scholarly journals Relationship Between Rainfall Duration and Sewer System Performance Measures Within the Context of Uncertainty

2021 ◽  
Author(s):  
Bartosz Szeląg ◽  
Adam Kiczko ◽  
Grzegorz Łagód ◽  
Francesco De Paola
Keyword(s):  
Performance Measures ◽  
Model Uncertainty ◽  
Roughness Coefficient ◽  
Engineering Practice ◽  
Model Parameters ◽  
Sewer System ◽  
Flood Volume ◽  
Intensity Duration Frequency ◽  
Glue Method ◽  
Generalized Likelihood Uncertainty Estimation

AbstractUrbanization and climate change have resulted in an increase in catchment runoff, often exceeding the designed capacity of sewer systems. The decision to modernize a sewer system should be based on appropriate criteria. In engineering practice, the above is commonly achieved using a hydrodynamic model of the catchment and the simulation of various rainfall events. The article presents a methodology to analyze the effect of rainfall characteristics parametrized with intensity-duration-frequency (IDF) curves in regard to performance measures of sewerage networks (flood volume per unit impervious surface and share of overfilled manholes in the sewerage network) accounting for the model uncertainty determined via the generalized likelihood uncertainty estimation (GLUE) method. An urban catchment was modeled with the Storm Water Management Model (SWMM). Analyses showed that the model uncertainty exerts a large impact on certain measures of sewage network operation. Therefore, these measures should be analyzed in similar studies. This is very important at the stage of decision making in regard to the modernization and sustainable development of catchments. It was found that among the model parameters, the Manning roughness coefficient of sewer channels yields a key impact on the specific flood volume, while the area of impervious surfaces yields the greatest impact on the share of overflowed manholes.

scholarly journals A conceptual grey rainfall-runoff model for simulation with uncertainty

2012 ◽  
Vol 15 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Stefano Alvisi ◽  
Anna Bernini ◽  
Marco Franchini
Keyword(s):  
Model Uncertainty ◽  
Model Parameters ◽  
Rainfall Runoff ◽  
Total Uncertainty ◽  
Computing Procedure ◽  
Glue Method ◽  
Grey Number ◽  
Rainfall Runoff Model ◽  
Simplified Procedure ◽  
Runoff Model

This paper presents an approach based on grey numbers to represent the total uncertainty of a conceptual rainfall-runoff model. Using this approach, once the grey numbers representing the model parameters have been properly defined, it is possible to obtain simulated discharges in the form of intervals (grey numbers) whose envelope defines a band which represents the total model uncertainty. The application to a real case showed that the construction of this band, according to a rigorous application of grey number theory, involves long computational times. However, these times can be significantly reduced using a simplified computing procedure with minimal approximations in the quantification of the simulated grey discharge. Relying on this simplified procedure, the conceptual rainfall-runoff grey model was then calibrated in order to respect a predefined level of model uncertainty, i.e. the band obtained from the envelope of simulated grey discharges had to include an assigned percentage of observed discharges and was at the same time as narrow as possible. Finally, the uncertainty bands were compared with the ones obtained using a well-established approach for characterising uncertainty, the Generalised Likelihood Uncertainty Estimation (GLUE) method. The results of the comparison showed that the proposed approach may represent a valid tool for characterising the total uncertainty of a rainfall-runoff model.

scholarly journals Assessment of Streamflow Simulation for a Tropical Forested Catchment Using Dynamic TOPMODEL—Dynamic fluxEs and ConnectIvity for Predictions of HydRology (DECIPHeR) Framework and Generalized Likelihood Uncertainty Estimation (GLUE)

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 317
Author(s):  
Fadhliani Umar ◽  
Zed Zulkafli ◽  
Badronnisa Yusuf ◽  
Siti Nurhidayu
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Peak Flow ◽  
Uncertainty Estimation ◽  
Model Parameters ◽  
Model Framework ◽  
Behavioral Threshold ◽  
Continuous Simulation ◽  
Generalized Likelihood Uncertainty Estimation ◽  
Streamflow Simulation

