共查询到20条相似文献,搜索用时 31 毫秒
1.
人工湿地的去污机理复杂、呈高度非线性,故利用神经网络技术构建模型预测其长期运行效果。通过构建人工湿地复合基质模拟槽系统进行为期4个月的实验,监测得到56组COD去除率数据样本,经Matlab小波去噪后分别利用RBF和Elman网络构建动态神经网络模型,预测该系统对生活污水中COD去除效果。结果表明,RBF和Elman神经网络预测值的均方根误差分别为0.0186和0.0163,精度较高,该系统后期的COD去除率在49.4%~59.0%之间。 相似文献
2.
Richard W.Tock 《Journal of the Air & Waste Management Association (1995)》2013,63(10):953-962
Abstract Neural networks have shown tremendous promise in modeling complex problems. This work describes the development and validation of a neural network for the purpose of estimating point source emission rates of hazardous gases. This neural network approach has been developed and tested using experimental data obtained for two specific air pollutants of concern in West Texas, hydrogen sulfide and ammonia. The prediction of the network is within 20% of the measured emission rates for these two gases at distances of less than 50 m. The emission rate estimations for ground level releases were derived as a function of seven variables: downwind distance, crosswind distance, wind speed, downwind concentration, atmospheric stability, ambient temperature, and relative humidity. A backpropagation algorithm was used to develop the neural network and is also discussed here. The experimental data were collected at the Wind Engineering Research Field Site located at Texas Tech University in Lubbock, Texas. Based on the results of this study, the use of neural networks provides an attractive and highly effective tool to model atmospheric dispersion, in which a large number of variables interact in a nonlinear manner. 相似文献
3.
Dimopoulos IF Tsiros IX Serelis K Kamoutsis A Chronopoulou A 《Journal of the Air & Waste Management Association (1995)》2003,53(4):396-405
Various statistical models were developed for assessing airborne fluoride (F) levels in natural vegetation near an aluminum reduction plant using as predictor variables the distance from the emission source, the predominating wind, and characteristic topography-geomorphology parameters. Results revealed that F concentrations in vegetation showed a predictable response to both wind conditions and landscape features. The linear model was found to give good estimations, taking advantage of the relatively strong linear correlation between concentration and distance. A nonlinear relationship between the F concentration in vegetation and the other variables was also found, while interactions between the variables were found to be non-first-order. The nonlinear relationship hypothesis was supported by the improved results of various nonlinear models that also indicated the importance of the area's topography-geomorphology and meteorology in model predictions. The application of an artificial neural network (ANN) model showed the closest agreement between predicted and observed values with a correlation coefficient of 0.92. The improved reliability of the ANN and a regression tree model (RTM) also were indicated by the normal distribution of their residuals. The RTM and the ANN were further investigated and found to be capable of identifying the importance of the variables in vegetation exposure to air emissions. 相似文献
4.
Archontoula Chaloulakou Georgios Grivas Nikolas Spyrellis 《Journal of the Air & Waste Management Association (1995)》2013,63(10):1183-1190
Abstract Particulate atmospheric pollution in urban areas is considered to have significant impact on human health. Therefore, the ability to make accurate predictions of particulate ambient concentrations is important to improve public awareness and air quality management. This study examines the possibility of using neural network methods as tools for daily average particulate matter with aerodynamic diameter <10 µm (PM10) concentration forecasting, providing an alternative to statistical models widely used up to this day. Based on a data inventory, in a fixed central site in Athens, Greece, ranging over a two-year period, and using mainly meteorological variables as inputs, neural network models and multiple linear regression models were developed and evaluated. Comparison statistics used indicate that the neural network approach has an edge over regression models, expressed both in terms of prediction error (root mean square error values lower by 8.2–9.4%) and of episodic prediction ability (false alarm rate values lower by 7–13%). The results demonstrate that artificial neural networks (ANNs), if properly trained and formed, can provide adequate solutions to particulate pollution prognostic demands. 相似文献
5.
《Atmospheric environment (Oxford, England : 1994)》2001,35(5):815-825
Forecasting of air quality parameters is one topic of air quality research today due to the health effects caused by airborne pollutants in urban areas. The work presented here aims at comparing two principally different neural network methods that have been considered as potential tools in that area and assessing them in relation to regression with periodic components. Self-organizing maps (SOM) represent a form of competitive learning in which a neural network learns the structure of the data. Multi-layer perceptrons (MLPs) have been shown to be able to learn complex relationships between input and output variables. In addition, the effect of removing periodic components is evaluated with respect to neural networks. The methods were evaluated using hourly time series of NO2 and basic meteorological variables collected in the city of Stockholm in 1994–1998. The estimated values for forecasting were calculated in three ways: using the periodic components alone, applying neural network methods to the residual values after removing the periodic components, and applying only neural networks to the original data. The results showed that the best forecast estimates can be achieved by directly applying a MLP network to the original data, and thus, that a combination of the periodic regression method and neural algorithms does not give any advantage over a direct application of neural algorithms. 相似文献
6.
Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends 总被引:2,自引:0,他引:2
Monitoring and forecasting of air quality parameters are popular and important topics of atmospheric and environmental research today due to the health impact caused by exposing to air pollutants existing in urban air. The accurate models for air pollutant prediction are needed because such models would allow forecasting and diagnosing potential compliance or non-compliance in both short- and long-term aspects. Artificial neural networks (ANN) are regarded as reliable and cost-effective method to achieve such tasks and have produced some promising results to date. Although ANN has addressed more attentions to environmental researchers, its inherent drawbacks, e.g., local minima, over-fitting training, poor generalization performance, determination of the appropriate network architecture, etc., impede the practical application of ANN. Support vector machine (SVM), a novel type of learning machine based on statistical learning theory, can be used for regression and time series prediction and have been reported to perform well by some promising results. The work presented in this paper aims to examine the feasibility of applying SVM to predict air pollutant levels in advancing time series based on the monitored air pollutant database in Hong Kong downtown area. At the same time, the functional characteristics of SVM are investigated in the study. The experimental comparisons between the SVM model and the classical radial basis function (RBF) network demonstrate that the SVM is superior to the conventional RBF network in predicting air quality parameters with different time series and of better generalization performance than the RBF model. 相似文献
7.
Chaloulakou A Grivas G Spyrellis N 《Journal of the Air & Waste Management Association (1995)》2003,53(10):1183-1190
Particulate atmospheric pollution in urban areas is considered to have significant impact on human health. Therefore, the ability to make accurate predictions of particulate ambient concentrations is important to improve public awareness and air quality management. This study examines the possibility of using neural network methods as tools for daily average particulate matter with aerodynamic diameter <10 microm (PM10) concentration forecasting, providing an alternative to statistical models widely used up to this day. Based on a data inventory, in a fixed central site in Athens, Greece, ranging over a two-year period, and using mainly meteorological variables as inputs, neural network models and multiple linear regression models were developed and evaluated. Comparison statistics used indicate that the neural network approach has an edge over regression models, expressed both in terms of prediction error (root mean square error values lower by 8.2-9.4%) and of episodic prediction ability (false alarm rate values lower by 7-13%). The results demonstrate that artificial neural networks (ANNs), if properly trained and formed, can provide adequate solutions to particulate pollution prognostic demands. 相似文献
8.
Forecasts using neural network versus Box-Jenkins methodology for ambient air quality monitoring data 总被引:5,自引:0,他引:5
This study explores ambient air quality forecasts using the conventional time-series approach and a neural network. Sulfur dioxide and ozone monitoring data collected from two background stations and an industrial station are used. Various learning methods and varied numbers of hidden layer processing units of the neural network model are tested. Results obtained from the time-series and neural network models are discussed and compared on the basis of their performance for 1-step-ahead and 24-step-ahead forecasts. Although both models perform well for 1-step-ahead prediction, some neural network results reveal a slightly better forecast without manually adjusting model parameters, according to the results. For a 24-step-ahead forecast, most neural network results are as good as or superior to those of the time-series model. With the advantages of self-learning, self-adaptation, and parallel processing, the neural network approach is a promising technique for developing an automated short-term ambient air quality forecast system. 相似文献
9.
Paul J. Lioy Michael Avdenko Ronald Harkov Thomas Atherholt Joan M. Daisey 《Journal of the Air & Waste Management Association (1995)》2013,63(6):653-657
Abstract Many large metropolitan areas experience elevated concentrations of ground-level ozone pollution during the summertime “smog season”. Local environmental or health agencies often need to make daily air pollution forecasts for public advisories and for input into decisions regarding abatement measures and air quality management. Such forecasts are usually based on statistical relationships between weather conditions and ambient air pollution concentrations. Multivariate linear regression models have been widely used for this purpose, and well-specified regressions can provide reasonable results. However, pollution-weather relationships are typically complex and nonlinear—especially for ozone—properties that might be better captured by neural networks. This study investigates the potential for using neural networks to forecast ozone pollution, as compared to traditional regression models. Multiple regression models and neural networks are examined for a range of cities under different climate and ozone regimes, enabling a comparative study of the two approaches. Model comparison statistics indicate that neural network techniques are somewhat (but not dramatically) better than regression models for daily ozone prediction, and that all types of models are sensitive to different weather-ozone regimes and the role of persistence in aiding predictions. 相似文献
10.
