Breakthrough Curves Characterization and Identification of an Unknown Pollution Source in Groundwater System Using an Artificial Neural Network (ANN)

Contamination of groundwater constrains its uses and poses a serious threat to the environment. Once groundwater is contaminated, the cleanup may be difficult and expensive. Identification of unknown pollution sources is the first step toward adopting any remediation strategy. The proposed methodology exploits the capability of a universal function approximation by a feed-forward multilayer artificial neural network (ANN) to identify the sources in terms of its location, magnitudes, and duration of activity. The back-propagation algorithm is utilized for training the ANN to identify the source characteristics based on simulated concentration data at specified observation locations in the aquifer. Uniform random generation and the Latin hypercube sampling method of random generation are used to generate temporal varying source fluxes. These source fluxes are used in groundwater flow and the transport simulation model to generate necessary data for the ANN model-building processes. Breakthrough curves obtained for the specified pollution scenario are characterized by different methods. The characterized breakthrough curves parameters serve as inputs to ANN model. Unknown pollution source characteristics are outputs for ANN model. Experimentation is also performed with different number of training and testing patterns. In addition, the effects of measurement errors in concentration measurements values are used to show the robustness of ANN based methodology for source identification in case of erroneous data.     

基于人工蜂群算法与BP神经网络的水质评价模型

针对BP网络水质评价模型的不足,引入人工蜂群(ABC)算法,将求解BP神经网络各层权值、阀值的过程转化为蜜蜂寻找最佳蜜源的过程,提出了一种新的结合人工蜂群算法的BP网络水质评价方法(ABC-BP)。并以2000—2006年渭河监测断面的10组实测数据作为测试样本对其水质进行了评价,实验结果表明该方法得到的水质评价结果准确,并具有很强的稳定性和鲁棒性。     

Development of artificial neural network based metamodels for inactivation of anthrax spores in ventilated spaces using computational fluid dynamics

Linear, quadratic, and artificial neural network (ANN)-based metamodels were developed for predicting the extent of anthrax spore inactivation by chlorine dioxide in a ventilated three-dimensional space over time from computational fluid dynamics model (CFD) simulation data. Dimensionless groups were developed to define the design space of the problem scenario. The Hammersley sequence sampling (HSS) method was used to determine the sampling points for the numerical experiments within the design space. A CFD model, comprised of multiple submodels, was applied to conduct the numerical experiments. Large eddy simulation (LES) with the Smagorinsky subgridscale model was applied to compute the airflow. Anthrax spores were modeled as a dispersed solid phase using the Lagrangian treatment. The disinfectant transport was calculated by solving a mass transport equation. Kinetic decay constants were included for spontaneous decay of the disinfectant and for the reaction of the disinfectant with the surfaces of the three-dimensional space. To enhance the mixing of the disinfectant with the room air, a momentum source was included in the simulation. An inactivation rate equation accounted for the reaction between the spores and the disinfectant. The ANN-based metamodels were most successful in predicting the number of viable bioaerosols remaining in an arbitrary enclosed space. Sensitivity analysis showed that the mass fraction of the disinfectant, inactivation rate constant, and contact time had the most influence on the inactivation of the spores.     

大气环境质量的人工神经网络决策模型

为了对环境质量进行综合评价，运用误差反向传播算法的人工神经网络方法建立了环境质量评价的Ｂ－Ｐ决策模型。用此模型研究实例的大气环境质量，结果表明Ｂ－Ｐ网络用于环境质量评价具有客观性和实用性。     

One-year measurements of chloroethenes in tree cores and groundwater at the SAP Mimoň Site, Northern Bohemia