Rainfall runoff modeling has been a subject of interest for decades due to a need to understand a catchment system for management, for example regarding extreme event occurrences such as flooding. Tropical catchments are particularly prone to the hazards of extreme precipitation and the internal drivers of change in the system, such as deforestation and land use change. A model framework of dynamic TOPMODEL, DECIPHeR v1—considering the flexibility, modularity, and portability—and Generalized Likelihood Uncertainty Estimation (GLUE) method are both used in this study. They reveal model performance for the streamflow simulation in a tropical catchment, i.e., the Kelantan River in Malaysia, that is prone to flooding and experiences high rates of land use change. Thirty-two years’ continuous simulation at a daily time scale simulation along with uncertainty analysis resulted in a Nash Sutcliffe Efficiency (NSE) score of 0.42 from the highest ranked parameter set, while 25.35% of the measurement falls within the uncertainty boundary based on a behavioral threshold NSE 0.3. The performance and behavior of the model in the continuous simulation suggests a limited ability of the model to represent the system, particularly along the low flow regime. In contrast, the simulation of eight peak flow events achieves moderate to good fit, with the four peak flow events simulation returning an NSE > 0.5. Nonetheless, the parameter scatter plot from both the continuous simulation and analyses of peak flow events indicate unidentifiability of all model parameters. This may be attributable to the catchment modeling scale. The results demand further investigation regarding the heterogeneity of parameters and calibration at multiple scales.

scholarly journals Uncertainty Assessment of Urban Hydrological Modelling from a Multiple Objective Perspective

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1393 ◽  
Author(s):  
Bo Pang ◽  
Shulan Shi ◽  
Gang Zhao ◽  
Rong Shi ◽  
Dingzhi Peng ◽  
...  
Keyword(s):  
Flood Management ◽  
Uncertainty Assessment ◽  
Multiple Objective ◽  
Hydrological Models ◽  
Validation Period ◽  
Urban Flood ◽  
Calibration Period ◽  
Storm Water Management Model ◽  
Glue Method ◽  
Generalized Likelihood Uncertainty Estimation

The uncertainty assessment of urban hydrological models is important for understanding the reliability of the simulated results. To satisfy the demand for urban flood management, we assessed the uncertainty of urban hydrological models from a multiple-objective perspective. A multiple-criteria decision analysis method, namely, the Generalized Likelihood Uncertainty Estimation-Technique for Order Preference by Similarity to Ideal Solution (GLUE-TOPSIS) was proposed, wherein TOPSIS was adopted to measure the likelihood within the GLUE framework. Four criteria describing different urban stormwater characteristics were combined to test the acceptability of the parameter sets. The TOPSIS was used to calculate the aggregate employed in the calculation of the aggregate likelihood value. The proposed method was implemented in the Storm Water Management Model (SWMM), which was applied to the Dahongmen catchment in Beijing, China. The SWMM model was calibrated and validated based on the three and two flood events respectively downstream of the Dahongmen catchment. The results showed that the GLUE-TOPSIS provided a more precise uncertainty boundary compared with the single-objective GLUE method. The band widths were reduced by 7.30 m3/s in the calibration period, and by 7.56 m3/s in the validation period. The coverages increased by 20.3% in the calibration period, and by 3.2% in the validation period. The median estimates improved, with an increase of the Nash–Sutcliffe efficiency coefficients by 1.6% in the calibration period, and by 10.0% in the validation period. We conclude that the proposed GLUE-TOPSIS is a valid approach to assess the uncertainty of urban hydrological model from a multiple objective perspective, thereby improving the reliability of model results in urban catchment.

Program Assessment: Equivalency and Credentials for the Global Marketplace

2000 ◽  
Author(s):  
Winfred M. Phillips
Keyword(s):  
Quality Assurance ◽  
Space Shuttle ◽  
Multinational Companies ◽  
Program Assessment ◽  
Engineering Practice ◽  
Sewer System ◽  
Engineering Structures ◽  
Global Marketplace ◽  
At Home

Abstract Great engineering achievements, from the Aqueducts of Rome and Hausman’s Sewer System for Paris to the Boeing 757 and the Space Shuttle, have always benefitted from international influence and content. The reliability of engineering structures and systems has always engendered the confidence of international users. U.S. citizens drive European automobiles with confidence and Europeans drive across U.S. bridges without pause. Today, international content is extensive, often formalized and regulated and a permanent part of tomorrow’s engineering. Engineers both participate in their profession worldwide and evaluate and accommodate international content at home. Multinational companies demand multinational engineering practice. “Credentials without borders” is desired. Accreditation is key to quality assurance.

scholarly journals Spatial Pattern Oriented Multi-Criteria Sensitivity Analysis of a Distributed Hydrologic Model

