Distinguishing Between Data Uncertainty and Natural Variability in Virtual Geotechnical Databases

2013 ◽  
Author(s):  
J. David Rogers ◽  
Jae-Won Chung
Keyword(s):  
Natural Variability ◽  
Data Uncertainty ◽  
Geotechnical Databases

Monetary Policy and Data Uncertainty

2005 ◽  
Author(s):  
Jarkko P. P. Jääskelä ◽  
Anthony Yates
Keyword(s):  
Monetary Policy ◽  
Data Uncertainty

scholarly journals Effect of environmental history on the habitat-forming kelp Macrocystis pyrifera responses to ocean acidification and warming: a physiological and molecular approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pamela A. Fernández ◽  
Jorge M. Navarro ◽  
Carolina Camus ◽  
Rodrigo Torres ◽  
Alejandro H. Buschmann
Keyword(s):  
Climate Change ◽  
Ocean Acidification ◽  
Environmental History ◽  
Coastal Upwelling ◽  
Macrocystis Pyrifera ◽  
Natural Variability ◽  
Kelp Forests ◽  
Species Response ◽  
Enhanced Expression ◽  
Mesocosm Experiments

AbstractThe capacity of marine organisms to adapt and/or acclimate to climate change might differ among distinct populations, depending on their local environmental history and phenotypic plasticity. Kelp forests create some of the most productive habitats in the world, but globally, many populations have been negatively impacted by multiple anthropogenic stressors. Here, we compare the physiological and molecular responses to ocean acidification (OA) and warming (OW) of two populations of the giant kelp Macrocystis pyrifera from distinct upwelling conditions (weak vs strong). Using laboratory mesocosm experiments, we found that juvenile Macrocystis sporophyte responses to OW and OA did not differ among populations: elevated temperature reduced growth while OA had no effect on growth and photosynthesis. However, we observed higher growth rates and NO3− assimilation, and enhanced expression of metabolic-genes involved in the NO3− and CO2 assimilation in individuals from the strong upwelling site. Our results suggest that despite no inter-population differences in response to OA and OW, intrinsic differences among populations might be related to their natural variability in CO2, NO3− and seawater temperatures driven by coastal upwelling. Further work including additional populations and fluctuating climate change conditions rather than static values are needed to precisely determine how natural variability in environmental conditions might influence a species’ response to climate change.

Assessment of Machine Learning-Based Audiovisual Quality Predictors

2021 ◽  
Vol 17 (2) ◽  
pp. 1-22
Author(s):  
Mythili K. ◽  
Manish Narwaria
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Quality Assessment ◽  
Computational Models ◽  
Model Development ◽  
Multimedia Communication ◽  
Current Approach ◽  
Learning Ability ◽  
Data Uncertainty ◽  
Attractive Alternative

Quality assessment of audiovisual (AV) signals is important from the perspective of system design, optimization, and management of a modern multimedia communication system. However, automatic prediction of AV quality via the use of computational models remains challenging. In this context, machine learning (ML) appears to be an attractive alternative to the traditional approaches. This is especially when such assessment needs to be made in no-reference (i.e., the original signal is unavailable) fashion. While development of ML-based quality predictors is desirable, we argue that proper assessment and validation of such predictors is also crucial before they can be deployed in practice. To this end, we raise some fundamental questions about the current approach of ML-based model development for AV quality assessment and signal processing for multimedia communication in general. We also identify specific limitations associated with the current validation strategy which have implications on analysis and comparison of ML-based quality predictors. These include a lack of consideration of: (a) data uncertainty, (b) domain knowledge, (c) explicit learning ability of the trained model, and (d) interpretability of the resultant model. Therefore, the primary goal of this article is to shed some light into mentioned factors. Our analysis and proposed recommendations are of particular importance in the light of significant interests in ML methods for multimedia signal processing (specifically in cases where human-labeled data is used), and a lack of discussion of mentioned issues in existing literature.

