Development of a neural-based forecasting tool to classify recreational water quality using fecal indicator organisms

Users of recreational waters may be exposed to elevated pathogen levels through various point/non-point sources. Typical daily notifications rely on microbial analysis of indicator organisms (e.g., Escherichia coli) that require 18, or more, hours to provide an adequate response. Modeling approaches, such as multivariate linear regression (MLR) and artificial neural networks (ANN), have been utilized to provide quick predictions of microbial concentrations for classification purposes, but generally suffer from high false negative rates. This study introduces the use of learning vector quantization (LVQ) - a direct classification approach - for comparison with MLR and ANN approaches and integrates input selection for model development with respect to primary and secondary water quality standards within the Charles River Basin (Massachusetts, USA) using meteorologic, hydrologic, and microbial explanatory variables. Integrating input selection into model development showed that discharge variables were the most important explanatory variables while antecedent rainfall and time since previous events were also important. With respect to classification, all three models adequately represented the non-violated samples (>90%). The MLR approach had the highest false negative rates associated with classifying violated samples (41-62% vs 13-43% (ANN) and <16% (LVQ)) when using five or more explanatory variables. The ANN performance was more similar to LVQ when a larger number of explanatory variables were utilized, but the ANN performance degraded toward MLR performance as explanatory variables were removed. Overall, the use of LVQ as a direct classifier provided the best overall classification ability with respect to violated/non-violated samples for both standards.     

基于ANN的岩土体热阻系数预测模型研究

为准确预测土体热阻系数,通过室内热探针测试与数据分析,简要分析了含水量、干密度、矿物成分和颗粒形态等因素对土体热传导特性的影响,利用人工神经网络(ANN)技术,建立了计算土体热阻系数的预测模型,并与传统经验关系模型进行对比,明确所提计算模型的可靠性与优越性.结果表明:土体传热性能受众多因素影响,其热阻系数难以准确估算,基于ANN的计算模型可以较好地解决这一问题;以含水量和干密度为输入参数的单个模型适用于特定类型土体,而4个输入参数(含水量、干密度、黏粒含量和石英含量)的广义模型不受此限制,增加相关输入参数可有效保证模型计算结果的精确度;单个模型和广义模型的计算结果与实测结果吻合良好,预测能力均显著优于传统经验关系模型;对于工程性质差异显著、沉积环境复杂的不同类型土体,建议优先选用广义模型来估算其热阻系数.     

Neural network modeling of salinity variation in Apalachicola River.

Salinity is an important indicator for water quality and aquatic ecosystem in tidal rivers. The increase of salinity intrusion in a river may have an adverse effect on the aquatic environment system. This study presents an application of the artificial neural network (ANN) to assess salinity variation responding to the multiple forcing functions of freshwater input, tide, and wind in Apalachicola River, Florida. Parameters in the neural network model were trained until the model predictions of salinity matched well with the observations. Then, the trained model was validated by applying the model to another independent data set. The results indicate that the ANN model is capable of correlating the non-linear time series of salinity to the multiple forcing signals of wind, tides. and freshwater input in the Apalachicola River. This study suggests that the ANN model is an easy-to-use modeling tool for engineers and water resource managers to obtain a quick preliminary assessment of salinity variation in response to the engineering modifications to the river system.     

Dynamic wind response of tall buildings using artificial neural network

This paper presents an alternative approach for predicting the dynamic wind response of tall buildings using artificial neural network (ANN). The ANN model was developed, trained, and validated based on the data generated in the context of Indian Wind Code (IWC), IS 875 (Part 3):2015. According to the IWC, dynamic wind responses can be calculated for a specific configuration of buildings. The dynamic wind loads and their corresponding responses of structures other than the specified configurations in IWC have to be estimated by wind tunnel tests or computational techniques, which are expensive and time intensive. Alternatively, ANN is an efficient and economical computational analysis tool that can be implemented to estimate the dynamic wind response of a building. In this paper, ANN models were developed to predict base shear and base bending moment of a tall building in along‐ and across‐wind direction by giving the input as the configuration of the building, wind velocity, and terrain category. Multilayer perceptron ANN models with back‐propagation training algorithm was adopted. On comparison of results, it was found that the predicted values obtained from the ANN models and the calculated responses acquired using IWC standards are almost similar. Using the best fit model of ANN, an extensive parametric study was performed to predict the dynamic wind response of tall buildings for the configurations on which IWC is silent. Based on the results obtained from this study, design charts are developed for the prediction of dynamic wind response of tall buildings.     

