LOCAL SCOUR AROUND CYLINDRICAL OBJECTS1

ABSTRACT: Several predictors for the maximum local scour around cylindrical objects are compared to available experimental data. The range of flow parameters for which these formulas are valid are presented. The best predictors among those compared in this study were identified. Based on the available data a formula for estimating local scour around cylindrical objects is also given.     

湛江某海上风电桩基局部冲刷特征研究

海上风电场桩基局部冲刷是工程设计与运行阶段的重要参数之一。基于湛江某海上风电场桩基3次现场局部冲刷实测数据,进行冲刷坑最大深度、冲刷坑半径和冲淤变化特征的分析与研究;根据桩基局部冲刷经验公式,采用工程海域实测海洋水文动力学数据进行最大冲刷深度与冲刷半径的计算,并进行公式计算值的对比与分 析。结果表明:桩基础在防冲刷设施的保护下,3次实测最大冲刷深度基本稳定为4.0 m,最大冲刷深度与桩径之比为0.57。而经验公式的最大冲刷深度与桩径之比均超过了1.1,说明桩基防冲刷设施取得了一定的效果,冲刷坑半径的计算值与现场实测值吻合较好。建议海上风电场在运行阶段进一步加强桩基冲刷坑监测与防护。     

COARSE WOODY DEBRIS AND CHANNEL MORPHOLOGY: A FLUME STUDY1

ABSTRACT: In recent years, logs and other structures have been added to streams for the purposes of altering channel morphology to improve fish habitat. This flume study was conducted to evaluate the effects of coarse woody debris on local channel morphology. Wooden dowels were used to simulate the effects of individual logs in a stream, and scour depth and surface area were determined at the end of each test run. The maximum scour depth was significantly correlated (90 percent confidence level) with both the vertical orientation of the dowels and the channel opening ratio; the scour surface area was significantly correlated (90 percent confidence level) with both the flow depth and the vertical orientation. Upstream-oriented dowels caused relatively large streambed scour and also deflected flows toward the streambank. Downstream-oriented dowels generally caused less bed scour and appeared to provide better bank protection because flow was generally deflected from the bank. In conjunction with data from field studies, these results provide information on the effects of orientation, hydraulic function, and relative stability of coarse woody debris in streams.     

LAKE WATER QUALITY ASSESSMENT FROM LANDSAT THEMATIC MAPPER DATA USING NEURAL NETWORK: AN APPROACH TO OPTIMAL BAND COMBINATION SELECTION1

Abstract: The concern about water quality in inland water bodies such as lakes and reservoirs has been increasing. Owing to the complexity associated with field collection of water quality samples and subsequent laboratory analyses, scientists and researchers have employed remote sensing techniques for water quality information retrieval. Due to the limitations of linear regression methods, many researchers have employed the artificial neural network (ANN) technique to decorrelate satellite data in order to assess water quality. In this paper, we propose a method that establishes the output sensitivity toward changes in the individual input reflectance channels while modeling water quality from remote sensing data collected by Landsat thematic mapper (TM). From the sensitivity, a hypothesis about the importance of each band can be made and used as a guideline to select appropriate input variables (band combination) for ANN models based on the principle of parsimony for water quality retrieval. The approach is illustrated through a case study of Beaver Reservoir in Arkansas, USA. The results of the case study are highly promising and validate the input selection procedure outlined in this paper. The results indicate that this approach could significantly reduce the effort and computational time required to develop an ANN water quality model.     

The impact of storm events on a riverbed system and its hydraulic conductivity at a site of induced infiltration

The spatial and temporal variability of riverbed vertical hydraulic conductivity (K(v)) was investigated at a site of induced infiltration, associated with a municipal well field, to assess the impact of high-stage events on scour and subsequently the riverbed K(v). Such impacts are important when considering the potential loss of riverbank filtration capacity due to storm events. The study site, in and along the Great Miami River in southwest Ohio, overlaid a highly productive glacial-outwash aquifer. A three-layer model for this system was conceptualized: a top layer of transient sediment, a second layer comprising large sediment resistant to scour, but clogged with finer sediment (the armor/colmation layer), and a third layer that was transitional to the underlying higher-K(v) aquifer. One location was studied in detail to confirm and quantify the conceptual model. Methods included seepage meters, heat-flow modeling, grain-size analyses, laboratory permeameter tests, slug tests and the use of scour chains and pressure-load cells to directly measure the amount of sediment scour and re-deposition. Seepage meter measured riverbed K(v) ranged from 0.017 to 1.7 m/d with a geometric mean of 0.19 m/d. Heat-transport model-calibrated estimates were even lower, ranging from 0.0061 to 0.046 m/d with a mean of 0.017 m/d. The relatively low K(v) was indicative of the clogged armor layer. In contrast, slug tests in the underlying riverbed sediment yielded K(v) values an order of magnitude greater. There was a linear relationship between scour chain measured scour and event intensity with a maximum scour of only 0.098 m. Load-cell pressure sensor data over a 7-month period indicated a total sediment-height fluctuation of 0.42 m and a maximum storm-event scour of 0.28 m. Scour data indicated that the assumed armor/colmation layer almost always remained intact. Based on measured layer conductivities and thicknesses, the overall K(v) of this conceptualized system was 1.6 m/d. Sensitivity analyses indicated that even complete scour of the armor/colmation layer would likely increase the overall K(v) only by a factor of 1.5. Most scour events observed removed only the transient sediment, having very little effect on the entire system indicating low risk of losing filtration capacity during storms. The research, however, focused on the point bar, depositional side of the river. More research of the entire river profile is necessary.     

Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance

A reliable model for any wastewater treatment plant is essential in order to provide a tool for predicting its performance and to form a basis for controlling the operation of the process. This would minimize the operation costs and assess the stability of environmental balance. This process is complex and attains a high degree of nonlinearity due to the presence of bio-organic constituents that are difficult to model using mechanistic approaches. Predicting the plant operational parameters using conventional experimental techniques is also a time consuming step and is an obstacle in the way of efficient control of such processes. In this work, an artificial neural network (ANN) black-box modeling approach was used to acquire the knowledge base of a real wastewater plant and then used as a process model. The study signifies that the ANNs are capable of capturing the plant operation characteristics with a good degree of accuracy. A computer model is developed that incorporates the trained ANN plant model. The developed program is implemented and validated using plant-scale data obtained from a local wastewater treatment plant, namely the Doha West wastewater treatment plant (WWTP). It is used as a valuable performance assessment tool for plant operators and decision makers. The ANN model provided accurate predictions of the effluent stream, in terms of biological oxygen demand (BOD), chemical oxygen demand (COD) and total suspended solids (TSS) when using COD as an input in the crude supply stream. It can be said that the ANN predictions based on three crude supply inputs together, namely BOD, COD and TSS, resulted in better ANN predictions when using only one crude supply input. Graphical user interface representation of the ANN for the Doha West WWTP data is performed and presented.     

APPLICATION OF GREY MODEL AND ARTIFICIAL NEURAL NETWORKS TO FLOOD FORECASTING1

The main focus of this study was to compare the Grey model and several artificial neural network (ANN) models for real time flood forecasting, including a comparison of the models for various lead times (ranging from one to six hours). For hydrological applications, the Grey model has the advantage that it can easily be used in forecasting without assuming that forecast storm events exhibit the same stochastic characteristics as the storm events themselves. The major advantage of an ANN in rainfall‐runoff modeling is that there is no requirement for any prior assumptions regarding the processes involved. The Grey model and three ANN models were applied to a 2,509 km² watershed in the Republic of Korea to compare the results for real time flood forecasting with from one to six hours of lead time. The fifth‐order Grey model and the ANN models with the optimal network architectures, represented by ANN1004 (34 input nodes, 21 hidden nodes, and 1 output node), ANN1010 (40 input nodes, 25 hidden nodes, and 1 output node), and ANN1004T (14 input nodes, 21 hidden nodes, and 1 output node), were adopted to evaluate the effects of time lags and differences between area mean and point rainfall. The Grey model and the ANN models, which provided reliable forecasts with one to six hours of lead time, were calibrated and their datasets validated. The results showed that the Grey model and the ANN1010 model achieved the highest level of performance in forecasting runoff for one to six lead hours. The ANN model architectures (ANN1004 and ANN1010) that used point rainfall data performed better than the model that used mean rainfall data (ANN1004T) in the real time forecasting. The selected models thus appear to be a useful tool for flood forecasting in Korea.     

