PREDICTION OF LOCAL SCOUR AROUND BRIDGE PIERS USING ARTIFICIAL NEURAL NETWORKS1

This paper describes a method for predicting local scour around bridge piers using an artificial neural network (ANN). Methods for selecting input variables, calibrations of network control parameters, learning process, and verifications are also discussed. The ANN model trained by laboratory data is applied to both laboratory and field measurements. The results illustrate that the ANN model can be used to predict local scour in the laboratories and in the field better than other empirical relationships that are currently in use. A parameter study is also carried out to investigate the importance of each input variable as reflected in data.     

Tracking remediation and redevelopment trends of brownfield clean‐up programmes: the Cook County experience

This study provides evidence on the clean‐up and development of properties in Cook County that have completed the two Illinois brownfield voluntary clean‐up programmes (VCPs) since 1989. Real estate and environmental databases are merged to allow analysis of development trends for a sample of several hundred entering sites, which are followed through the programme and into the marketplace. Results show that over half of the sites entering the programme received a closure letter, and that a quarter used a residential standard when remediating property. One‐third used caps or other engineering controls, and about 20% have received financing since obtaining the closure letter. These promising outcomes demonstrate the return of these properties to productive use.     

IMPLEMENTATION OF BIORETENTION SYSTEMS: A WISCONSIN CASE STUDY1

ABSTRACT: The implementation of various bioretention systems was analyzed, including rain gardens, vegetated swales, trenches, and infiltration basins in the St. Francis subdivision, Cross Plains, Wisconsin. Through the examination of archival data and interviews with key participants, it was found that although regulatory and political pressures encouraged the inclusion of bioretention, current standards for storm water management prevailed. The developers had to meet both existing requirements and anticipated rules requiring infiltration. As a result, bioretention systems simply supplemented, rather than replaced, traditional storm water practices. The confusion surrounding dual standards contributed to substantial delays in the negotiations among relevant stakeholders in the watershed. It is concluded that the St. Francis subdivision serves as both a cautionary tale and a bioretention success story. As a caution, this situation demonstrates the need for careful review and refinement of existing storm water ordinances to incorporate water quality improvement technologies, such as bioretention. The demonstrated success of the St. Francis development, however, is that it became a positive prototype for best management storm water practices elsewhere in the region. In addition, the water quality monitoring data from the site has contributed to development of a new county ordinance, the first in Wisconsin to address both quantity and quality of storm water runoff.     

MULTIVARIATE ANALYSIS OF WATER QUALITY IN THE RICHIBUCTO DRAINAGE BASIN (NEW BRUNSWICK,CANADA)1

ABSTRACT: Specific conductivity, pH, dissolved oxygen, carbon, phosphorous, and nitrogen species were measured at 36 stations in the Richibucto River drainage basin, including the estuary, in New Brunswick, Canada, over the six‐year period 1996 through 2001. Each station was sampled between 1 and 26 times (mean = 7.5, standard deviation = 6.0) during the ice free seasons without regard to tide. There was significant variance among stations in most parameters. Principal component analysis (PCA) was used to identify the processes explaining the observed variance in water quality. Because of the high variability in specific conductance, stations were first grouped in a freshwater subset and an estuarine (brackish water) subset. For freshwater stations, most of the variance in water quality was explained by pH and total organic carbon, as well as high nutrient concentrations. These high nutrient concentrations, along with water salinity, which varies with flow and tides, are also important in determining water quality variability in brackish water. It is recommended that water quality parameters that were found to explain most of the variance by PCA be monitored more closely, as they are key elements in understanding the variability in water quality in the Richibucto drainage basin. Cluster analyses showed that high phosphorous and nitrate concentrations were mostly found in areas of peat runoff, tributaries receiving treated municipal effluent, and lentic zones upstream of culverts. Peat runoff was also shown to be acidic, whether it is runoff from a harvested area or a natural bog.     

WATER QUALITY MODELING FOR THE FEITSUI RESERVOIR IN NORTHERN TAIWAN1

ABSTRACT: Field data collected from the Feitsui Reservoir in Taiwan indicate that the water is at a stage between mesotrophic and eutrophic. Recent measurements such as total phosphorus, chlorophyll a and Secchi depth levels suggest that the water quality has been declining. A two‐dimensional, laterally averaged, finite difference hydrodynamic and water quality model was used to simulate the water quality in the Feitsui Reservoir in an effort to determine sound water quality management strategies. The model was calibrated and verified using data collected in 1996 and 1997. The calibrated model was used to simulate algal biomass (in terms of chlorophyll a) levels under various wasteload reduction scenarios. Model results show that 50 percent reduction of the total phosphorus load will improve the existing water quality, shifting the trophic status from eutrophic/mesotrophic to oligotrophic. The modeling effort has yielded valuable information that can be used by decision makers for the evaluation of different management strategies of reducing watershed nutrient loads.     

