Initial assessment of bridge backwater using an artificial neural network approach

The assessment of backwater resulting from extra energy losses on flood flows caused by bridge constrictions is of vital interest in hydraulic engineering due to its importance in the design of waterways and management of flooding. Although many detailed methods for estimating bridge backwater have been developed, an initial estimate of the magnitude of bridge backwater using a practical model, such as the multiple linear regression (MLR) technique, has a crucial importance for rapid evaluation of flood damages upstream of the bridge structure. In the current study, first, two artificial neural network (ANN) models using the same amount of input data as that of an MLR approach were developed, and then the ability of these ANN models versus the MLR models was investigated for the initial assessment of bridge backwater, both models having been based on the comprehensive laboratory data of the Hydraulic Research Wallingford in UK. The comparison of the results by the MLR and the ANN approaches revealed that the ANN model gave better predictions than those of the MLR model when applied to these laboratory data. United States Geological Survey (USGS) field data were also used for the validation and comparison of these methods. The results showed that ANN approaches yielded more accurate results than those of the MLR models when applied to these field data including actual flood profiles through many bridges. © 2008 NRC Canada

Prediction of hydropower energy using ANN for the feasibility of hydropower plant installation to an existing irrigation dam

Recently, artificial neural networks (ANNs) have been used successfully for many engineering problems. This paper presents a practical way of predicting the hydropower energy potential using ANNs for the feasibility of adding a hydropower plant unit to an existing irrigation dam. Because the cost of energy has risen considerably in recent decades, addition of a suitable capacity hydropower plant (HPP) to the end of the pressure conduit of an existing irrigation dam may become economically feasible. First, a computer program to realistically calculate all local, frictional, and total head losses (THL) throughout any pressure conduit in detail is coded, whose end-product enables determination of the C coefficient of the highly significant model for total losses as: THL = C·Q2. Next, a computer program to determine the hydroelectric energies produced at monthly periods, the present worth (PW) of their monetary gains, and the annual average energy by a HPP is coded, which utilizes this simple but precise model for quantification of total energy losses from the inlet to the turbine. Inflows series, irrigation water requirements, evaporation rates, turbine running time ratios, and the C coefficient are the input data of this program. This model is applied to randomly chosen 10 irrigation dams in Turkey, and the selected input variables are gross head and reservoir capacity of the dams, recorded monthly inflows and irrigation releases for the prediction of hydropower energy. A single hidden-layered feed forward neural network using Levenberg-Marquardt algorithm is developed with a detailed analysis of model design of those factors affecting successful implementation of the model, which provides for a realistic prediction of the annual average hydroelectric energy from an irrigation dam in a quick-cut manner without the excessive operation studies needed conventionally. Estimation of the average annual energy with the help of this model should be useful for reconnaissance studies. © Springer Science+Business Media B.V. 2007

Prediction of groundwater levels from lake levels and climate data using ANN approach

There are many environmental concerns relating to the quality and quantity of surface and groundwater. It is very important to estimate the quantity of water by using readily available climate data for managing water resources of the natural environment. As a case study an artificial neural network (ANN) methodology is developed for estimating the groundwater levels (upper Floridan aquifer levels) as a function of monthly averaged precipitation, evaporation, and measured levels of Magnolia and Brooklyn Lakes in north-central Florida. Groundwater and surface water are highly interactive in the region due to the characteristics of the geological structure, which consists of a sandy surficial aquifer, and a highly transmissive limestone-confined aquifer known as the Floridan aquifer system (FAS), which are separated by a leaky clayey confining unit. In a lake groundwater system that is typical of many karst lakes in Florida, a large part of the groundwater outflow occurs by means of vertical leakage through the underlying confining unit to a deeper highly transmissive upper Floridan aquifer. This provides a direct hydraulic connection between the lakes and the aquifer, which creates fast and dynamic surface water/groundwater interaction. Relationships among take levels, groundwater levels, rainfall, and evapotranspiration were determined using ANN-based models and multiple-linear regression (MLR) and multiple-nonlinear regression (MNLR) models. All the models were fitted to the monthly data series and their performances were compared. ANN-based models performed better than MLR and MNLR models in predicting groundwater levels

Comparison of an ANN approach with 1-D and 2-D methods for estimating discharge capacity of straight compound channels

Most natural streams or rivers exhibit a compound or two-stage geometry consisting of a main channel and one or two floodplains. The discharge capacity of compound channels has an importance in flood defence schemes and in the economic development of floodplain areas for agriculture and parks. Therefore, the comprehensive stage-discharge model studies performed and different one or two-dimensional methods have been developed. In this study, the single-channel method (SCM), the divided-channel method (DCM), the coherence method (COHM), the exchange discharge method (EDM) and the Shiono-Knight method (SKM) have been compared with a multilayer perception neural network (MLP) with Levenberg-Marquardt algorithm. The results of the comparisons reveal that the artificial neural network (ANN) model gives slightly better statistical results than those of the COHM, EDM and these three give more accurate results than those of the SCM, DCM, and SKM. © 2009 Elsevier Ltd. All rights reserved

Three dimensional simulation of seawater intrusion in coastal aquifers: A case study in the Goksu Deltaic Plain

