Electrocoagulation/Electroflotation Process for Removal of Organics and Microplastics in Laundry Wastewater

In the present research, laundry wastewater treatment is studied using the electrocoagulation/electroflotation process. For the optimization of treatment conditions such as electrode type (Al–Al, Al–Fe, Fe–Fe, and Fe–Al), initial pH (5–9), current (0.54–2.16 A), and application time (15–60 min), response surface methodology is used. Removal efficiencies of chemical oxygen demand (COD), color, anionic surfactant, microplastic, and phosphate are studied. It is determined that the most effective removal is obtained with 2.16 A current, pH 9, and 60 min reaction time using Fe–Al electrode. Here, 91%, 94%, 100%, and 98% removal efficiencies are achieved for COD, surfactant, color, and microplastic, respectively. The operating cost of the combined process is calculated as $1.32 m^?3 for the optimum removal parameters. The adsorption kinetics study shows that the removal follows second‐order kinetics. The laboratory‐scale test results indicate that the electrocoagulation/electroflotation process is feasible for the treatment of laundry wastewater.     

地震诱发的侧向水平位移神经网络预测模型

在对地震液化诱发的侧向水平位移预测模型评述的基础上，分析了地震、地形、土质等实测数据与侧向水平水移之间的相互关系，并提出了侧向位平位移神经网络预测模型。模型较好地反映了参数之间复杂的非线性关系，网络预测结果与实测数据较为吻合，两者之间相关系数为0．9左右。模型数据分析结果表明侧向位移随着距自由临空面距离（L）的增加而呈双曲线关系下降，随液化层厚度的增加而增加。不同L条件一临空面高度与侧向位移之间有一灵敏变化区，即当H约等于4－7m之间时，侧向位移急剧变化。     

Comparison of bootstrap confidence intervals for an ANN model of a karstic aquifer response

Following many applications artificial neural networks (ANNs) have found in hydrology, a question has been rising for quantification of the output uncertainty. A pre‐optimized ANN simulated the hydraulic head change at two observation wells, having as input hydrological and meteorological parameters. In order to calculate confidence intervals (CI) for the ANN output two bootstrap methods were examined namely bootstrap percentile and BCa (Bias‐Corrected and accelerated). The actual coverage of the CI was compared to the theoretical coverage for different certainty levels as a means of examining the method's reliability. The results of this work support the idea that the bootstrap methods provide a simple tool for confidence interval computation of ANNs. Comparing the two methods, the percentile requires fewer calculations and yields narrower intervals with similar actual coverage to that of BCa. Overall, the actual coverage was proved lower than desired when not modeled points were present in the data subset. Copyright © 2011 John Wiley & Sons, Ltd.     

Genetic programming model for forecast of short and noisy data

Though forecasting of river flow has received a great deal of attention from engineers and researchers throughout the world, this still continues to be a challenging task owing to the complexity of the process. In the last decade or so, artificial neural networks (ANNs) have been widely applied, and their ability to model complex phenomena has been clearly demonstrated. However, the success of ANNs depends very crucially on having representative records of sufficient length. Further, the forecast accuracy decreases rapidly with an increase in the forecast horizon. In this study, the use of the Darwinian theory‐based recent evolutionary technique of genetic programming (GP) is suggested to forecast fortnightly flow up to 4‐lead. It is demonstrated that short lead predictions can be significantly improved from a short and noisy time series if the stochastic (noise) component is appropriately filtered out. The deterministic component can then be easily modelled. Further, only the immediate antecedent exogenous and/or non‐exogenous inputs can be assumed to control the process. With an increase in the forecast horizon, the stochastic components also play an important role in the forecast, besides the inherent difficulty in ascertaining the appropriate input variables which can be assumed to govern the underlying process. GP is found to be an efficient tool to identify the most appropriate input variables to achieve reasonable prediction accuracy for higher lead‐period forecasts. A comparison with ANNs suggests that though there is no significant difference in the prediction accuracy, GP does offer some unique advantages. Copyright © 2006 John Wiley & Sons, Ltd.     

基于人工神经网络的钢筋混凝土异型节点滞回模型

讨论了应用神经网络技术模拟钢筋混凝土材料滞回行为的若干问题，建立了钢筋混凝土异型节点滞回特性的BP神经网络预测模型，并获得了满意的结果。分析表明，神经网络计算是钢筋混凝土构件抗震性能研究中的一种很有潜力的新方法。     

Multivariate approaches optimize locations of groundwater pumping facilities for different hydrogeological scales