Framework of Reliability-Based Stochastic Mobility Map for Next Generation NATO Reference Mobility Model

2019 ◽  
Vol 14 (2) ◽  
Author(s):  
K. K. Choi ◽  
Paramsothy Jayakumar ◽  
Matthew Funk ◽  
Nicholas Gaul ◽  
Tamer M. Wasfy
Keyword(s):  
Normal Distribution ◽  
Soil Property ◽  
Region Of Interest ◽  
Mobility Model ◽  
Maximum Speed ◽  
Next Generation ◽  
Input Parameters ◽  
Terrain Elevation ◽  
Dynamic Kriging ◽  
Geotechnical Databases

A framework for generation of reliability-based stochastic off-road mobility maps is developed to support the next generation NATO reference mobility model (NG-NRMM) using full stochastic knowledge of terrain properties and modern complex terramechanics modeling and simulation capabilities. The framework is for carrying out uncertainty quantification (UQ) and reliability assessment for Speed Made Good and GO/NOGO decisions for the ground vehicle based on the input variability models of the terrain elevation and soil property parameters. To generate the distribution of the slope at given point, realizations of the elevation raster are generated using the normal distribution. For the soil property parameters, such as cohesion, friction, and bulk density, the min and max values obtained from geotechnical databases for each of the soil types are used to generate the normal distribution with a 99% confidence value range. In the framework, the ranges of terramechanics input parameters that will cover the regions of interest are first identified. Within these ranges of input parameters, a dynamic kriging (DKG) surrogate model is obtained for the maximum speed of the nevada automotive test center (NATC) wheeled vehicle platform complex terramechanics model. Finally, inverse reliability analysis using Monte Carlo simulation is carried out to generate the reliability-based stochastic mobility maps for Speed Made Good and GO/NOGO decisions. It is found that the deterministic map of the region of interest has probability of only 25% to achieve the indicated speed.

scholarly journals “Grey swan” storm surges pose a greater coastal flood hazard than climate change

2021 ◽  
Author(s):  
Kevin Horsburgh ◽  
Ivan D. Haigh ◽  
Jane Williams ◽  
Michela De Dominicis ◽  
Judith Wolf ◽  
...  
Keyword(s):  
Sea Level ◽  
Tide Gauge ◽  
Storm Surges ◽  
Natural Variability ◽  
Major Incident ◽  
Dynamical Response ◽  
Mean Sea Level ◽  
Coastal Flood ◽  
Extreme Storm ◽  
Observational Record

AbstractIn this paper, we show that over the next few decades, the natural variability of mid-latitude storm systems is likely to be a more important driver of coastal extreme sea levels than either mean sea level rise or climatically induced changes to storminess. Due to their episodic nature, the variability of local sea level response, and our short observational record, understanding the natural variability of storm surges is at least as important as understanding projected long-term mean sea level changes due to global warming. Using the December 2013 North Atlantic Storm Xaver as a baseline, we used a meteorological forecast modification tool to create “grey swan” events, whilst maintaining key physical properties of the storm system. Here we define “grey swan” to mean an event which is expected on the grounds of natural variability but is not within the observational record. For each of these synthesised storm events, we simulated storm tides and waves in the North Sea using hydrodynamic models that are routinely used in operational forecasting systems. The grey swan storms produced storm surges that were consistently higher than those experienced during the December 2013 event at all analysed tide gauge locations along the UK east coast. The additional storm surge elevations obtained in our simulations are comparable to high-end projected mean sea level rises for the year 2100 for the European coastline. Our results indicate strongly that mid-latitude storms, capable of generating more extreme storm surges and waves than ever observed, are likely due to natural variability. We confirmed previous observations that more extreme storm surges in semi-enclosed basins can be caused by slowing down the speed of movement of the storm, and we provide a novel explanation in terms of slower storm propagation allowing the dynamical response to approach equilibrium. We did not find any significant changes to maximum wave heights at the coast, with changes largely confined to deeper water. Many other regions of the world experience storm surges driven by mid-latitude weather systems. Our approach could therefore be adopted more widely to identify physically plausible, low probability, potentially catastrophic coastal flood events and to assist with major incident planning.

An Adaptive Response Surface Method Using Bayesian Metric and Model Bias Correction Function

2014 ◽  
Vol 136 (3) ◽  
Author(s):  
Lei Shi ◽  
Ren-Jye Yang ◽  
Ping Zhu
Keyword(s):  
Response Surface ◽  
Bias Correction ◽  
Adaptive Response ◽  
Large Scale ◽  
Correction Method ◽  
Data Uncertainty ◽  
Correction Function ◽  
Surface Model ◽  
Model Bias ◽  
Adaptive Response Surface Method

The Bayesian metric was used to select the best available response surface in the literature. One of the major drawbacks of this method is the lack of a rigorous method to quantify data uncertainty, which is required as an input. In addition, the accuracy of any response surface is inherently unpredictable. This paper employs the Gaussian process based model bias correction method to quantify the data uncertainty and subsequently improve the accuracy of a response surface model. An adaptive response surface updating algorithm is then proposed for a large-scale problem to select the best response surface. The proposed methodology is demonstrated by a mathematical example and then applied to a vehicle design problem.

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