Evaluation of the ability of an artificial neural network model to assess the variation of groundwater quality in an area of blackfoot disease in Taiwan

The back-propagation (BP) artificial neural network (ANN) is applied to forecast the variation of the quality of groundwater in the blackfoot disease area in Taiwan. Three types of BP ANN models were established to evaluate their learning performance. Model A included five concentration parameters as input variables for seawater intrusion and three concentration parameters as input variables for arsenic pollutant, respectively, whereas models B and C used only one concentration parameter for each. Furthermore, model C used seasonal data from two seasons to train the ANN, whereas models A and C used only data from one season. The results indicate that model C outperforms models A and B. Model C can describe complex variation of groundwater quality and be used to perform reliable forecasting. Moreover, the number of hidden nodes does not significantly influence the performance of the ANN model in training or testing.     

Application of artificial neural network for performance evaluation of vertical axis wind turbine rotor

In the present paper, artificial neural network (ANN) modelling has been performed for evaluating power coefficient (C_p) and torque coefficient (C_t) of a combined three-bucket-Savonius and three-bladed-Darrieus vertical axis wind turbine rotor, which has got potential for power generation in a small-scale manner, especially in low wind speed conditions. However, detailed experimental work on the rotor for evaluating its performance parameters is either scarce or too costly and time consuming to carry out. In this work, a new ANN modelling method is adopted to map the input–output parameters using very small training data sets, selected from past experimental results of the rotor. The trained ANN models are used to predict the performance data, which are obtained within acceptable error limits. Furthermore, to evaluate the fit values and estimate the variance of the predicted data by the ANN models, linear regression equations are fitted to the experimental and predicted results, which shows that R-squared (R²) values are obtained close to unity meaning good fitting of the data. Moreover, the results of ANN modelling are also compared with that of radial basis function (RBF) networks, which also show a good agreement between ANN predicted data and RBF network data. The present ANN models can be exploited to extract more performance data within a given range of input data.     

Daily suspended sediment concentration simulation using ANN and neuro-fuzzy models

In the present study, artificial neural networks (ANNs), neuro-fuzzy (NF), multi linear regression (MLR) and conventional sediment rating curve (SRC) models are considered for time series modeling of suspended sediment concentration (SSC) in rivers. As for the artificial intelligence systems, feed forward back propagation (FFBP) method and Sugeno inference system are used for ANNs and NF models, respectively. The models are trained using daily river discharge and SSC data belonging to Little Black River and Salt River gauging stations in the USA.Obtained results demonstrate that ANN and NF models are in good agreement with the observed SSC values; while they depict better results than MLR and SRC methods. For example, in Little Black River station, the determination coefficient is 0.697 for NF model, while it is 0.457, 0.257 and 0.225 for ANN, MLR and SRC models, respectively. The values of cumulative suspended sediment load estimated by ANN and NF models are closer to the observed data than the other models. In general, the results illustrate that NF model presents better performance in SSC prediction in compression to other models.     