A number of optimization approaches regarding the design location of groundwater pumping facilities in heterogeneous porous media have elicited little discussion. However, the location of groundwater pumping facilities is an important factor because it affects water resource usage. This study applies two optimization approaches to estimate the best recharge zone and suitable locations of the pumping facilities in southwestern Taiwan for different hydrogeological scales. First, for the regional scale, this study employs numerical modelling, MODFLOW‐96, to simulate groundwater direction and the optimal recharge zone in the study area. Based on the model's calibration and verification results, this study preliminarily utilizes the simulated spatial direction of groundwater and compares the safe yield for each well group in order to determine the best recharge zone. Additionally, for the local scale, the micro‐hydrogeological characteristics are considered before determining the design locations of the pumping facilities. According to drawdown record data from six observation wells derived from pumping tests at the best recharge area, this study further utilizes the modified artificial neural network approach to improve the accuracy of the estimation parameters as well as to analyse the direction and anisotropy of the hydraulic conductivities of an equivalent homogeneous aquifer. The results suggested that the best locations for the pumping facilities are along the more permeable major direction. Copyright © 2011 John Wiley & Sons, Ltd.     

建筑结构利用TLCD减振的神经网络智能控制

本文提出了建筑结构利用调谐液体柱型阻尼器（ＴＬＣＤ）减振的神经网络智能控制方法。首先阐述了确定ＴＬＣＤ半主动控制策略;然后利用ＢＰ人工神经网络方法计算并控制ＴＬＣＤ隔板孔洞的面积,以调节和控制阻尼比＆Ｔ,实现对建筑结构的智能控制。地震作用下的数值分析表明,本文所述的方法是十分有效的。     

Evaluation of nonlinear models for precipitation forecasting

Prediction of factors affecting water resources systems is important for their design and operation. In hydrology, wavelet analysis (WA) is known as a new method for time series analysis. In this study, WA was combined with an artificial neural network (ANN) for prediction of precipitation at Varayeneh station, western Iran. The results obtained were compared with the adaptive neural fuzzy inference system (ANFIS) and ANN. Moreover, data on relative humidity and temperature were employed in addition to rainfall data to examine their influence on precipitation forecasting. Overall, this study concluded that the hybrid WANN model outperformed the other models in the estimation of maxima and minima, and is the best at forecasting precipitation. Furthermore, training and transfer functions are recommended for similar studies of precipitation forecasting.     

Optimal selection of artificial neural network parameters for the prediction of a karstic aquifer's response

The simulation of karstic aquifers is difficult using traditional groundwater numerical simulators, as the exact knowledge of the hydraulic characteristics of the physical system in small scale is rarely available and the numerical simulators produce results of limited reliability. In the present work, artificial neural networks (ANNs) are utilized to predict the response of a karstic aquifer, using the hydraulic head change per time step rather than the hydraulic head itself as output parameter of the network. As it will be demonstrated, in the first case a better approximation of the physical system's response is achieved as the change of the hydraulic head is more naturally connected to the input parameters of the network, which model the aquatic equilibrium of the system. The correlation of rainfall and hydraulic head change per time step was initially used to determine the time lag of the rainfall input data, which represents the time needed by the rainfall to percolate and reach the water table. In a second step, a differential evolution (DE) algorithm is utilized for the optimal selection of rainfall time lag as well as ANN's architecture and training parameters. Although a time consuming procedure, the improvement obtained suggests that the empirical determination of the ANN parameters and structure is not always sufficient and an optimization procedure, which minimizes the training and evaluation errors of the ANN, may provide substantially better simulation results. The optimized networks were finally used for midterm predictions (30 to 90 days ahead) of the hydraulic head, showing the ability of the ANN with hydraulic head change as output parameter to provide predictions with high accuracy at the end of the considered time period. Copyright © 2009 John Wiley & Sons, Ltd.     

Comparison of data-driven modelling techniques for river flow forecasting

Abstract

Accurate forecasting of streamflow is essential for the efficient operation of water resources systems. The streamflow process is complex and highly nonlinear. Therefore, researchers try to devise alterative techniques to forecast streamflow with relative ease and reasonable accuracy, although traditional deterministic and conceptual models are available. The present work uses three data-driven techniques, namely artificial neural networks (ANN), genetic programming (GP) and model trees (MT) to forecast river flow one day in advance at two stations in the Narmada catchment of India, and the results are compared. All the models performed reasonably well as far as accuracy of prediction is concerned. It was found that the ANN and MT techniques performed almost equally well, but GP performed better than both these techniques, although only marginally in terms of prediction accuracy in normal and extreme events.

Citation Londhe, S. & Charhate, S. (2010) Comparison of data-driven modelling techniques for river flow forecasting. Hydrol. Sci. J. 55(7), 1163–1174.     