11.
Claudio Carnevale Giovanna Finzi Enrico Pisoni Marialuisa Volta 《Atmospheric environment (Oxford, England : 1994)》2009,43(31):4811-4821
In order to define efficient air quality plans, Regional Authorities need suitable tools to evaluate both the impact of emission reduction strategies on pollution indexes and the costs of such emission reductions. The air quality control can be formalized as a two-objective nonlinear mathematical problem, integrating source–receptor models and the estimate of emission reduction costs. Both aspects present several complex elements. In particular the source–receptor models cannot be implemented through deterministic modelling systems, that would bring to a computationally unfeasible mathematical problem. In this paper we suggest to identify source–receptor statistical models (neural network and neuro-fuzzy) processing the simulations of a deterministic multi-phase modelling system (GAMES). The methodology has been applied to ozone and PM10 concentrations in Northern Italy. The results show that, despite a large advantage in terms of computational costs, the selected source–receptor models are able to accurately reproduce the simulation of the 3D modelling system. 相似文献
12.
13.
Perez P Palacios R Castillo A 《Journal of the Air & Waste Management Association (1995)》2004,54(8):908-913
In the city of Santiago, Chile, air quality is defined in terms of particulate matter with an aerodynamic diameter < or = 10 microm (PM10) concentrations. An air quality forecasting model based on past concentrations of PM10 and meteorological conditions currently is used by the metropolitan agency for the environment, which allows restrictions to emissions to be imposed in advance. This model, however, fails to forecast between 40 and 50% of the days considered to be harmful for the inhabitants every year. Given that a high correlation between particulate matter and carbon monoxide (CO) concentrations is observed at monitoring stations in the city, a model for CO concentration forecasting would be a useful tool to complement information about expected air quality in the city. Here, the results of a neural network-based model aimed to forecast maximum values of the 8-hr moving average of CO concentrations for the next day are presented. Forecasts from the neural network model are compared with those produced with linear regressions. The neural network model seems to leave more room to adjust free parameters with 1-yr data to predict the following year's values. We have worked with 3 yr of data measured at the monitoring station located in the zone with the worst air quality in the city of Santiago, Chile. 相似文献
14.
Jaques Reifman Earl E. Feldman 《Journal of the Air & Waste Management Association (1995)》2013,63(5):174-185
ABSTRACT We investigate the application of two classes of artificial neural networks for the identification and control of discrete-time nonlinear dynamical systems. A fully connected recurrent network is used for process identification, and a multilayer feedforward network is used for process control. The two neural networks are arranged in series for closed-loop control of oxides of nitrogen (NOx) emissions of a simplified representation of a dynamical system. Plant data from one of Commonwealth Edison's coal-fired power plants are used for testing the approach, with initial results indicating that the method is feasible. However, further work is required to determine whether the method remains feasible as the number of state variables and control variables are increased. 相似文献
15.
loannis F. Dimopoulos Konstantinos Serelis Aikaterini Chronopoulou 《Journal of the Air & Waste Management Association (1995)》2013,63(12):1506-1515
Abstract Neural networks (NNs) have the ability to model a wide range of complex nonlinearities. A major disadvantage of NNs, however, is their instability, especially under conditions of sparse, noisy, and limited data sets. In this paper, different combining network methods are used to benefit from the existence of local minima and from the instabilities of NNs. A nonlinear k-fold cross-validation method is used to test the performance of the various networks and also to develop and select a set of networks that exhibits a low correlation of errors. The various NN models are applied to estimate the spatial patterns of atmospherically transported and deposited lead (Pb) in soils around an historical industrial air emission point source. It is shown that the resulting ensemble networks consistently give superior predictions compared with the individual networks because, for the ensemble networks, R2 values were found to be higher than 0.9 while, for the contributing individual networks, values for R2 ranged between 0.35 and 0.85. It is concluded that combining networks can be adopted as an important component in the application of artificial NN techniques in applied air quality studies. 相似文献
16.
17.