Assessing artificial neural network performance for predicting interlayer conditions and layer modulus of multi-layered flexible pavement

The objective of this study is to evaluate the performance of the artificial neural network (ANN) approach for predicting interlayer conditions and layer modulus of a multi-layered flexible pavement structure. To achieve this goal, two ANN based back-calculation models were proposed to predict the interlayer conditions and layer modulus of the pavement structure. The corresponding database built with ANSYS based finite element method computations for four types of a structure subjected to falling weight deflectometer load. In addition, two proposed ANN models were verified by comparing the results of ANN models with the results of PADAL and double multiple regression models. The measured pavement deflection basin data was used for the verifications. The comparing results concluded that there are no significant differences between the results estimated by ANN and double multiple regression models. PADAL modeling results were not accurate due to the inability to reflect the real pavement structure because pavement structure was not completely continuous. The prediction and verification results concluded that the proposed back-calculation model developed with ANN could be used to accurately predict layer modulus and interlayer conditions. In addition, the back-calculation model avoided the back-calculation errors by considering the interlayer condition, which was barely considered by former models reported in the published studies.     

Development of predictive models for determining enterococci levels at Gulf Coast beaches

The US EPA BEACH Act requires beach managers to issue swimming advisories when water quality standards are exceeded. While a number of methods/models have been proposed to meet the BEACH Act requirement, no systematic comparisons of different methods against the same data series are available in terms of relative performance of existing methods. This study presents and compares three models for nowcasting and forecasting enterococci levels at Gulf Coast beaches in Louisiana, USA. One was developed using the artificial neural network (ANN) in MATLAB Toolbox and the other two were based on the US EPA Virtual Beach (VB) Program. A total of 944 sets of environmental and bacteriological data were utilized. The data were collected and analyzed weekly during the swimming season (May-October) at six sites of the Holly Beach by Louisiana Beach Monitoring Program in the six year period of May 2005-October 2010. The ANN model includes 15 readily available environmental variables such as salinity, water temperature, wind speed and direction, tide level and type, weather type, and various combinations of antecedent rainfalls. The ANN model was trained, validated, and tested using 308, 103, and 103 data sets (collected in 2007, 2008, and 2009) with an average linear correlation coefficient (LCC) of 0.857 and a Root Mean Square Error (RMSE) of 0.336. The two VB models, including a linear transformation-based model and a nonlinear transformation-based model, were constructed using the same data sets. The linear VB model with 6 input variables achieved an LCC of 0.230 and an RMSE of 1.302 while the nonlinear VB model with 5 input variables produced an LCC of 0.337 and an RMSE of 1.205. In order to assess the predictive performance of the ANN and VB models, hindcasting was conducted using a total of 430 sets of independent environmental and bacteriological data collected at six Holly Beach sites in 2005, 2006, and 2010. The hindcasting results show that the ANN model is capable of predicting enterococci levels at the Holly Beach sites with an adjusted RMSE of 0.803 and LCC of 0.320 while the adjusted RMSE and LCC values are 1.815 and 0.354 for the linear VB model and 1.961and 0.521 for the nonlinear VB model. The results indicate that the ANN model with 15 parameters performs better than the VB models with 6 or 5 parameters in terms of RMSE while VB models perform better than the ANN model in terms of LCC. The predictive models (especially the ANN and the nonlinear VB models) developed in this study in combination with readily available real-time environmental and weather forecast data can be utilized to nowcast and forecast beach water quality, greatly reducing the potential risk of contaminated beach waters to human health and improving beach management. While the models were developed specifically for the Holly Beach, Louisiana, the methods used in this paper are generally applicable to other coastal beaches.     

Prediction of contamination potential of groundwater arsenic in Cambodia, Laos, and Thailand using artificial neural network

The arsenic (As) contamination of groundwater has increasingly been recognized as a major global issue of concern. As groundwater resources are one of most important freshwater sources for water supplies in Southeast Asian countries, it is important to investigate the spatial distribution of As contamination and evaluate the health risk of As for these countries. The detection of As contamination in groundwater resources, however, can create a substantial labor and cost burden for Southeast Asian countries. Therefore, modeling approaches for As concentration using conventional on-site measurement data can be an alternative to quantify the As contamination. The objective of this study is to evaluate the predictive performance of four different models; specifically, multiple linear regression (MLR), principal component regression (PCR), artificial neural network (ANN), and the combination of principal components and an artificial neural network (PC-ANN) in the prediction of As concentration, and to provide assessment tools for Southeast Asian countries including Cambodia, Laos, and Thailand. The modeling results show that the prediction accuracy of PC-ANN (Nash-Sutcliffe model efficiency coefficients: 0.98 (traning step) and 0.71 (validation step)) is superior among the four different models. This finding can be explained by the fact that the PC-ANN not only solves the problem of collinearity of input variables, but also reflects the presence of high variability in observed As concentrations. We expect that the model developed in this work can be used to predict As concentrations using conventional water quality data obtained from on-site measurements, and can further provide reliable and predictive information for public health management policies.     