TWO PRACTICAL APPROACHES FOR DETERMINING THE CONFIDENCE INTERVAL OF RAINFALL DEPTH PERCENTILES1

ABSTRACT: A simple simulation type approach and a statistical method are proposed for determining the confidence interval of the T‐year frequency rainfall percentiles (or precipitation extremes) for generalized extreme value (GEV) distributions. The former method is based on the Monte Carlo testing procedure. To generate realizations, the covariance structure of the three parameters of GEV is investigated using an observed information matrix of the likelihood function. For distributions with realistic parameters, the correlation between the location and the scale parameters is practically constant when the shape parameter varies around values close to its optimal value. The latter method is based on likelihood ratio statistics. In the case where the joint confidence surface for shape parameters and estimates is plotted with lines of best estimates, the region where the estimated best percentile value can be chosen as a possible estimate is part of the joint confidence surface. The projection of this bounded region on axis of percentile is defined as the effective confidence interval in this research. The use of this effective interval as the confidence interval of the percentile of T‐year frequency rainfall is particularly recommended because it is stable for T and it reflects variations in all three parameters of GEV appropriately.     

GENETIC PROGRAMMING: A NEW PARADIGM IN RAINFALL RUNOFF MODELING1

ABSTRACT: Genetic Programming (GP) is a domain‐independent evolutionary programming technique that evolves computer programs to solve, or approximately solve, problems. To verify GP's capability, a simple example with known relation in the area of symbolic regression, is considered first. GP is then utilized as a flow forecasting tool. A catchment in Singapore with a drainage area of about 6 km² is considered in this study. Six storms of different intensities and durations are used to train GP and then verify the trained GP. Analysis of the GP induced rainfall and runoff relationship shows that the cause and effect relationship between rainfall and runoff is consistent with the hydrologic process. The result shows that the runoff prediction accuracy of symbolic regression based models, measured in terms of root mean square error and correlation coefficient, is reasonably high. Thus, GP induced rainfall runoff relationships can be a viable alternative to traditional rainfall runoff models.     

AN INTEGRATIVE APPROACH TOWARDS UNDERSTANDING ECOLOGICAL RESPONSES TO DAM REMOVAL: THE MANATAWNY CREEK STUDY1

ABSTRACT: Dam removal has been proposed as an effective method of river restoration, but few integrative studies have examined ecological responses to the removal of dams. In 1999, we initiated an interdisciplinary study to determine ecological responses to the removal of a 2 m high dam on lower Manatawny Creek in southeastern Pennsylvania. We used an integrative monitoring program to assess the physical, chemical, and biological responses to dam removal. Following removal in 2000, increased sediment transport has led to major changes in channel form in the former impoundment and downstream reaches. Water quality did not change markedly following removal, probably because of the impoundment's short hydraulic residence time (less than two hours at base flow) and infrequent temperature stratification. When the impoundment was converted to a free flowing reach, the composition of the benthic macroinvertebrate and fish assemblages in this portion of Manatawny Creek shifted dramatically from lentic to lotic taxa. Some fish species inhabiting the free flowing reach downstream from the dam were negatively affected by large scale sediment transport and habitat alteration following dam removal, but this appears to be a short term response. Based on our observations and experiences in this study, we provide a list of issues to evaluate when considering future dam removals.     

Neural Network Input Selection for Hydrological Forecasting Affected by Snowmelt1

Abstract: Snowmelt largely affects runoff in watersheds in Nordic countries. Neural networks (NN) are particularly attractive for streamflow forecasting whereas they rely at least on daily streamflow and precipitation observations. The selection of pertinent model inputs is a major concern in NNs implementation. This study investigates performance of auxiliary NN inputs that allow short‐term streamflow forecasting without resorting to a deterministic snowmelt routine. A case study is presented for the Rivière des Anglais watershed (700 km²) located in Southern Québec, Canada. Streamflow (Q), precipitations (rain R and snow S, or total P), temperature (T) and snow lying (A) observations, combined with climatic and snowmelt proxy data, including snowmelt flow (Q_SM) obtained from a deterministic model, were tested. NN implemented with antecedent Q and R produced the largest gains in performance. Introducing increments of A and T to the NNs further improved the performance. Long‐term averages, seasonal data, and Q_SM failed to improve the networks.