《Journal of the Air & Waste Management Association (1995)》2013,63(9):1034-1037
Abstract A growing interest in security and occupant exposure to contaminants revealed a need for fast and reliable identification of contaminant sources during incidental situations. To determine potential contaminant source positions in outdoor environments, current state-of-the-art modeling methods use computational ?uid dynamic simulations on parallel processors. In indoor environments, current tools match accidental contaminant distributions with cases from precomputed databases of possible concentration distributions. These methods require intensive computations in pre- and postprocessing. On the other hand, neural networks emerged as a tool for rapid concentration forecasting of outdoor environmental contaminants such as nitrogen oxides or sulfur dioxide. All of these modeling methods depend on the type of sensors used for real-time measurements of contaminant concentrations. A review of the existing sensor technologies revealed that no perfect sensor exists, but intensity of work in this area provides promising results in the near future. The main goal of the presented research study was to extend neural network modeling from the outdoor to the indoor identification of source positions, making this technology applicable to building indoor environments. The developed neural network Locator of Contaminant Sources was also used to optimize number and allocation of contaminant concentration sensors for real-time prediction of indoor contaminant source positions. Such prediction should take place within seconds after receiving real-time contaminant concentration sensor data. For the purpose of neural network training, a multizone program provided distributions of contaminant concentrations for known source positions throughout a test building. Trained networks had an output indicating contaminant source positions based on measured concentrations in different building zones. A validation case based on a real building layout and experimental data demonstrated the ability of this method to identify contaminant source positions. Future research intentions are focused on integration with real sensor networks and model improvements for much more complicated contamination scenarios. 相似文献
18.
Anastasia K. Paschalidou Spyridon Karakitsios Savvas Kleanthous Pavlos A. Kassomenos 《Environmental science and pollution research international》2011,18(2):316-327
In the present work, two types of artificial neural network (NN) models using the multilayer perceptron (MLP) and the radial
basis function (RBF) techniques, as well as a model based on principal component regression analysis (PCRA), are employed
to forecast hourly PM10 concentrations in four urban areas (Larnaca, Limassol, Nicosia and Paphos) in Cyprus. The model development is based on a
variety of meteorological and pollutant parameters corresponding to the 2-year period between July 2006 and June 2008, and
the model evaluation is achieved through the use of a series of well-established evaluation instruments and methodologies.
The evaluation reveals that the MLP NN models display the best forecasting performance with R
2 values ranging between 0.65 and 0.76, whereas the RBF NNs and the PCRA models reveal a rather weak performance with R
2 values between 0.37-0.43 and 0.33-0.38, respectively. The derived MLP models are also used to forecast Saharan dust episodes
with remarkable success (probability of detection ranging between 0.68 and 0.71). On the whole, the analysis shows that the
models introduced here could provide local authorities with reliable and precise predictions and alarms about air quality
if used on an operational basis. 相似文献
19.
《Atmospheric environment (Oxford, England : 1994)》2002,36(28):4555-4561
We have developed a neural network based model that uses values of PM10 concentrations measured until 6 p.m. on the present day plus measured and forecasted values of meteorological variables as input in order to predict the level reached by the maximum of the 24-h moving average (24MA) of PM10 concentration on the next day. We have adjusted the parameters of the model using 1998 data to predict 1999 conditions and 1999 data to forecast 2000 maximum concentrations. We have found that among the relevant meteorological input variables, the forecasted difference between maximum and minimum temperature is the most important. Due to the fact that local authorities impose restrictions to emissions on days when the maximum of 24MA of PM10 concentration is expected to exceed 240 μg/m3, we have corrected the measured concentrations on these days before evaluating the efficacy of the forecasting model. Percent errors in forecasting the numerical value are of the order of 20%. The performance of the neural network is better than that of a linear model with the same inputs, but the difference being not great is an indication that the right choice of input variables may be more important than the decision to use a linear or a nonlinear model. 相似文献
20.
A neural network model forecasting for prediction of daily maximum ozone concentration in an industrialized urban area 总被引:9,自引:0,他引:9
Prediction of ambient ozone concentrations in urban areas would allow evaluation of such factors as compliance and noncompliance with EPA requirements. Though ozone prediction models exist, there is still a need for more accurate models. Development of these models is difficult because the meteorological variables and photochemical reactions involved in ozone formation are complex. In this study, we developed a neural network model for forecasting daily maximum ozone levels. We then compared the neural network's performance with those of two traditional statistical models, regression, and Box-Jenkins ARIMA. The neural network model for forecasting daily maximum ozone levels is different from the two statistical models because it employs a pattern recognition approach. Such an approach does not require specification of the structural form of the model. The results show that the neural network model is superior to the regression and Box-Jenkins ARIMA models we tested. 相似文献