Unplanned exploitation of groundwater from coastal aquifers may cause salt water intrusion in coastal aquifers. Coastal areas are generally overpopulated with fertile agricultural lands and diversified irrigated farming activities. The objective of this study was to develop a model to control/prevent seawater intrusion into the coastal aquifer with a case study of the Silifke-Goksu Deltaic Plain. A computer program for the simulation of three-dimensional variable density groundwater flow, SEAWAT, is used to model the seawater intrusion mechanism of the Goksu Deltaic Plain along the Mediterranean coast of Turkey. The calibration analysis of the developed seawater intrusion model is performed using field measured data in the water-year of 2008 including static groundwater head, electrical conductivity, total dissolved solid (TDS), and chloride concentration values collected from 23 observation wells and the existing data which were compiled and reviewed. The main objectives for applying the seawater intrusion model to the Goksu Deltaic Plain were (1) to determine the hydraulic and hydrogeologic parameters of the aquifer, (2) to estimate the spatial variation of the salt concentration in the aquifer and (3) to investigate the impact of the increase and decrease in groundwater extractions. The simulation results show that the Goksu Deltaic Plain aquifer is especially sensitive to the increase in groundwater extraction. © 2012 Elsevier B.V.Firat University Scientific Research Projects Management UnitThis work was supported by the Cukurova University Scientific Research Project Unit, Grant No. MMF2006D6

Prediction of geometrical properties of perfect breaking waves on composite breakwaters

Breaking wave loads on coastal structures depend primarily on the type of wave breaking at the instant of impact. When a wave breaks on a vertical wall with an almost vertical front face called the "perfect breaking", the greatest impact forces are produced. The correct prediction of impact forces from perfect breaking of waves on seawalls and breakwaters is closely dependent on the accurate determination of their configurations at breaking. The present study is concerned with the determination of the geometrical properties of perfect breaking waves on composite-type breakwaters by employing artificial neural networks. Using a set of laboratory data, the breaker crest height, hb, breaker height, Hb, and water depth in front of the wall, dw, from perfect breaking of waves on composite breakwaters are predicted using the artificial neural network technique and the results are compared with those obtained from linear and multi-linear regression models. The comparisons of the predicted results from the present models with measured data show that the hb, Hb and dw values, which represent the geometry of waves breaking directly on composite breakwaters, can be predicted more accurately by artificial neural networks compared to linear and multi-linear regressions. © 2011 Elsevier Ltd

Bridge afflux analysis through arched bridge constrictions using artificial intelligence methods

Although many studies have been carried out for estimating the afflux through modern straight deck bridge constrictions, little attention has been given to medieval arched bridge constrictions. Hydraulic Research Wallingford in the UK (Brown, P.M., 1988. Afflux at arch bridges. Report SR 182. Wallingford, UK: HR Wallingford) recently published a major coverage of both experimental and field afflux data obtained from arched bridge constrictions. The report pointed out that the present day formulas developed for estimating the bridge afflux are inadequate to apply to ancient arched structures. Therefore, this study aimed at developing new afflux methods for arched bridge constrictions using multi-layer perceptrons (MLP) neural networks, radial basis function-based neural networks (RBNN), generalised regression neural networks (GRNN) and adaptive neuro-fuzzy inference system (ANFIS) model. Multiple linear and multiple nonlinear regression analyses were also used for comparison purposes. Mean square errors, mean absolute errors, mean absolute relative errors, average of individual ratios between predicted and actual values, and determination coefficients were used as comparison criteria for the evaluation of model performances. The test results showed that MLP, RBNN, GRNN, and ANFIS models gave reasonable accuracy when applied to both the field and experimental data collected by Hydraulic Research Wallingford. © 2009 Taylor & Francis

Application of ANN techniques for estimating backwater through bridge constrictions in Mississippi River basin

Bridge backwater data were collected for 92 different floods at 35 bridge sites in the Mississippi River basin in 1960s [Neely BL. Hydraulic performance of bridges, hydraulic efficiency of bridges-analysis of field data. Unpublished Report Conducted by US Geological Survey, June 30; 1966]. This major field data showed that the backwater computed both by the United States Geological Survey's method (USGS) and the United States Bureau of Public Roads' method (USBPR) averaged approximately 50% less than the measured backwater. Therefore, in the current work, a new bridge backwater formula based on the three different artificial neural network approaches (ANNs), namely FFBP (Feed-Forward Back Propagation), RBNN (Radial Basis Function-Based Neural Network), and GRNN (Generalized Regression Neural Networks) are proposed and compared with the methods mentioned above. The results showed that the FFBP produced slightly better estimations than those of the RBNN and these two was significantly superior to the GRNN, USGS and USBPR methods when applied to Neely's field data. © 2009 Elsevier Ltd. All rights reserved

Forecasting backwater through bridge constrictions in Mississippi River Basin

Hydraulic data collected in the 1960s during 92 distinct floods at 35 different bridge sites in the Mississippi River Basin revealed that the water surface profiles of these real-life cases were distinctly different from those observed in laboratory models of the comprehensive experimental studies of the 1950s by the U.S. Geological Survey (USGS) and by U.S. Bureau of Public Roads (USBPR). The laboratory-developedmethods of USGS andUSBPR yielded only about half of the field backwaterswhen applied to the comprehensive field data. In the current work, using the same field data and accepting a profile like that observed in the field, a new regression-based formula for estimating bridge backwater is proposed and compared with the methods of USGS and USBPR, which yields more accurate results than these two methods with the advantage of requiring a smaller load of arithmetic operations. Copyright © 2008 John Wiley & Sons, Ltd