Chlorinated ethenes (CE) are among the most frequent contaminants of soil and groundwater in the Czech Republic. Because conventional methods of subsurface contamination investigation are costly and technically complicated, attention is directed on alternative and innovative field sampling methods. One promising method is sampling of tree cores (plugs of woody tissue extracted from a host tree). Volatile organic compounds can enter into the trunks and other tissues of trees through their root systems. An analysis of the tree core can thus serve as an indicator of the subsurface contamination. Four areas of interest were chosen at the experimental site with CE groundwater contamination and observed fluctuations in groundwater concentrations. CE concentrations in groundwater and tree cores were observed for a 1-year period. The aim was to determine how the CE concentrations in obtained tree core samples correlate with the level of contamination of groundwater. Other factors which can affect the transfer of contaminants from groundwater to wood were also monitored and evaluated (e.g., tree species and age, level of groundwater table, river flow in the nearby Plou?nice River, seasonal effects, and the effect of the remediation technology operation). Factors that may affect the concentration of CE in wood were identified. The groundwater table level, tree species, and the intensity of transpiration appeared to be the main factors within the framework of the experiment. Obtained values documented that the results of tree core analyses can be used to indicate the presence of CE in the subsurface. The results may also be helpful to identify the best sampling period for tree coring and to learn about the time it takes until tree core concentrations react to changes in groundwater conditions. Interval sampling of tree cores revealed possible preservation of the contaminant in the wood of trees.     

Development of Artificial Neural Network Based Metamodels for Inactivation of Anthrax Spores in Ventilated Spaces Using Computational Fluid Dynamics

ABSTRACT

Linear, quadratic, and artificial neural network (ANN)-based metamodels were developed for predicting the extent of anthrax spore inactivation by chlorine dioxide in a ventilated three-dimensional space over time from computational fluid dynamics model (CFD) simulation data. Dimensionless groups were developed to define the design space of the problem scenario. The Hammersley sequence sampling (HSS) method was used to determine the sampling points for the numerical experiments within the design space. A CFD model, comprised of multiple submodels, was applied to conduct the numerical experiments. Large eddy simulation (LES) with the Smagorinsky subgrid-scale model was applied to compute the airflow. Anthrax spores were modeled as a dispersed solid phase using the Lagrangian treatment. The disinfectant transport was calculated by solving a mass transport equation. Kinetic decay constants were included for spontaneous decay of the disinfectant and for the reaction of the disinfectant with the surfaces of the three-dimensional space. To enhance the mixing of the disinfectant with the room air, a momentum source was included in the simulation. An inactivation rate equation accounted for the reaction between the spores and the disinfectant. The ANN-based metamodels were most successful in predicting the number of viable bioaerosols remaining in an arbitrary enclosed space. Sensitivity analysis showed that the mass fraction of the disinfectant, inactivation rate constant, and contact time had the most influence on the inactivation of the spores.

IMPLICATIONS This investigation presents a framework for the development of user-friendly models; metamodels for the prediction of the number of viable spores remaining in an indoor room during disinfection from accurate but time-consuming CFD studies. During any decontamination event, to know when to stop pumping in the disinfectant and to know what level of log reduction of the spores have been achieved before even starting decontamination would provide valuable guidance. The neural network based metamodels can be applied to obtain quick and relatively accurate answers. This would be necessary when immediate information is required during emergencies.     

基于改进型灰色神经网络组合模型的空气质量预测

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。     

Prediction of ambient PM10 and toxic metals using artificial neural networks

In this study, an artificial neural network is employed to predict the concentration of ambient respirable particulate matter (PM10) and toxic metals observed in the city of Jaipur, India. A feed-forward network with a back-propagation learning algorithm is used to train the neural network the behavior of the data patterns. The meteorological variables of wind speed, wind direction, relative humidity, temperature, and time are taken as input to the network. The results indicate that the network is able to predict concentrations of PM10 and toxic metals quite accurately.     

Deep learning architecture for air quality predictions

With the rapid development of urbanization and industrialization, many developing countries are suffering from heavy air pollution. Governments and citizens have expressed increasing concern regarding air pollution because it affects human health and sustainable development worldwide. Current air quality prediction methods mainly use shallow models; however, these methods produce unsatisfactory results, which inspired us to investigate methods of predicting air quality based on deep architecture models. In this paper, a novel spatiotemporal deep learning (STDL)-based air quality prediction method that inherently considers spatial and temporal correlations is proposed. A stacked autoencoder (SAE) model is used to extract inherent air quality features, and it is trained in a greedy layer-wise manner. Compared with traditional time series prediction models, our model can predict the air quality of all stations simultaneously and shows the temporal stability in all seasons. Moreover, a comparison with the spatiotemporal artificial neural network (STANN), auto regression moving average (ARMA), and support vector regression (SVR) models demonstrates that the proposed method of performing air quality predictions has a superior performance.     