Predicting Debris Yield From Burned Watersheds: Comparison of Statistical and Artificial Neural Network Models1

Abstract: Alluvial fans in southern California are continuously being developed for residential, industrial, commercial, and agricultural purposes. Development and alteration of alluvial fans often require consideration of mud and debris flows from burned mountain watersheds. Accurate prediction of sediment (hyper‐concentrated sediment or debris) yield is essential for the design, operation, and maintenance of debris basins to safeguard properly the general population. This paper presents results based on a statistical model and Artificial Neural Network (ANN) models. The models predict sediment yield caused by storms following wildfire events in burned mountainous watersheds. Both sediment yield prediction models have been developed for use in relatively small watersheds (50‐800 ha) in the greater Los Angeles area. The statistical model was developed using multiple regression analysis on sediment yield data collected from 1938 to 1983. Following the multiple regression analysis, a method for multi‐sequence sediment yield prediction under burned watershed conditions was developed. The statistical model was then calibrated based on 17 years of sediment yield, fire, and precipitation data collected between 1984 and 2000. The present study also evaluated ANN models created to predict the sediment yields. The training of the ANN models utilized single storm event data generated for the 17‐year period between 1984 and 2000 as the training input data. Training patterns and neural network architectures were varied to further study the ANN performance. Results from these models were compared with the available field data obtained from several debris basins within Los Angeles County. Both predictive models were then applied for hind‐casting the sediment prediction of several post 2000 events. Both the statistical and ANN models yield remarkably consistent results when compared with the measured field data. The results show that these models are very useful tools for predicting sediment yield sequences. The results can be used for scheduling cleanout operation of debris basins. It can be of great help in the planning of emergency response for burned areas to minimize the damage to properties and lives.     

Modelling and Prediction of Diesel Engine Performance using Relevance Vector Machine

Diesel engines are being increasingly adopted by many car manufacturers today, yet no exact mathematical diesel engine model exists due to its highly nonlinear nature. In the current literature, black-box identification has been widely used for diesel engine modelling and many artificial neural network (ANN) based models have been developed. However, ANN has many drawbacks such as multiple local minima, user burden on selection of optimal network structure, large training data size, and over-fitting risk. To overcome these drawbacks, this article proposes to apply an emerging machine learning technique, relevance vector machine (RVM), to model and predict the diesel engine performance. The property of global optimal solution of RVM allows the model to be trained using only a few experimental data sets. In this study, the inputs of the model are engine speed, load, and cooling water temperature, while the output parameters are the brake-specific fuel consumption and the amount of exhaust emissions like nitrogen oxides and carbon dioxide. Experimental results show that the model accuracy is satisfactory even the training data is scarce. Moreover, the model accuracy is compared with that using typical ANN. Evaluation results also show that RVM is superior to typical ANN approach.     

MOUND FORMATION AT CULVERT OUTLETS1

ABSTRACT One component of the local scour process near a culvert outlet is the formation of an aggraded mound downstream of the scour area. This investigation presents a series of observations and empirical relationships depicting the formation, growth, and estimated maximum dimensions of a mound in a uniformly graded sand material due to clear water scour. The maximum dimensions of the mound were correlated to the discharge intensity (Qg^-0.5 D^-2.5), the maximum dimensions of the scour hole, time, and tailwater elevation. The concept of an approximate area of scour influence was developed relating the mound width, scour hole length, and mound length as a function of the culvert diameter and discharge intensity.     

Non-linear autoregressive neural network approach for inside air temperature prediction of a pillar cooler

The volcanic plate made pillar cooler system is designed for outdoor spaces as a heat exchanging medium and reduces the outcoming air temperature which flows through the exhaust port. This paper proposes the use of artificial neural networks (ANNs) to predict inside air temperature of a pillar cooler. For this purpose, at first, three statistically significant factors (outside temperature, airing and watering) influencing the inside air temperature of the pillar cooler are identified as input parameters for predicting the output (inside air temperature) and then an ANN was employed to predict the output. In addition, 70%, 15% and 15% data was chosen from a previously obtained data set during the field trial of the pillar cooler for training, testing and validation, respectively, and then an ANN was employed to predict inside air temperature. The training (0.99918), testing (0.99799) and validation errors (0.99432) obtained from the model indicate that the artificial neural network model (NARX) may be used to predict inside air temperature of pillar cooler. This study reveals that, an ANN approach can be used successfully for predicting the performance of pillar cooler.     

Evaluation of PCA and Gamma test techniques on ANN operation for weekly solid waste prediction

Artificial neural networks (ANNs) are suitable for modeling solid waste generation. In the present study, four training functions, including resilient backpropagation (RP), scale conjugate gradient (SCG), one step secant (OSS), and Levenberg–Marquardt (LM) algorithms have been used. The main goal of this research is to develop an ANN model with a simple structure and ample accuracy. In the first step, an appropriate ANN model with 13 input variables is developed using the afore-mentioned algorithms to optimize the network parameters for weekly solid waste prediction in Mashhad, Iran. Subsequently, principal component analysis (PCA) and Gamma test (GT) techniques are used to reduce the number of input variables. Finally, comparison amongst the operation of ANN, PCA-ANN, and GT-ANN models is made. Findings indicated that the PCA-ANN and GT-ANN models have more effective results than the ANN model. These two models decrease the number of input variables from 13 to 7 and 5, respectively.     

