Daily streamflow modelling using autoregressive moving average and artificial neural networks models: case study of Çoruh basin,Turkey

Streamflow modelling is a quite important issue for water resources system planning and management projects, such as dam construction, reservoir operation and flood control. This study demonstrates the application of artificial neural networks (ANN) and autoregressive moving average (ARMA) models for modelling daily streamflow in Çoruh basin, Turkey, where there are numerous highly critical power plants either under construction or being projected. Daily streamflow records from nine gauging stations located in the basin were used in this study. In the first phase of our study, ANN and ARMA models were obtained using daily streamflow. In the second phase, 100 synthetic streamflow series were generated using previously determined ANN and ARMA models in order to ensure the preservation of main statistical characteristics of the historical time series. The results have showed that the historical time series have similar statistical parameters to those of the generated time series at 95% confidence level.     

Exploring relationships between semi-variogram and spatial autoregressive models

This paper seeks to continue the building of a common foundation for spatial statistics and geostatistics. Equations from the conditional autoregressive (CAR) model of spatial statistics for estimating missing geo-referenced data have been found to be exactly those best linear unbiased estimates obtained with the exponential semi-variogram model of kriging, but in terms of the inverse covariance matrix rather than the covariance matrix itself. Further articulation of such relations, between the moving average (MA) and simultaneous autoregressive (SAR) or autoregressive response (AR) models of spatial statistics, and, respectively, the linear and Gaussian semi-variogram models of kriging, is outlined. The exploratory graphical and numerical work summarized in this paper indicates the following: (a) there is evidence to pair the moving average and linear models; (b) the simultaneous autoregressive and autoregressive response model pair with a Bessel function (modified of the second kind and order one) rather than the Gaussian semi-variogram model; (c) both specification error and measurement error can give rise to the nugget effect discussed in geostatistics; (d) restricting estimation to a geographic subregion introduces edge effects that increasingly bias semi-variogram model parameter estimates as the degree of spatial autocorrelation increases toward its upper limit; and (e) the theoretical spectral density function for a simultaneous autoregressive model is a direct extension of that for the conditional autoregressive model.     

Performance of stochastic approaches for forecasting river water quality

This study analysed water quality data collected from the river Ganges in India from 1981 to 1990 for forecasting using stochastic models. Initially the box and whisker plots and Kendall's tau test were used to identify the trends during the study period. For detecting the possible intervention in the data the time series plots and cusum charts were used. The three approaches of stochastic modelling which account for the effect of seasonality in different ways, i.e. multiplicative autoregressive integrated moving average (ARIMA) model, deseasonalised model and Thomas–Fiering model were used to model the observed pattern in water quality. The multiplicative ARIMA model having both nonseasonal and seasonal components were, in general, identified as appropriate models. In the deseasonalised modelling approach, the lower order ARIMA models were found appropriate for the stochastic component. The set of Thomas–Fiering models were formed for each month for all water quality parameters. These models were then used to forecast the future values. The error estimates of forecasts from the three approaches were compared to identify the most suitable approach for the reliable forecast. The deseasonalised modelling approach was recommended for forecasting of water quality parameters of a river.     

Statistical analysis of environmental data as the basis of forecasting: an air quality application

A statistical analysis technique is used for the development of an environmental forecasting tool. More specifically, a stochastic autoregressive integrated moving average (ARIMA) model is developed for maximum ozone concentration forecasts in Athens, Greece. For this purpose, the Box-Jenkins approach is applied for the analysis of a 9-year air quality observation record. The model developed is checked against real data for 1 year. Results show a good index of agreement, accompanied by a weakness in forecasting alarms. Finally, suggestions are made regarding the enrichment of the approach used in order to improve the forecasting performance.     

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.     

基于BP神经网络改进算法在地铁隧道施工中的沉降预测

为使地铁隧道在施工中沉降监测数据具有一定的预见性,分别采用了BP神经网络改进算法的预测模型、传统BP神经网络预模型以及基于时间序列的三次指数平滑法预测模型对地铁隧道施工中的沉降监测数据进行了预测。对其预测结果进行分析,得出了BP神经网络改进算法模型预测精度优于传统BP神经网络模型以及基于时间序列的三次指数平滑法模型预测精度的结论。     

Development of room temperature and relative humidity linear parametric models for an open office using BMS data

This study investigates Box-Jenkins (BJ), autoregressive with external inputs (ARX), autoregressive moving average with external inputs (ARMAX) and output error (OE) models to identify the thermal behaviour of an office positioned in a modern commercial building in London. These models can all be potentially used for improving the performance of the thermal environment control system. External and internal climate data, recorded over the summer, autumn and winter seasons, were used to build and validate the models. The paper demonstrates the potential of using linear parametric models to predict room temperature and relative humidity for different time scales (30 min or 2 h ahead). The prediction performance is evaluated using the criteria of goodness of fit, coefficient of determination, mean absolute error and mean squared error between predicted model output and real measurements. The results demonstrate that all models provide reasonably good predictions but the BJ model outperforms the ARMAX and ARX models.     

