岛市的大气污染预方程,经检验其准确率在70% 左右。
4) air pollution prediction
空气污染预报
1.
The basic principle of BP neural network is briefly introduced, and the applications of BP neural network to air pollution prediction in China are reviewed in detail.
简单介绍了BP神经网络的基本原理,较详细地回顾了国内BP神经网络在空气污染预报领域的研究应用情况,并建立了苏州市区SO2浓度预报的BP神经网络,预报结果表明:该模型具有简便、快速、准确的优点,可推广用于其它空气污染物的预报。
2.
And the air pollution prediction is just this class of problem.
人工神经网络是20世纪80年代迅速兴起的一门非线性科学,特别适用于对具有多因素性、不确定性、随机性、非线性等特点的对象进行研究,而空气污染预报正是这样的一类问题。
5) pollution forecasting
污染预报
1.
Based on the analysis of main problem in existing methods for urban air pollution forecasting,a new idea based on Bayesian Network was proposed,with preliminary discussion of technical route as well as key challenges in putting the new idea into practice.
论述了当前国内外城市空气污染预报的主要方法以及存在的问题,探讨了一个基于贝叶斯网络的城市空气污染预报的新思路,并对其技术路线和关键问题等进行了初步探讨。
6) air pollution prediction
大气污染预测
1.
SVM used in the concentration prediction of air pollutant by the method, first selecting suitable information criteria to determine the order of the model, then choosing parameters by experiment to form the training sample set of SVM, and then the time series air pollution prediction model has been created based on SVM.
该方法将支持向量机应用于大气污染物浓度预测:首先通过选择合适的信息量准则来确定模型阶数;而后通过实验的方法选择参数从而形成支持向量机的训练样本集,在此基础上建立了基于支持向量机的时间序列大气污染预测模型。
补充资料：《大气污染物综合排放标准》(GB16297—1996)
《大气污染物综合排放标准》(GB16297—1996)
Integrated Emission Standard of Air Pollutants
、产切‘沪，‘，丫lO闪iwuronwuZonghe而而ngBicozhun《大气污染物综合排放标准)( GB 16297一19%)(加吨哪翻召动‘3fon及翔舰么rd ofA介护bll~)该标准于1卯7年l月1日起实施，是在原有工业“三废”排放试行标准废气部分和有关行业大气污染物排放标准的基础上制定，在技术内容上与原有各标准有一定的继承关系，亦有相当大的修改和变化。规定了二氧化硫、氮氧化物、颗粒物等33种大气污染物的排放限值，其指标体系为最高允许排放浓度、最高允许排放速率和无组织排放监控浓度限值。大气污染物综合排放标准主要控制对象为工业生产过程中排放的工艺尾气。按照综合性排放标准与行业性排放标准“不交叉执行”的原则，除锅炉、工业炉窑、火电厂、炼焦炉、水泥厂、恶臭物质、机动车执行各自的大气污染排放标准外，其他大气污染物排放均执行本标准。 (葛大陆膝静)
说明：补充资料仅用于学习参考，请勿用于其它任何用途。
参考词条