Fault propagation analysis by combining data-driven causal analysis and plant connectivity

2014 ◽  
Author(s):  
Rinat Landman ◽  
Jukka Kortela ◽  
Sirkka-Liisa Jamsa-Jounela
Keyword(s):  
Causal Analysis ◽  
Data Driven ◽  
Fault Propagation ◽  
Propagation Analysis ◽  
Combining Data

scholarly journals COMBINING DATA-DRIVEN AND NUMERICAL MODELLING APPROACHES TO STORM EROSION PREDICTION

2020 ◽  
pp. 38
Author(s):  
Joshua Simmons ◽  
Kristen Splinter
Keyword(s):  
Numerical Models ◽  
Individual Performance ◽  
Study Data ◽  
Data Driven ◽  
Erosion Prediction ◽  
Trade Offs ◽  
Combining Data ◽  
Erosion Models ◽  
Using Data ◽  
The Individual

Physics-based numerical models play an important role in the estimation of storm erosion, particularly at beaches for which there is little historical data. However, the increasing availability of pre-and post-storm data for multiple events and at a number of beaches around the world has opened the possibility of using data-driven approaches for erosion prediction. Both physics-based and purely data-driven approaches have inherent strengths and weaknesses in their ability to predict storm-induced erosion. It is vital that coastal managers and modelers are aware of these trade-offs as well as methods to maximise the value from each modelling approach in an increasingly data-rich environment. In this study, data from approximately 40 years of coastal monitoring at Narrabeen-Collaroy Beach (SE Australia)has been used to evaluate the individual performance of the numerical erosion models SBEACH and XBeach, and a data-driven modelling technique. The models are then combined using a simple weighting technique to provide a hybrid estimate of erosion.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/v53dZiO8Y60

Combining Data-Driven and User-Driven Evaluation Measures to Identify Interesting Rules

2009 ◽  
pp. 38-55 ◽  
Author(s):  
Solange Oliveira Rezende ◽  
Edson Augusto Melanda ◽  
Magaly Lika Fujimoto ◽  
Roberta Akemi Sinoara ◽  
Veronica Oliveira de Carvalho
Keyword(s):  
Data Mining ◽  
Data Driven ◽  
Objective Measures ◽  
Post Processing ◽  
Rule Mining ◽  
Evaluation Measures ◽  
Combining Data ◽  
Pros And Cons ◽  
The Subject

Association rule mining is a data mining task that is applied in several real problems. However, due to the huge number of association rules that can be generated, the knowledge post-processing phase becomes very complex and challenging. There are several evaluation measures that can be used in this phase to assist users in finding interesting rules. These measures, which can be divided into data-driven (or objective measures) and user-driven (or subjective measures), are first discussed and then analyzed for their pros and cons. A new methodology that combines them, aiming to use the advantages of each kind of measure and to make user’s participation easier, is presented. In this way, data-driven measures can be used to select some potentially interesting rules for the user’s evaluation. These rules and the knowledge obtained during the evaluation can be used to calculate user-driven measures, which are used to aid the user in identifying interesting rules. In order to identify interesting rules that use our methodology, an approach is described, as well as an exploratory environment and a case study to show that the proposed methodology is feasible. Interesting results were obtained. In the end of the chapter tendencies related to the subject are discussed.

Combining Data-Driven and Semantic Approaches for Text Mining

2011 ◽  
pp. 115-142 ◽  
Author(s):  
Stephan Bloehdorn ◽  
Sebastian Blohm ◽  
Philipp Cimiano ◽  
Eugenie Giesbrecht ◽  
Andreas Hotho ◽  
...  
Keyword(s):  
Text Mining ◽  
Data Driven ◽  
Combining Data

Remaining useful life estimation in aeronautics: Combining data-driven and Kalman filtering

2019 ◽  
Vol 184 ◽  
pp. 228-239 ◽  
Author(s):  
Marcia Baptista ◽  
Elsa M.P. Henriques ◽  
Ivo P. de Medeiros ◽  
Joao P. Malere ◽  
Cairo L. Nascimento ◽  
...  
Keyword(s):  
Kalman Filtering ◽  
Remaining Useful Life ◽  
Data Driven ◽  
Life Estimation ◽  
Combining Data ◽  
Useful Life
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