Modeling Flow and Sediment Transport in a River System Using an Artificial Neural Network

A river system is a network of intertwining channels and tributaries, where interacting flow and sediment transport processes are complex and floods may frequently occur. In water resources management of a complex system of rivers, it is important that instream discharges and sediments being carried by streamflow are correctly predicted. In this study, a model for predicting flow and sediment transport in a river system is developed by incorporating flow and sediment mass conservation equations into an artificial neural network (ANN), using actual river network to design the ANN architecture, and expanding hydrological applications of the ANN modeling technique to sediment yield predictions. The ANN river system model is applied to modeling daily discharges and annual sediment discharges in the Jingjiang reach of the Yangtze River and Dongting Lake, China. By the comparison of calculated and observed data, it is demonstrated that the ANN technique is a powerful tool for real-time prediction of flow and sediment transport in a complex network of rivers. A significant advantage of applying the ANN technique to model flow and sediment phenomena is the minimum data requirements for topographical and morphometric information without significant loss of model accuracy. The methodology and results presented show that it is possible to integrate fundamental physical principles into a data-driven modeling technique and to use a natural system for ANN construction. This approach may increase model performance and interpretability while at the same time making the model more understandable to the engineering community.     

A TECHNIQUE FOR MEASURING SCOUR AND FILL OF SALMON SPAWNING RIFFLES IN HEADWATER STREAMS1

A 30 x 0.9 cm piece of steel rod bent in the shape of an “L” and attached by hose clamps to a 15 x 3.2 cm section of plastic pipe sliding on an 86 x 1.9 cm steel shaft was tested for use in measuring scour and fill of salmon spawning riffles. Installed along channel cross-sections, results of tests at four sites on two hydraulically different streams showed the device to be useful in monitoring event specific scour and fill. Measurement error was estimated to be ± 10 mm.     

FLOOD STAGE FORECASTING WITH SUPPORT VECTOR MACHINES1

ABSTRACT: Machine learning techniques are finding more and more applications in the field of forecasting. A novel regression technique, called Support Vector Machine (SVM), based on the statistical learning theory is explored in this study. SVM is based on the principle of Structural Risk Minimization as opposed to the principle of Empirical Risk Minimization espoused by conventional regression techniques. The flood data at Dhaka, Bangladesh, are used in this study to demonstrate the forecasting capabilities of SVM. The result is compared with that of Artificial Neural Network (ANN) based model for one‐lead day to seven‐lead day forecasting. The improvements in maximum predicted water level errors by SVM over ANN for four‐lead day to seven‐lead day are 9.6 cm, 22.6 cm, 4.9 cm and 15.7 cm, respectively. The result shows that the prediction accuracy of SVM is at least as good as and in some cases (particularly at higher lead days) actually better than that of ANN, yet it offers advantages over many of the limitations of ANN, for example in arriving at ANN's optimal network architecture and choosing useful training set. Thus, SVM appears to be a very promising prediction tool.     

Accuracy improvement of various short-term load forecasting models by a novel and unified statistical data-filtering method

ABSTRACT

Time-series and machine-learning methods are being strongly exploited to improve the accuracy of short-term load forecasting (STLF) results. In developing countries, power consumption behaviors could be suddenly changed by different customers, e.g. industrial customers, residential customers, so the load-demand dataset is often unstable. Therefore, reliability assessment of the load-demand dataset is obviously necessary for STLF models. Hence, this paper proposes a novel and unified statistical data-filtering method with the best confidence interval to eliminate unexpected noises/outliers of the input dataset before performing various short-term load forecasting models. This proposed novel data-filtering method, so-called the data pre-processing method, is also compared to other existing data-filtering methods (e.g. Kalman filter, Density-Based Spatial Clustering of Applications with Noise, Wavelet transform, and Singular Spectrum Analysis). By using an SCADA system?-based database of a typical 22kV distribution network in Vietnam, NYISO database, and PJM-RTO database, case studies of short-term load forecasting have been conducted with a conventional ARIMA model, an ANN forecasting model, an LSTM-RNN model, an LSTM-CNN combined model, a deep auto-encoder (DAE) network, a Wavenet-based model, a Wavenet and LSTM hybrid model, and a Wavelet Neural Network (WNN) model, which are to validate the novel and unified statistical data-filtering method proposed. The achieved numerical results demonstrate which the accuracy of the aforementioned STLF models can be significantly improved due to the proposed statistical data-filtering method with the best confidence interval of the input load dataset. The proposed statistical data-filtering method can considerably outperform the existing data-filtering methods.     