Modeling hysteretic nonlinear behavior of bridge aerodynamics via cellular automata nested neural network

A new approach to model aerodynamic nonlinearities in the time domain utilizing an artificial neural network (ANN) framework with embedded cellular automata (CA) scheme has been developed. This nonparametric modeling approach has shown good promise in capturing the hysteretic nonlinear behavior of aerodynamic systems in terms of hidden neurons involving higher-order terms. Concurrent training of a set of higher-order neural networks facilitates a unified approach for modeling the combined analysis of flutter and buffeting of cable-supported bridges. Accordingly the influence of buffeting response on the self-excited forces can be captured, including the contribution of damping and coupling effects on the buffeting response. White noise is intentionally introduced to the input data to enhance the robustness of the trained neural network embedded with optimal typology of CA. The effectiveness of this approach and its applications are discussed by way of modeling the aerodynamic behavior of a single-box girder cross-section bridge deck (2-D) under turbulent wind conditions. This approach can be extended to a full-bridge (3-D) model that also takes into account the correlation of aerodynamic forces along the bridge axis. This novel application of data-driven modeling has shown a remarkable potential for applications to bridge aerodynamics and other related areas.     

Predicting a water infrastructure leakage index via machine learning

In this study, the infrastructure leakage index (ILI) indicator that is preferred frequently by the water utilities with sufficient data to determine the performances of water distribution systems is modeled for the first time through the three different methodologies using different input data. In addition to the variables in the literature used for the classical ILI calculations, the age parameter is also included in the models. In the first step, the ILI values have been estimated via multiple linear regression (MLR) using water supply quantity, water accrual quantity, network length, service connection length, number of service connections, and pressure variables. Secondly, the Artificial Neural Network (ANN) approach has been applied with raw data to improve the ILI prediction performance. Finally, the data set has been standardized with the Z-Score method for increasing the learning power of the ANN models, and then the ANN predictions have been made by converting the data through the principal component analysis (PCA) method to minimize complexity by reducing the data set size. The model predictions have been evaluated via mean square error, G-value, mean absolute error, mean bias error, and adjusted-R² model performance scale. When the model outputs obtained at the end of the study are evaluated together with the classical ILI calculations, it is seen that the successful ILI predictions with three and four variables, including the age parameter, rather than six variables, have been made through the PC-ANN method. Water utilities with insufficient physical and operational data for ILI indicator calculation can make network performance evaluations by predicting the ILI through the models suggested in this study with high accuracy in a reliable way.     

Neural network prediction of concrete degradation by sulphuric acid attack

Microbiologically induced corrosion is a leading cause of the deterioration of wastewater collection, transmission and treatment infrastructure around the world. This paper examines the feasibility of using artificial neural networks (ANNs) to predict the compressive strength of concrete and its degradation under exposure to sulphuric acid of various concentrations. A database incorporating 78 concrete mixtures performed by the authors was developed to train and test the ANN models. Data were arranged in a patterned format in such a manner that each pattern contains input variables (concrete mixture parameters) and the corresponding output vector (weight loss of concrete by H₂SO₄ attack and compressive strength at different ages). Results show that the ANN model I successfully predicted the weight loss of concrete specimens subjected to sulphuric acid attack, not only for mixtures used in the training process, but also for new mixtures unfamiliar to the ANN model designed within the practical range of the input parameters used in the training process. Root-mean-squared error (RMSE) and average absolute error (AAE) for ANN predictions of weight loss due to sulphuric acid attack were 0.013 and 8.45%, respectively. The ANN model II accurately predicted the compressive strength of the various concrete mixtures at different ages with RMSE and AAE of 2.35 MPa and 4.49%, respectively. A parametric study shows that both models I and II can successfully capture the sensitivity of output variables to changes in input parameters.     