基于小波分解的城市供水管网短期水量负荷预测

利用小波分解和人工神经网络相结合的方法建立了城市供水管网短期水量负荷的组合预测模型。该方法首先利用小波分解技术将时负荷水量分解为相对简单的带通分量信号，然后根据各分量信号的特点分别建立独立的神经网络预测模型，最后将预报结果集成。由于小波分解后各分量的频率相对单一，因而可有效缩短网络训练时间，提高计算速度。仿真计算结果表明，该方法建模合理、计算量适中，可准确预测管网水量。     

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.     

Stochastic modelling of mean monthly flows of Karasu River,in Turkey

Stochastic modelling of streamflows is vital for planning water resource systems. In this study, a stochastic model of the mean monthly streamflows at 2154 A?a?ιka?dariç Gauging Station on Karasu River was constructed. Studies were carried out using data from the water yearbooks published by chk later onEIE. The modelling procedure for streamflows with constant coefficient autoregressive moving average (ARMA) models was given in detail and indicated models were constructed. Analysis with streamflows at 2154 A?a?ιka?dariç Gauging Station showed that the autoregressive (AR) (1) model is the most appropriate model among the competing models. While selecting the most efficient model the Akaike information criterion (AIC) was used. The Port–Manteau test showed that residuals are white noise series. Using the AR(1) model, 100 synthetic series were generated and the time series generated were found to have the same statistical parameters (monthly mean, monthly standard deviation and autocorrelation) as historic time series within 95% confidence intervals.     

Stochastic modeling of an industrial activated sludge process

The purpose of this paper is to develop a Box-Jenkins time series transfer function model of the effluent COD using the influent COD, from a sewage treatment process. The data is from an industrial waste treatment plant encompassing a 14-month period.The work was done in 3 steps: (1) Box-Jenkins ARIMA (autoregressive integrated moving average) modeling of the influent COD; (2) ARIMA modeling of the effluent COD; and (3) development of a transfer function model for the waste treatment process.It was found that single difference models best described influent and effluent COD. For the transfer function model, the influent COD had an insignificant effect on the effluent COD.     

An ARIMA-NARX hybrid model for forecasting urban water consumption (case study: Tehran metropolis)

ABSTRACT

In this research, an ARIMA-NARX (Autoregressive Integrated Moving Average-Nonlinear Auto-Regressive eXogenous) hybrid model is proposed to forecast daily Urban Water Consumption (UWC) for Tehran Metropolis. The linear and nonlinear component of the UWC was forecast by ARIMA as a linear forecasting model and the artificial neural network as a nonlinear forecasting model, respectively. An alternative hybrid model including sunshine hour in addition to the previous studies’ predictors (the minimum, maximum and average temperature, relative humidity and precipitation) was selected as the superior alternative model. Then, the performance of proposed model was compared with ARIMA and NARX models. The results showed that the hybrid model, which benefits from capability of both linear and nonlinear models, has a higher accuracy than the other two models in forecasting UWC. Therefore, the proposed hybrid model has better results in UWC forecasting and, as a consequence, better urban water reservoir management will be provided.     

A Bayesian forecasting approach to constructing regional input-output based employment multipliers

A Bayesian mixed estimation framework is used to examine the forecast accuracy of alternative closures of an input-output model for the Oklahoma economy. The closures correspond to textbook Type I and Type II multipliers, as well as variations of extended input-output and Type IV multipliers. Relative forecast performance of the alternative IO model closures determines which set of multipliers should be used for impact analysis. The exercise reveals differences in forecast accuracy across alternative IO model closures, suggesting that before closures of a particular IO model are adopted, they should be tested for accuracy in predicting the time series data for the regional economy under scrutiny. Received: 26 November 2000 / Accepted: 27 November 2001 RID="*" ID="*" An earlier version of this article was presented at the 47th North American Meetings of the RSAI, Chicago, IL. I would like to thank Stephan Weiler and three anonymous referees for helpful comments.     

Performance studies of R433A in a direct expansion solar-assisted heat pump

ABSTRACT

Energy assessment of a simple direct expansion solar-assisted heat pump system has been experimentally assessed with R433A as a possible alternative to R22. An artificial neural network integrated genetic algorithm model was developed to assess the performance system. The data obtained from the experimentation at different ambient conditions are used as the training data for the ANN network. The back propagation learning mechanism with variants Lavenberg-Maguardt with 20 neurons in the hidden layer were used in modelling of ANN. The values obtained from the analysis using ANN are optimised further by integrating the ANN procedure with GA. The results indicated that R433A has 6.4% and 1% lower instantaneous compressor power consumption and heating capacity compared to R22. Energy performance ratio of R433A was found to be about 5.67% higher compared to R22. The results confirmed that R433A can be used as a possible alternative to R22 DX-SAHP systems.     