Evaluation of temporal concentration profiles for ungauged rivers following pollution incidents

Abstract

In order to predict the impact of pollution incidents on rivers, it is necessary to predict the dispersion coefficient and the flow velocity corresponding to the discharge in the river of interest. This paper explores methods for doing this, particularly with a view to applications on ungauged rivers, i.e. those for which little hydraulic or morphometric data are available. An approach based on neural networks, trained on a wide-ranging database of optimized parameter values from tracer experiments and corresponding physical variables assembled for American and European rivers, is proposed. Tests using independent cases showed that the neural networks generally gave more reliable parameter estimates than a second-order polynomial regression approach. The quality of predictions of temporal concentration profiles was heavily influenced by the accuracy of the velocity prediction.

Citation Piotrowski, A. P., Napiorkowski, J. J., Rowinski, P. M. & Wallis, S. G. (2011) Evaluation of temporal concentration profiles for ungauged rivers following pollution incidents. Hydrol. Sci. J. 56(5), 883–894.     

Probabilistic and ensemble simulation approaches for input uncertainty quantification of artificial neural network hydrological models

Artificial neural network (ANN) has been demonstrated to be a promising modelling tool for the improved prediction/forecasting of hydrological variables. However, the quantification of uncertainty in ANN is a major issue, as high uncertainty would hinder the reliable application of these models. While several sources have been ascribed, the quantification of input uncertainty in ANN has received little attention. The reason is that each measured input quantity is likely to vary uniquely, which prevents quantification of a reliable prediction uncertainty. In this paper, an optimization method, which integrates probabilistic and ensemble simulation approaches, is proposed for the quantification of input uncertainty of ANN models. The proposed approach is demonstrated through rainfall-runoff modelling for the Leaf River watershed, USA. The results suggest that ignoring explicit quantification of input uncertainty leads to under/over estimation of model prediction uncertainty. It also facilitates identification of appropriate model parameters for better characterizing the hydrological processes.     

Optimization of the Coagulation–Flocculation Process for Slaughterhouse Wastewater Using Response Surface Methodology

Slaughterhouse wastewater is one of the main sources of environmental pollutants, containing a high amount of organic matter (chemical oxygen demand (COD), biochemical oxygen demand (BOD)), total nitrogen (TN), total suspended solids (TSS), total phosphorus (TP), grease, and oil. The main aim of the present research is optimizing the coagulation–flocculation process and examining the effects of experimental factors with each other, for example, pH, the concentration of two different coagulants (FeCl₃ and alum), rapid mixing rate, and settling time. Therefore, it is aimed to treat slaughterhouse wastewater using the coagulation–flocculation process with the optimization of the response surface methodology (RSM). COD, turbidity, and suspended solids (SS) of the treated wastewater are chosen as the response variables. Furthermore, the optimal conditions for three responses are acquired by employing the desirability function approach. When the experimental results of two coagulants are compared, it is observed that the alum coagulant gave better results for the three responses. The alum coagulant utilized in the present research is able to increase COD, SS, and turbidity removal efficiency by 75.25%, 90.16%, and 91.18%, respectively. It is possible to optimize coagulation–flocculation by utilizing the RSM analysis, which proves that coagulation can pre‐treat slaughterhouse wastewater.     

A Basic Water Quality Model for the River Neckar: Part 2 – model‐based analysis of the oxygen budget and scenarios

In this paper the Basic Water Quality Model (BWQM) for the central part of River Neckar is used to analyse the oxygen budget and to assess the potentials of various measures to prevent or mitigate critical dissolved oxygen (DO) declines. It is shown that the oxygen budget is mainly governed by phytoplankton dynamics. The excessive growth of algae and the sudden break down of the resulting algal blooms may cause episodic DO depressions. Therefore, to stabilise the oxygen budget in a sustainable way, eutrophication has to be controlled within the central part of River Neckar and the upstream regions. The only feasible way to reach this goal appears to be a further drastic reduction of phosphorus emissions. In addition, it is indispensable to hold the very high standards of biochemical oxygen demand and ammonium retention at the wastewater treatment plants. A worse performance of the treatment plants would dramatically aggravate critical DO declines which may be caused by algae dynamics. As long as the oxygen budget is not completely stabilised, weir and turbine aeration can be used to mitigate DO depressions. It could be shown that the potentials of these measures suffice to keep DO at a tolerable level. However, due to the long travel times in River Neckar, it is important to start aeration up to several days before the DO minimum is reached.     

Assessment of sediment transport approaches for sand-bed rivers by means of machine learning

Abstract

The quantification of the sediment carrying capacity of a river is a difficult task that has received much attention. For sand-bed rivers especially, several sediment transport functions have appeared in the literature based on various concepts and approaches; however, since they present a significant discrepancy in their results, none of them has become universally accepted. This paper employs three machine learning techniques, namely artificial neural networks, symbolic regression based on genetic programming and an adaptive-network-based fuzzy inference system, for the derivation of sediment transport formulae for sand-bed rivers from field and laboratory flume data. For the determination of the input parameters, some of the most prominent fundamental approaches that govern the phenomenon, such as shear stress, stream power and unit stream power, are utilized and a comparison of their efficacy is provided. The results obtained from the machine learning techniques are superior to those of the commonly-used sediment transport formulae and it is shown that each of the input combinations tested has its own merit, as they produce similarly good results with respect to the data-driven technique employed.