Simulation-based construction productivity forecast using Neural-Network-Driven Fuzzy Reasoning

Fuzzy models and Artificial Neural Network (ANN) systems are two well-known areas of soft-computing that have significantly helped researchers with decision-making under uncertainties. Uncertainty, an ever-present factor in construction projects, has made such intelligent systems very attractive to the construction industry. Estimating the productivity of construction operations, as a basic element of project planning and control, has become a remarkable target for forecasting models. A glimpse into this interdisciplinary field of research exposes the need for a system, that (1) models the effect of qualitative and quantitative variables on construction productivity with an improved accuracy of estimation and (2) has the ability to deal with both crisp and fuzzy input variables in one single framework. Neural-Network-Driven Fuzzy Reasoning (NNDFR), as one of the hybrid intelligent structures, displays a great potential for modeling datasets among which clear clusters are recognizable. The weakness of NNDFR in auto-tuning the design of fuzzy membership functions along with this model's insufficient attention to the optimization of number of clusters has created an area for further research. In this paper, the parameters (fuzzifier and number of clusters) of the proposed system are optimized by using Genetic Algorithm (GA) to fine-tune the system for the highest possible level of accuracy that can be exploited for productivity estimation. The proposed model is also capable of dealing with a combination of crisp and fuzzy input variables by using a hybrid modeling approach based on the application of the alpha-cut technique. The developed model helps researchers and practitioners use historical data to forecast the productivity of construction operations with a level of accuracy greater than what could be offered by traditional techniques.     

模型参数误差对用神经网络进行结构损伤识别的影响

通过理论推导得到了模型参数误差对损伤引起模态参数改变的贡献的表达式,用该式可指导神经网络输入参数的选择和输入向量的构造.理论分析表明,适当地构造输入向量,可以减小模型参数误差对结构损伤识别的影响.在采用BP网络和合适的输入向量后,还用数值模拟的方式对一榀六层框架的损伤识别进行了确定性研究和概率分析,结果表明,用神经网络进行结构损伤识别,受模型参数误差的影响很小,在训练神经网络时,10%的模型参数误差是可以接受的.最后,用一个两层钢框架的实验数据验证了神经网络在有模型误差时的识别能力.     

Artificial neural network-based prediction of performance and emission characteristics of CI engine using polanga as a biodiesel

The present work predicts the performance parameters, namely brake specific fuel consumption (BSFC), brake thermal efficiency (BTE), peak pressure, exhaust gas temperature and exhaust emissions of a single cylinder four-stroke diesel engine at different injection timings and engine load using blended mixture of polanga biodiesel by artificial neural network (ANN). The properties of biodiesel produced from polanga were measured based on ASTM standards. Using some of the experimental data for training, an ANN model was developed based on standard back-propagation algorithm for the engine. Multi-layer perception network was used for non-linear mapping between input and output parameters. Different activation functions and several rules were used to assess the percentage error between the desired and the predicted values. It was observed that the developed ANN model can predict the engine performance and exhaust emissions quite well with correlation coefficient (R) 0.99946, 0.99968, 0.99988, 0.99967, 0.99899, 0.99941 and 0.99991 for the BSFC, BTE, peak pressure, exhaust gas temperature, NO_x, smoke and unburned hydrocarbon emissions, respectively. The experimental results revealed that the blended fuel provides better engine performance and improved emission characteristics.     

