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1.
人工神经网络在水环境质量评价中的应用   总被引:7,自引:0,他引:7  
为了将人工神经网络应用于水环境质量评价,应用了人工神经网络B—P算法,构造了水环境质量评价模型,该模型应用于实例评价结果表明,人工神经网络用于环境质量评价具有客观性,通用性和实用性。  相似文献
2.
Biodiversity studies in ecology often begin with the fitting and documentation of sampling data. This study is conducted to make function approximation on sampling data and to document the sampling information using artificial neural network algorithms, based on the invertebrate data sampled in the irrigated rice field.Three types of sampling data, i.e., the curve species richness vs. the sample size, the curve rarefaction, and the curve mean abundance of newly sampled species vs.the sample size, are fitted and documented using BP (Backpropagation) network and RBF (Radial Basis Function) network. As the comparisons, The Arrhenius model, and rarefaction model, and power function are tested for their ability to fit these data. The results show that the BP network and RBF network fit the data better than these models with smaller errors.BP network and RBF network can fit non-linear functions (sampling data) with specified accuracy and don't require mathematical assumptions. In addition to the interpolation, BP network is used to extrapolate the functions and the asymptote of the sampling data can be drawn. BP network cost a longer time to train the network and the results are always less stable compared to the RBF network. RBF network require more neurons to fit functions and generally it may not be used to extrapolate the functions. The mathematical function for sampling data can be exactly fitted using artificial neural network algorithms by adjusting the desired accuracy and maximum iterations. The total numbers of functional species of invertebrates in the tropical irrigated rice field are extrapolated as 140 to 149 using trained BP network, which are similar to the observed richness.  相似文献
3.
大气环境数据分析预测方法对比研究   总被引:3,自引:2,他引:1  
以西安市2006年9月27日至2008年5月3日每日的SO2平均浓度时间序列为例,应用时间序列分析对前555个数据进行拟合,得到合适的时间序列模型ARIMA(1,1,2);利用神经网络中的BP神经网络和RBF神经网络对同样的样本进行训练,用这三种方法对2008年4月4日至2008年5月3日的SO2日均浓度值进行了预测,并用同样的方法分析预测了同期PM10日均浓度值,最后比较了它们的预测效果。结果表明,利用这三种方法进行浓度预测都是可行的,其中RBF神经网络法的预测误差最小,效果最好。  相似文献
4.
This paper describes the development of artificial neural network (ANN) based carbon monoxide (CO) persistence (ANNCOP) models to forecast 8-h average CO concentration using 1-h maximum predicted CO data for the critical (winter) period (November–March). The models have been developed for three 8-h groupings of 10 p.m. to 6 a.m., 6 a.m. to 2 p.m. and 2–10 p.m., at two air quality control regions (AQCRs) in Delhi city, representing an urban intersection and an arterial road consisting heterogeneous traffic flows. The result indicates that time grouping of 2–10 pm is dominantly affected by inversion conditions and peak traffic flow. The ANNCOP model corresponding to this grouping predicts the 8-h average CO concentrations within the accuracy range of 68–71%. The CO persistence values derived from ANNCOP model are comparable with the persistence values as suggested by the Environmental Protection Agency (EPA), USA. This work demonstrates that ANN based model is capable of describing winter period CO persistence phenomena.  相似文献
5.
人工神经网络用于铅的化学形态模拟计算   总被引:1,自引:0,他引:1  
邓勃  莫华 《干旱环境监测》1996,10(3):155-162
用前馈线性网络法求解水体系中Pb(2+)与OH-之间的反应常数,不同训练算法对求解结果的精度、收敛速度及权值均有影响.结果表明,批处理算法的精度最好,权值不出现负值,但运算时间最长;在线算法的精度虽不如批处理算法,而比数据变换-在线算法好,权值有时会出现负值.运算时间较长;数据变换-在线算法的优点是运算时间短,但相对误差较大,权值出现负值的机会多。采用反馈网络模拟计算铅的各种化学形态的浓度.用物料核算的方法对反馈网络模型进行检验表明,此种模型用于平衡计算是可行的,详细分析了理论模拟和实验曲线的差异的原因,温度的影响最小,在4<pH<9时,CO有重要的影响.在国代检验时,n值取整所引入的误差的影响亦不可忽视。从本文的结果可以看到,采用前馈网络和反馈网络相结合的方法考察水体中的化学形态是可行的.从而为解决这一类问题提供了一种可能的途径.  相似文献
6.