Editor Z.W. Kundzewicz     

On the importance of training methods and ensemble aggregation for runoff prediction by means of artificial neural networks

ABSTRACT

Artificial neural networks (ANNs) become widely used for runoff forecasting in numerous studies. Usually classical gradient-based methods are applied in ANN training and a single ANN model is used. To improve the modelling performance, in some papers ensemble aggregation approaches are used whilst in others, novel training methods are proposed. In this study, the usefulness of both concepts is analysed. First, the applicability of a large number of population-based metaheuristics to ANN training for runoff forecasting is tested on data collected from four catchments, namely upper Annapolis (Nova Scotia, Canada), Biala Tarnowska (Poland), upper Allier (France) and Axe Creek (Victoria, Australia). Then, the importance of the search for novel training methods is compared with the importance of the use of a very simple ANN ensemble aggregation approach. It is shown that although some metaheuristics may slightly outperform the classical gradient-based Levenberg-Marquardt algorithm for a specific catchment, none performs better for the majority of the tested ones. One may also point out a few metaheuristics that do not suit ANN training at all. On the other hand, application of even the simplest ensemble aggregation approach clearly improves the results when the ensemble members are trained by any suitable algorithms.

EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR E. Toth     

An explicit finite element－finite difference method for analyzing the effect of visco－elastic local topography on the earthquake motion

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Polynomial chaos expansion and response surface method for nonlinear modelling of reference evapotranspiration

The feasibility of polynomial chaos expansion (PCE) and response surface method (RSM) models is investigated for modelling reference evapotranspiration (ET₀). The modelling results of the proposed models are validated against the M5 model tree and multi-layer perceptron neural network (MLPNN) methods. Two meteorological stations, Isparta and Antalya, in the Mediterranean region of Turkey, are inspected. Various input combinations of daily air temperature, solar radiation, wind speed and relative humidity are constructed as input attributes for the ET₀. Generally, the modelling accuracy is increased by increasing the number of inputs. Including wind speed in the model inputs considerably increases their accuracy in modelling ET₀. Mean absolute error (MAE), root mean square error (RMSE), agreement index (d) and Nash-Sutcliffe efficiency (NSE) are used as comparison criteria. The PCE is the most accurate model in estimating daily ET₀, giving the lowest MAE (0.036 and 0.037 mm) and RMSE (0.047 and 0.050 mm) and the highest d (0.9998 and 0.9999) and NSE (0.9992 and 0.9996) with the four-input PCE models for Isparta and Antalya, respectively.     

Quantitative analysis of riverbank groundwater flow for the Qinhuai River,China, and its influence factors

Interactions of surface water and groundwater (SW–GW) play an important role in the physical, chemical, and ecological processes of riparian zones. The main objective of this study was to describe the two‐dimensional characteristics of riverbank SW–GW interactions and to quantify their influence factors. The SW–GW exchange fluxes for six sections (S1 to S6) of the Qinhuai River, China, were estimated using a heat tracing method, and field hydrogeological and thermodynamic parameters were obtained via inverse modelling. Global sensitivity analysis was performed to compare the effects of layered heterogeneity of hydraulic conductivity and river stage variation on SW–GW exchange. Under the condition of varied river stage, only the lateral exchange fluxes at S1 apparently decreased during the monitoring period, probably resulting from its relatively higher hydraulic conductivity. Meanwhile, the SW–GW exchanges for the other five sections were quite stable over time. The lateral exchange fluxes were higher than the vertical ones. The riverbank groundwater flow showed different spatial variation characteristics for the six sections, but most of the higher exchange fluxes occurred in the lower area of a section. The section with larger hydraulic conductivity has an apparent dynamic response to surface water and groundwater level differences, whereas lower permeabilities severely reduced the response of groundwater flow. The influence of boundary conditions on SW–GW interactions was restricted to a limited extent, and the impact extent will expand with the increase of peak water level and hydraulic conductivity. The SW–GW head difference was the main influence factors in SW–GW interactions, and the influence of both SW–GW head difference and hydraulic conductivity decreased with an increase of the distance from the surface water boundary. For each layer of riverbank sediment, its hydraulic conductivity had greater influence on its groundwater flow than the other layers, whereas it had negligible effects on its overlying/underlying layers. Consequently, the variations in river stage and hydraulic conductivity were the main factors influencing the spatial and temporal characteristics of riverbank groundwater flow, respectively.