An artificial neural network-based mathematical model for the prediction of blast-induced ground vibrations

ABSTRACT

This paper presents an artificial neural network (ANN) based mathematical model for the prediction of blast-induced ground vibrations using the data obtained from the literature. A feed-forward back-propagation multi-layer perceptron (MLP) was adopted, and the Levenberg–Marquardt algorithm was used in training the network. The powder factor, the maximum charge per delay, and distance from blasting face to monitoring point are the input variables. The peak particle velocity (PPV) is the targeted output variable. The model was then formulated using the weights and biases output from the ANN simulation. Multilinear regression (MLR) analysis was also performed using the same number of datasets, as in the case of ANN. The quality of the proposed ANN-based model was also tested with another 14 datasets outside the one used in developing the models and compared with more classical models. The coefficient of the determination (R²) of the proposed ANN-based model was the highest.     

Fecal indicator bacteria and Salmonella in ponds managed as bird habitat, San Francisco Bay, California, USA

Throughout the world, coastal resource managers are encouraging the restoration of previously modified coastal habitats back into wetlands and managed ponds for their ecosystem value. Because many coastal wetlands are adjacent to urban centers and waters used for human recreation, it is important to understand how wildlife can affect water quality. We measured fecal indicator bacteria (FIB) concentrations, presence/absence of Salmonella, bird abundance, and physico-chemical parameters in two coastal, managed ponds and adjacent sloughs for 4 weeks during the summer and winter in 2006. We characterized the microbial water quality in these waters relative to state water-quality standards and examined the relationship between FIB, bird abundance, and physico-chemical parameters. A box model approach was utilized to determine the net source or sink of FIB in the ponds during the study periods. FIB concentrations often exceeded state standards, particularly in the summer, and microbial water quality in the sloughs was generally lower than in ponds during both seasons. Specifically, the inflow of water from the sloughs to the ponds during the summer, more so than waterfowl use, appeared to increase the FIB concentrations in the ponds. The box model results suggested that the ponds served as net wetland sources and sinks for FIB, and high bird abundances in the winter likely contributed to net winter source terms for two of the three FIB in both ponds. Eight serovars of the human pathogen Salmonella were isolated from slough and pond waters, although the source of the pathogen to these wetlands was not identified. Thus, it appeared that factors other than bird abundance were most important in modulating FIB concentrations in these ponds.     

An intelligent approach to assessing the effect of building occupancy on building cooling load prediction

Building cooling load prediction is one of the key factors in the success of energy-saving measures. Many computational models available in the industry have been developed from either forward or inverse modeling approaches. However, these models usually require extensive computer resources and lengthy computation. This paper discusses the use of the multi-layer perceptron (MLP) model, one of the artificial neural network (ANN) models widely adopted in engineering applications, to estimate the cooling load of a building. The training samples used include weather data obtained from the Hong Kong Observatory and building-related data acquired from an existing prestigious commercial building in Hong Kong that houses a mega complex and operates 24 h a day. The paper also discusses the practical difficulties encountered in acquiring building-related data. In contrast to other studies that use ANN models to predict building cooling load, this paper includes the building occupancy rate as one of the input parameters used to determine building cooling load. The results demonstrate that the building occupancy rate plays a critical role in building cooling load prediction and significantly improves predictive accuracy.     

Using extreme learning machines for short-term urban water demand forecasting

This study explores the ability of various machine learning methods to improve the accuracy of urban water demand forecasting for the city of Montreal (Canada). Artificial Neural Network (ANN), Support Vector Regression (SVR) and Extreme Learning Machine (ELM) models, in addition to a traditional model (Multiple linear regression, MLR) were developed to forecast urban water demand at lead times of 1 and 3 days. The use of models based on ELM in water demand forecasting has not previously been explored in much detail. Models were based on different combinations of the main input variables (e.g., daily maximum temperature, daily total precipitation and daily water demand), for which data were available for Montreal, Canada between 1999 and 2010. Based on the squared coefficient of determination, the root mean square error and an examination of the residuals, ELM models provided greater accuracy than MLR, ANN or SVR models in forecasting Montreal urban water demand for 1 day and 3 days ahead, and can be considered a promising method for short-term urban water demand forecasting.