2018 ◽  
Author(s):  
Mehmet Cüneyd Demirel ◽  
Julian Koch ◽  
Gorka Mendiguren ◽  
Simon Stisen
Keyword(s):  
Sensitivity Analysis ◽  
Spatial Variability ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Performance Measures ◽  
Sampling Strategy ◽  
Behavioral Model ◽  
Hydrologic Model ◽  
Actual Evapotranspiration ◽  
Model Parameters

Hydrologic models are conventionally constrained and evaluated using point measurements of streamflow, which represents an aggregated catchment measure. As a consequence of this single objective focus, model parametrization and model parameter sensitivity are typically not reflecting other aspects of catchment behavior. Specifically for distributed models, the spatial pattern aspect is often overlooked. Our paper examines the utility of multiple performance measures in a spatial sensitivity analysis framework to determine the key parameters governing the spatial variability of predicted actual evapotranspiration (AET). Latin hypercube one-at-a-time (LHS-OAT) sampling strategy with multiple initial parameter sets was applied using the mesoscale hydrologic model (mHM) and a total of 17 model parameters were identified as sensitive. The results indicate different parameter sensitivities for different performance measures focusing on temporal hydrograph dynamics and spatial variability of actual evapotranspiration. While spatial patterns were found to be sensitive to vegetation parameters, streamflow dynamics were sensitive to pedo-transfer function (PTF) parameters. Above all, our results show that behavioral model definition based only on streamflow metrics in the generalized likelihood uncertainty estimation (GLUE) type methods require reformulation by incorporating spatial patterns into the definition of threshold values to reveal robust hydrologic behavior in the analysis.

scholarly journals Automated calibration of a two-dimensional overland flow model by estimating Manning's roughness coefficient using genetic algorithm

2017 ◽  
Vol 20 (2) ◽  
pp. 440-456
Author(s):  
J. Drisya ◽  
D. Sathish Kumar
Keyword(s):  
Genetic Algorithm ◽  
Flow Model ◽  
Overland Flow ◽  
Fitness Function ◽  
Roughness Coefficient ◽  
Model Parameters ◽  
Automatic Data ◽  
Automated Calibration ◽  
Manning’S Roughness Coefficient ◽  
Overland Flow Model

Abstract Calibration is an important phase in the hydrological modelling process. In this study, an automated calibration framework is developed for estimating Manning's roughness coefficient. The calibration process is formulated as an optimization problem and solved using a genetic algorithm (GA). A heuristic search procedure using GA is developed by including runoff simulation process and evaluating the fitness function by comparing the experimental results. The model is calibrated and validated using datasets of Watershed Experimentation System. A loosely coupled architecture is followed with an interface program to enable automatic data transfer between overland flow model and GA. Single objective GA optimization with minimizing percentage bias, root mean square error and maximizing Nash–Sutcliffe efficiency is integrated with the model scheme. Trade-offs are observed between the different objectives and no single set of the parameter is able to optimize all objectives simultaneously. Hence, multi-objective GA using pooled and balanced aggregated function statistic are used along with the model. The results indicate that the solutions on the Pareto-front are equally good with respect to one objective, but may not be suitable regarding other objectives. The present technique can be applied to calibrate the hydrological model parameters.

Performance Analysis of Cloud Computing Centers for Bulk Services

2012 ◽  
Vol 2 (4) ◽  
pp. 53-65 ◽  
Author(s):  
Veena Goswami ◽  
Sudhansu Shekhar Patra ◽  
G. B. Mund
Keyword(s):  
Cloud Computing ◽  
Performance Measures ◽  
Service Providers ◽  
Blocking Probability ◽  
Cloud Service ◽  
Model Parameters ◽  
Queuing Model ◽  
Service Oriented ◽  
Computing Centers ◽  
Access To Data

Cloud is a service oriented platform where all kinds of virtual resources are treated as services to users. Several cloud service providers have offered different capabilities for a variety of market segments over the past few years. The most important aspects of cloud computing are resource scheduling, performance measures, and user requests. Sluggish access to data, applications, and web pages spoils employees and customers alike, as well as cause application crashes and data losses. In this paper, the authors propose an analytical queuing model for performance evaluation of cloud server farms for processing bulk data. Some important performance measures such as mean number of tasks in the queue, blocking probability, and probability of immediate service, and waiting-time distribution in the system have also been discussed. Finally, a variety of numerical results showing the effect of model parameters on key performance measures are presented.