基于人工神经网络的街道峡谷NOx浓度的数值模型研究

通过对反向传播人工神经网络的算法和网络结构的研究,发现拟牛顿算法训练速度较快,能够较好地接近误差目标值,同时建立了包括输入层、隐含层、输出层的人工神经网络三层拓扑结构。通过对街道峡谷人工神经网络的训练,模拟计算了街道峡谷NOx浓度分布值。结果显示,训练误差和测试误差比为1．11,训练样本的模拟值与实测值的相关系数为0．93,测试样本的模拟值与实测值的相关系数为0．87,模拟值与实测值的相关系数均高于显著水平为α=0．05与α=0．01所对应检验性表的相关系数临界值。该模型能够用于街道峡谷污染物浓度的模拟计算,具有较好的泛化能力。     

Cost-effective sampling network design for contaminant plume monitoring under general hydrogeological conditions

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.     

A neural-fuzzy approach to classify the ecological status in surface waters

A methodology based on a hybrid approach that combines fuzzy inference systems and artificial neural networks has been used to classify ecological status in surface waters. This methodology has been proposed to deal efficiently with the non-linearity and highly subjective nature of variables involved in this serious problem. Ecological status has been assessed with biological, hydro-morphological, and physicochemical indicators. A data set collected from 378 sampling sites in the Ebro river basin has been used to train and validate the hybrid model. Up to 97.6% of sampling sites have been correctly classified with neural-fuzzy models. Such performance resulted very competitive when compared with other classification algorithms. With non-parametric classification-regression trees and probabilistic neural networks, the predictive capacities were 90.7% and 97.0%, respectively. The proposed methodology can support decision-makers in evaluation and classification of ecological status, as required by the EU Water Framework Directive.     

基于人工神经网络的街道峡谷NO_x浓度的数值模型研究

通过对反向传播人工神经网络的算法和网络结构的研究,发现拟牛顿算法训练速度较快,能够较好地接近误差目标值,同时建立了包括输入层、隐含层、输出层的人工神经网络三层拓扑结构。通过对街道峡谷人工神经网络的训练,模拟计算了街道峡谷NOx浓度分布值。结果显示,训练误差和测试误差比为1.11,训练样本的模拟值与实测值的相关系数为0.93,测试样本的模拟值与实测值的相关系数为0.87,模拟值与实测值的相关系数均高于显著水平为α=0.05与α=0.01所对应检验性表的相关系数临界值。该模型能够用于街道峡谷污染物浓度的模拟计算,具有较好的泛化能力。     

Content of aliphatic hydrocarbons in limpets as a new way for classification of species using artificial neural networks

It is demonstrated that biological species like limpets can be classified according to their level of n-alkanes when artificial neural networks are applied. Marine intertidal and subtidal limpets of the Canary Islands (Spain), Patella piperata, Patella candei crenata and Patella ulyssiponensis aspera were selected as bioindicator organisms. Samples were collected at four stations on the coasts of Fuerteventura. Concentration of n-alkanes in the soft tissues of the limpets has been determined by gas chromatography. Data were treated with artificial neural networks (ANNs) and it was found that using suitable architecture of a supervised artificial neural network, the limpets can be successfully distinguished (classified) up to 98%.     