Monthly river flow forecasting by an adaptive neuro-fuzzy inference system

In this study, the applicability of an adaptive neuro-fuzzy inference system (ANFIS) to forecast for monthly river flows is investigated. For this, the Göksu river in the Seyhan catchment located in southern Turkey was chosen as a case study. The river flow forecasting models having various input structures are trained and tested by the ANFIS method. The results of ANFIS models for both training and testing are evaluated and the best-fit forecasting model is determined. The best-fit model is also trained and tested by feed forward neural networks (FFNN) and traditional autoregressive (AR) methods, and the performances of the models are compared. Moreover, ANFIS and FFNN models are verified by a validation data set including river flow data records during the time period 1997–2000. The results demonstrate that ANFIS can be applied successfully and provides high accuracy and reliability for monthly river flow forecasting.     

ANN and ANFIS performance prediction models for hydraulic impact hammers

Hydraulic impact hammers are mechanical excavators that can be used in tunneling projects economically under geologic conditions suitable for rock breakage by indentation. However, there is relatively less published material in the literature in relation to predicting the performance of that equipment employing rock properties and machine parameters. In tunnel excavation projects, there is often a need for accurate prediction the performance of such machinery. The poor prediction of machine performance can lead to very costly contractual claims. In this study, the application of soft computing methods for data analysis called artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) to predict the net breaking rate of an impact hammer is demonstrated. The prediction capabilities offered by ANN and ANFIS were shown by using field data of obtained from metro tunnel project in Istanbul, Turkey. For this purpose, two prediction models based on ANN and ANFIS were developed and the results obtained from those models were then compared to those of multiple regression-based predictions. Various statistical performance indexes were used to compare the performance of those prediction models. The results suggest that the proposed ANFIS-based prediction model outperforms both ANN model and the classical multiple regression-based prediction model, and thus can be used to produce a more accurate and reliable estimate of impact hammer performance from Schmidt hammer rebound hardness (SHRH) and rock quality designation (RQD) values obtained from the field tests.     

Forecasting of ozone episode days by cost-sensitive neural network methods

Forecasting the occurrence of ozone episode days can be regarded as an imbalanced dataset classification problem. Since the standard artificial neural network (ANN) methods cannot make accurate predictions of such a problem, two cost-sensitive ANN methods, cost-penalty and moving threshold, were used in this study. The models classify each day as episode or non-episode according to the standard of daily maximum 8 h O₃ concentration. The ozone measurements from six monitoring stations in Taiwan were used for model training and performance evaluation. Two different input datasets, regional and single-site, were generated from raw air quality and meteorological observations. According to the numerical experiments, the predictions based on the regional dataset are much better than those obtained from the single-site dataset. Two cost-sensitive ANN methods were evaluated by receiver operating characteristic (ROC) curves. It was found that the results obtained by the two approaches are similar. If the misclassification costs are known, the cost-sensitive method can minimise the total costs. If the misclassification costs are unknown, the cost-sensitive ANN can obtain a better forecast than the standard ANN method when an appropriate cost ratio is used. For clean areas where episode days are very rare, the forecasts are poor for all methods.     

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.     

Prediction of Onset of Corrosion in Concrete Bridge Decks Using Neural Networks and Case-Based Reasoning

Abstract:   This article proposes a methodology for predicting the time to onset of corrosion of reinforcing steel in concrete bridge decks while incorporating parameter uncertainty. It is based on the integration of artificial neural network (ANN), case-based reasoning (CBR), mechanistic model, and Monte Carlo simulation (MCS). A probabilistic mechanistic model is used to generate the distribution of the time to corrosion initiation based on statistical models of the governing parameters obtained from field data. The proposed ANN and CBR models act as universal functional mapping tools to approximate the relationship between the input and output of the mechanistic model. These tools are integrated with the MCS technique to generate the distribution of the corrosion initiation time using the distributions of the governing parameters. The proposed methodology is applied to predict the time to corrosion initiation of the top reinforcing steel in the concrete deck of the Dickson Bridge in Montreal. This study demonstrates the feasibility, adequate reliability, and computational efficiency of the proposed integrated ANN-MCS and CBR-MCS approaches for preliminary project-level and also network-level analyses.     

Prediction of bed load sediments using different artificial neural network models

Modeling and prediction of bed loads is an important but difficult issue in river engineering. The introduced empirical equations due to restricted applicability even in similar conditions provide different accuracies with each other and measured data. In this paper, three different artificial neural networks (ANNs) including multilayer percepterons, radial based function (RBF), and generalized feed forward neural network using five dominant parameters of bed load transport formulas for the Main Fork Red River in Idaho-USA were developed. The optimum models were found through 102 data sets of flow discharge, flow velocity, water surface slopes, flow depth, and mean grain size. The deficiency of empirical equations for this river by conducted comparison between measured and predicted values was approved where the ANN models presented more consistence and closer estimation to observed data. The coefficient of determination between measured and predicted values for empirical equations varied from 0.10 to 0.21 against the 0.93 to 0.98 in ANN models. The accuracy performance of all models was evaluated and interpreted using different statistical error criteria, analytical graphs and confusion matrixes. Although the ANN models predicted compatible outputs but the RBF with 79% correct classification rate corresponding to 0.191 network error was outperform than others.