scholarly journals Improving predictions made by ANN model using data quality assessment: an application to local scour around bridge piers

2015 ◽  
Vol 17 (6) ◽  
pp. 977-989 ◽  
Author(s):  
Sung-Uk Choi ◽  
Byungwoong Choi ◽  
Seongwook Choi
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Laboratory Data ◽  
Local Scour ◽  
Bridge Piers ◽  
Scour Depth ◽  
Ann Model ◽  
Field Scale ◽  
Distance Method ◽  
Data Quality Assessment

The scour depth that develops around bridge piers is known to be related to flow intensity, particle size of bed sediment, and pier dimensions. Earlier approaches to this issue have mainly relied on empirical formulas. Even numerical simulations have not been so successful due to problems associated with interactions between water flow and streambed morphology. This necessitates the application of an artificial intelligence (AI)-type approach to understanding the effects of local scour around bridge piers. Although previous studies reported that AI-based models are better predictors, they do not predict field-scale local scour well. Motivated by this, the present study reports on the use of data quality assessment with an artificial neural network (ANN) model for predicting field-scale scour depth around bridge piers. Both univariate and multivariate methods were applied and the predicted results are compared. For the multivariate method, the Euclidean distance method and Mahalanobis distance method were used and the predicted results are compared. The ANN model was first trained and validated using laboratory data and the model was applied to data obtained in laboratory experiments. The model was then applied to field data. Quantitative descriptions are given on how much the data quality assessment improves predictions based on the use of the ANN model.

scholarly journals Prediction of maximum scour depth around piers with debris accumulation using EPR, MT, and GEP models

2016 ◽  
Vol 18 (5) ◽  
pp. 867-884 ◽  
Author(s):  
Mohammad Najafzadeh ◽  
Mohammad Rezaie Balf ◽  
Esmat Rashedi
Keyword(s):  
Polynomial Regression ◽  
Gene Expression Programming ◽  
Laboratory Data ◽  
Experimental Studies ◽  
Local Scour ◽  
Bridge Piers ◽  
Scour Depth ◽  
Effective Parameters ◽  
Pier Scour ◽  
Debris Accumulation

Pier scour phenomena in the presence of debris accumulation have attracted the attention of engineers to present a precise prediction of the local scour depth. Most experimental studies of pier scour depth with debris accumulation have been performed to find an accurate formula to predict the local scour depth. However, an empirical equation with appropriate capacity of validation is not available to evaluate the local scour depth. In this way, gene-expression programming (GEP), evolutionary polynomial regression (EPR), and model tree (MT) based formulations are used to develop to predict the scour depth around bridge piers with debris effects. Laboratory data sets utilized to perform models are collected from different literature. Effective parameters on the local scour depth include geometric characterizations of bridge piers and debris, physical properties of bed sediment, and approaching flow characteristics. The efficiency of the training stages for the GEP, MT, and EPR models are investigated. Performances of the testing results for these models are compared with the traditional approaches based on regression methods. The uncertainty prediction of the MT was quantified and compared with those of existing models. Also, sensitivity analysis was performed to assign effective parameters on the scour depth prediction.

Applying Domain Knowledge for Data Quality Assessment in Dermatology

2017 ◽  
pp. 147-156
Author(s):  
Nemanja Igić ◽  
Branko Terzić ◽  
Milan Matić ◽  
Vladimir Ivančević ◽  
Ivan Luković
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Domain Knowledge ◽  
Data Quality Assessment

scholarly journals Evaluation of a staff training programme to reimplement a comprehensive health assessment

2018 ◽  
Vol 7 (4) ◽  
pp. e000353 ◽  
Author(s):  
Luke A Turcotte ◽  
Jake Tran ◽  
Joshua Moralejo ◽  
Nancy Curtin-Telegdi ◽  
Leslie Eckel ◽  
...  
Keyword(s):  
Data Quality ◽  
Quality Assessment ◽  
Health Information ◽  
Health Assessment ◽  
Training Programme ◽  
Care Planning ◽  
Assessment Framework ◽  
High Quality ◽  
Data Quality Assessment ◽  
Comprehensive Health

BackgroundHealth information systems with applications in patient care planning and decision support depend on high-quality data. A postacute care hospital in Ontario, Canada, conducted data quality assessment and focus group interviews to guide the development of a cross-disciplinary training programme to reimplement the Resident Assessment Instrument–Minimum Data Set (RAI-MDS) 2.0 comprehensive health assessment into the hospital’s clinical workflows.MethodsA hospital-level data quality assessment framework based on time series comparisons against an aggregate of Ontario postacute care hospitals was used to identify areas of concern. Focus groups were used to evaluate assessment practices and the use of health information in care planning and clinical decision support. The data quality assessment and focus groups were repeated to evaluate the effectiveness of the training programme.ResultsInitial data quality assessment and focus group indicated that knowledge, practice and cultural barriers prevented both the collection and use of high-quality clinical data. Following the implementation of the training, there was an improvement in both data quality and the culture surrounding the RAI-MDS 2.0 assessment.ConclusionsIt is important for facilities to evaluate the quality of their health information to ensure that it is suitable for decision-making purposes. This study demonstrates the use of a data quality assessment framework that can be applied for quality improvement planning.

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