Geomorphic and Ecological Consequences of Riprap Placement in River Systems

Riprap, consisting of large boulders or concrete blocks, is extensively used to stabilize streambanks and to inhibit lateral erosion of rivers, yet its effect on river morphology and its ecological consequences have been relatively little studied. In this paper, we review the available information, most of it culled from the “grey” literature. We use a simple one‐dimensional morphodynamic model as a conceptual tool to illustrate potential morphological effects of riprap placement in a gravel‐bed river, which include inhibition of local sediment supply to the channel and consequent channel bed scour and substrate coarsening, and downstream erosion. Riprap placement also tends to sever organic material input from the riparian zone, with loss of shade, wood input, and input of finer organic material. Available information on the consequences for the aquatic ecosystem mainly concerns effects on commercially and recreationally important fishes. The preponderance of studies report unfavorable effects on local numbers, but habitat niches created by openings in riprap can favorably affect invertebrates and some small fishes. There is a need for much more research on both morphological and ecosystem effects of riprap placement.     

Comparison of ANN,MVR, and SWR models for computing thermal efficiency of a solar still

In this paper, the viability of modeling the instantaneous thermal efficiency (η_ith) of a solar still was determined using meteorological and operational data with an artificial neural network (ANN), multivariate regression (MVR), and stepwise regression (SWR). This study used meteorological and operational variables to hypothesize the effect of solar still performance. In the ANN model, nine variables were used as input parameters: Julian day, ambient temperature, relative humidity, wind speed, solar radiation, feed water temperature, brine water temperature, total dissolved solids of feed water, and total dissolved solids of brine water. The η_ith was represented by one node in the output layer. The same parameters were used in the MVR and SWR models. The advantages and disadvantages were discussed to provide different points of view for the models. The performance evaluation criteria indicated that the ANN model was better than the MVR and SWR models. The mean coefficient of determination for the ANN model was about 13% and14% more accurate than those of the MVR and SWR models, respectively. In addition, the mean root mean square error values of 6.534% and 6.589% for the MVR and SWR models, respectively, were almost double that of the mean values for the ANN model. Although both MVR and SWR models provided similar results, those for the MVR were comparatively better. The relative errors of predicted η_ith values for the ANN model were mostly in the vicinity of ±10%. Consequently, the use of the ANN model is preferred, due to its high precision in predicting η_ith compared to the MVR and SWR models. This study should be extremely beneficial to those coping with the design of solar stills.     

STABLE PROFILES OF PLUNGE BASINS1

The characteristics of scour holes were discussed including the problems created by them in relation to the hydraulic structures associated with their formation. The philosophy on the design and use of deflector buckets together with the need for plunge basins to dissipate the energy of the high velocity jets were reviewed. Laboratory observations were made to study the erosion of beds of gravel caused by water jets projected from spillway buckets. Flip buckets with 15, 30, 45 and 60 degrees exit angles were utilized. One-quarter inch and %-inch nominal size bed materials were used in the investigation. The gravel was placed in a large comprehensive scour basin to observe their behavior when subjected to the water jets. Besides the formula derived for the maximum depth of scour, a set of dimensionless equations were developed to describe the three-dimensional configuration of scour holes. The dimensions of stable plunge basins could be obtained from these profiles.     

ARTIFICIAL NEURAL NETWORKS FOR SUBSURFACE DRAINAGE AND SUBIRRIGATION SYSTEMS IN ONTARIO,CANADA1

ABSTRACT: Artificial neural network (ANN) models were developed to simulate fluctuations in midspan water table depths (WTD) given rainfall, potential evapotranspiration, and irrigation inputs on a Brookston clay loam in Woodslee, Ontario, having a dual‐purpose subsurface drainage/subirrigation setup. Water table depths and meteorologic data collected at this site from 1992 to 1994 and from 1996 to 1997 were used to train the ANNs. The ANNs were then used for real‐time control and time series simulations. The lowest root mean squared errors (RMSE) for the various ANNs were 60.6 mm for real‐time control simulation, and 88.4 mm for time‐series simulation of water table depths. It was possible to simulate WTD for the different modes of water table management in one network by incorporating an indicator for switching from one to the other. The ANN simulations were quite good even though the training data sets had irregular measurement intervals. With fewer input parameters and small network structures, ANNs still provided accurate results and required little time for training and execution. ANNs are therefore easier and faster to develop and run than conventional models and can contribute to the proper management of subsurface drainage and subirrigation systems.