Estimating Model Uncertainty using Confidence Interval Networks: Applications to Robust Control

2005 ◽  
Vol 128 (3) ◽  
pp. 626-635 ◽  
Author(s):  
Gregory D. Buckner ◽  
Heeju Choi ◽  
Nathan S. Gibson
Keyword(s):  
Robust Control ◽  
Confidence Interval ◽  
Soft Computing ◽  
Model Uncertainty ◽  
Intelligent System ◽  
Magnetic Bearing ◽  
Active Magnetic Bearing ◽  
Control Synthesis ◽  
Model Parameters ◽  
Computing Approach

Robust control techniques require a dynamic model of the plant and bounds on model uncertainty to formulate control laws with guaranteed stability. Although techniques for modeling dynamic systems and estimating model parameters are well established, very few procedures exist for estimating uncertainty bounds. In the case of H∞ control synthesis, a conservative weighting function for model uncertainty is usually chosen to ensure closed-loop stability over the entire operating space. The primary drawback of this conservative, “hard computing” approach is reduced performance. This paper demonstrates a novel “soft computing” approach to estimate bounds of model uncertainty resulting from parameter variations, unmodeled dynamics, and nondeterministic processes in dynamic plants. This approach uses confidence interval networks (CINs), radial basis function networks trained using asymmetric bilinear error cost functions, to estimate confidence intervals associated with nominal models for robust control synthesis. This research couples the “hard computing” features of H∞ control with the “soft computing” characteristics of intelligent system identification, and realizes the combined advantages of both. Simulations and experimental demonstrations conducted on an active magnetic bearing test rig confirm these capabilities.

scholarly journals Aerosol and physical atmosphere model parameters are both important sources of uncertainty in aerosol ERF

2018 ◽  
Vol 18 (13) ◽  
pp. 9975-10006 ◽  
Author(s):  
Leighton A. Regayre ◽  
Jill S. Johnson ◽  
Masaru Yoshioka ◽  
Kirsty J. Pringle ◽  
David M. H. Sexton ◽  
...  
Keyword(s):  
Model Uncertainty ◽  
Radiative Forcing ◽  
Computational Cost ◽  
Short Wave ◽  
Sensitivity Analyses ◽  
Model Parameters ◽  
Large Set ◽  
Individual Parameter ◽  
Cloud Water Content ◽  
Atmosphere Model

Abstract. Changes in aerosols cause a change in net top-of-the-atmosphere (ToA) short-wave and long-wave radiative fluxes; rapid adjustments in clouds, water vapour and temperature; and an effective radiative forcing (ERF) of the planetary energy budget. The diverse sources of model uncertainty and the computational cost of running climate models make it difficult to isolate the main causes of aerosol ERF uncertainty and to understand how observations can be used to constrain it. We explore the aerosol ERF uncertainty by using fast model emulators to generate a very large set of aerosol–climate model variants that span the model uncertainty due to 27 parameters related to atmospheric and aerosol processes. Sensitivity analyses shows that the uncertainty in the ToA flux is dominated (around 80 %) by uncertainties in the physical atmosphere model, particularly parameters that affect cloud reflectivity. However, uncertainty in the change in ToA flux caused by aerosol emissions over the industrial period (the aerosol ERF) is controlled by a combination of uncertainties in aerosol (around 60 %) and physical atmosphere (around 40 %) parameters. Four atmospheric and aerosol parameters account for around 80 % of the uncertainty in short-wave ToA flux (mostly parameters that directly scale cloud reflectivity, cloud water content or cloud droplet concentrations), and these parameters also account for around 60 % of the aerosol ERF uncertainty. The common causes of uncertainty mean that constraining the modelled planetary brightness to tightly match satellite observations changes the lower 95 % credible aerosol ERF value from −2.65 to −2.37 W m−2. This suggests the strongest forcings (below around −2.4 W m−2) are inconsistent with observations. These results show that, regardless of the fact that the ToA flux is 2 orders of magnitude larger than the aerosol ERF, the observed flux can constrain the uncertainty in ERF because their values are connected by constrainable process parameters. The key to reducing the aerosol ERF uncertainty further will be to identify observations that can additionally constrain individual parameter ranges and/or combined parameter effects, which can be achieved through sensitivity analysis of perturbed parameter ensembles.

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