基于人工神经网络和遗传算法研究锰过氧化酶脱色甲基橙

基于锰过氧化物酶(MnP)氧化脱色偶氮类染料的原理,实验研究MnP对甲基橙的脱色工艺,采用人工神经网络(ANN)和遗传算法(GA)建立脱色模型并优化工艺。建立的ANN模型的误差、相关系数、均方根误差和绝对平均偏差分别为0.0009、0.9971、1.21和6.82,模型有效且能够用于预测和工艺优化。采用GA对ANN模型进行数值寻优,得到的最佳工艺条件为酶液量0.6 mL,Mn2+浓度4 mmol/L,H2O2浓度0.49 mmol/L。该条件下脱色率达到(90.74±0.59)%。ANN耦合GA有效地建立了锰过氧化物酶脱色甲基橙的模型,并优化了工艺参数,为甲基橙脱色的研究提供一定参考。     

Mechanistic study of the ChemChar Process using hexachlorobenzene as a surrogate for monitoring reaction products

The ChemChar Process, a non-incinerative thermolytic technology designed to destroy hazardous and non-hazardous wastes, was studied using the surrogate compound hexachlorobenzene (HCB). The process was varied from the conventional mode of operation, and thermal degradation products of HCB were collected in sampling trains. A mechanism of thermal destruction was proposed from experimental results and comparison to known mechanisms employing similar coal conversion technology.     

Modelling of dissolved oxygen content using artificial neural networks: Danube River, North Serbia, case study

The aims of this study are to create an artificial neural network (ANN) model using non-specific water quality parameters and to examine the accuracy of three different ANN architectures: General Regression Neural Network (GRNN), Backpropagation Neural Network (BPNN) and Recurrent Neural Network (RNN), for prediction of dissolved oxygen (DO) concentration in the Danube River. The neural network model has been developed using measured data collected from the Bezdan monitoring station on the Danube River. The input variables used for the ANN model are water flow, temperature, pH and electrical conductivity. The model was trained and validated using available data from 2004 to 2008 and tested using the data from 2009. The order of performance for the created architectures based on their comparison with the test data is RNN > GRNN > BPNN. The ANN results are compared with multiple linear regression (MLR) model using multiple statistical indicators. The comparison of the RNN model with the MLR model indicates that the RNN model performs much better, since all predictions of the RNN model for the test data were within the error of less than ±10 %. In case of the MLR, only 55 % of predictions were within the error of less than ±10 %. The developed RNN model can be used as a tool for the prediction of DO in river waters.     

Prediction of Ambient PM10 and Toxic Metals Using Artificial Neural Networks

Abstract

In this study, an artificial neural network is employed to predict the concentration of ambient respirable particu-late matter (PM₁₀) and toxic metals observed in the city of Jaipur, India. A feed-forward network with a back-propagation learning algorithm is used to train the neural network the behavior of the data patterns. The meteorological variables of wind speed, wind direction, relative humidity, temperature, and time are taken as input to the network. The results indicate that the network is able to predict concentrations of PM₁₀ and toxic metals quite accurately.     

利用复介电常数和BPAT神经网络进行环境监测的可行性研究

土壤中的污染物成分复杂，其含量与复介电常数之间具有很强的非线性关系。以土壤样品复介电常数的实部、虚部分别作为输入，以其含水率、体密度和所含6种已知离子的浓度分别作为输出，建立BP人工神经网络。把吉泰兰地区的土壤样品数据分为训练样本集和检验样本集，网络训练后，其学习效果显示模型的性能很好，检验样本的预测结果也与实测值较好吻合，说明利用复介电常数和BP人工神经网络进行环境监测是一种好的方法。     

QSAR modeling with the electrotopological state indices: predicting the toxicity of organic chemicals

A quantitative structure-activity relationship model, based on the atom-type electrotopological state (E-state) indices, for the prediction of toxicity to fathead minnow for a diverse set of 140 organic chemicals is presented. Multiple linear regression and artificial neural network techniques were employed in the modeling of experimental toxicity (-logLC(50)) values ranging from 0.85 to 6.09. For the training set of 130 organic compounds a linear regression model with r(2)=0.84 and s=0.36 was obtained with 14 atom-type E-state indices. For the test set of 10 compounds, the corresponding statistics were r(2)=0.83 and s=0.47, respectively. Neural networks gave a significant improvement using the same set of parameters, and the standard deviations were s=0.31 for the training set and s=0.30 for the test set when an artificial neural network with five neurons in the hidden layer was used. The results clearly show that accurate models can be rapidly calculated for the prediction of toxicity for a diverse set of organic chemicals using easily calculated parameters.