Eco-environment quality evaluation is an important research theme in environment management. In the present study, Fuzhou city in China was selected as a study area and a limited number of 222 sampling field sites were first investigated in situ with the help of a GPS device. Every sampling site was assessed by ecological experts and given an Eco-environment Background Value (EBV) based on a scoring and ranking system. The higher the EBV, the better the ecological environmental quality. Then, three types of eco-environmental attributes that are physically-based and easily-quantifiable at a grid level were extracted: (1) remote sensing derived attributes (vegetation index, wetness index, soil brightness index, surface land temperature index), (2) meteorological attributes (annual temperature and annual precipitation), and (3) terrain attribute (elevation). A Back Propagation (BP) Artificial Neural Network (ANN) model was proposed for the EBV validation and prediction. A three-layer BP ANN model was designed to automatically learn the internal relationship using a training set of known EBV and eco-environmental attributes, followed by the application of the model for predicting EBV values across the whole study area. It was found that the performance of the BP ANN model was satisfactory and capable of an overall prediction accuracy of 82.4%, with a Kappa coefficient of 0.801 in the validation. The evaluation results showed that the eco-environmental quality of Fuzhou city is considered as satisfactory. Through analyzing the spatial correlation between the eco-environmental quality and land uses, it was found that the best eco-environmental areas were related to forest lands, whereas the urban area had the relatively worst eco-environmental quality. Human activities are still considered as a major impact on the eco-environmental quality in this area.  相似文献
7.
The impact of long-range transport of yellow sand from Asian Continent to the Taipei Metropolitan Area (Taipei) not only deteriorates air quality but also poses health risks to all, especially the children and the elderly. As such, it is important to assess the enhancement of PM10 during yellow sand periods. In order to estimate PM10 enhancement, we adopted factor analysis to distinguish the yellow-sand (YS) periods from non-yellow-sand (NYS) periods based on air quality monitoring records. Eight YS events were identified using factor analysis coupling with an independent validation procedure by checking background site values, examining meteorological conditions, and modeling air mass trajectory from January 2001 to May 2001. The duration of each event varied from 11 to 132 h, which was identified from the time when the PM10 level was high, and the CO and NO x levels were low. Subsequently, we used the artificial neural network (ANN) to simulate local PM10 levels from related parameters including local gas pollutants and meteorological factors during the NYS periods. The PM10 enhancement during the YS periods is then calculated by subtracting the simulated PM10 from the observed PM10 levels. Based on our calculations, the PM10 enhancement in the maximum hour of each event ranged from 51 to 82%. Moreover, in the eight events identified in 2001, it was estimated that a total amount of 7,210 tons of PM10 were transported by yellow sand to Taipei. Thus, in this study, we demonstrate that an integration of factor analysis with ANN model could provide a very useful method in identifying YS periods and in determining PM10 enhancement caused by yellow sand.  相似文献
8.
对富营养化评价标准进行插值获取大量的样本,建立了基于BP人工神经网络的富营养化评价模型。将模型应用于评价深圳市13座主要水库的富营养化状况,对其成因进行分析,并提出了对策与建议。研究结果表明,石岩水库与深圳水库为轻度富营养化,占评价水库总数的15.4%;西丽水库等11座水库为中营养,占评价水库总数的84.6%。人工神经网络用于建立湖库富营养评价模型是适合的。  相似文献
9.
智能算法具有学习非线性问题的能力,可有效优化环境模型结构与参数,是环境预警的重要工具。重点分析了遗传算法和人工神经网络的相关特征,并以太湖蓝藻水华预报预警为例,介绍其在提高环境模型精度中的应用。  相似文献
10.
水质遥感技术在湖泊水质监测领域内的应用具有十分积极的意义。在总结现有水质遥感反演方法的基础上,选取了遥感指数法和神经网络法两种理论完全不同的反演方法,构建太湖叶绿素a与MODIS影像波段间的函数关系,并从反演能力和反演精度两个角度对上述方法进行了比较研究。结果表明,神经网络模型的非线性特征能够敏感地把握住叶绿素a浓度变化在反射波谱信息上的微小响应,较为成功地反演出叶绿素a与反射光谱信息间的非线性关系。神经网络模型的反演能力和反演精度均优于遥感指数方法,具有较好的应用前景。  相似文献
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