Slope Accuracy Associated with DEM Data Error and Terrain Representation Error

2010 ◽  
Vol 11 (1) ◽  
pp. 43-49
Author(s):  
Dunxin JIA ◽  
Guoan TANG ◽  
Chun WANG ◽  
Yini JIA
Keyword(s):  
Representation Error ◽  
Data Error ◽  
Terrain Representation

Correction: “Influence of Selection Bias on the Test Decision – A Simulation Study”

2014 ◽  
Vol 53 (05) ◽  
pp. 343-343
Keyword(s):  
Selection Bias ◽  
Simulation Study ◽  
Error Rate ◽  
Type I Error ◽  
Block Size ◽  
Error Rates ◽  
Type I ◽  
Type I Error Rate ◽  
Representation Error ◽  
Numeric Representation

We have to report marginal changes in the empirical type I error rates for the cut-offs 2/3 and 4/7 of Table 4, Table 5 and Table 6 of the paper “Influence of Selection Bias on the Test Decision – A Simulation Study” by M. Tamm, E. Cramer, L. N. Kennes, N. Heussen (Methods Inf Med 2012; 51: 138 –143). In a small number of cases the kind of representation of numeric values in SAS has resulted in wrong categorization due to a numeric representation error of differences. We corrected the simulation by using the round function of SAS in the calculation process with the same seeds as before. For Table 4 the value for the cut-off 2/3 changes from 0.180323 to 0.153494. For Table 5 the value for the cut-off 4/7 changes from 0.144729 to 0.139626 and the value for the cut-off 2/3 changes from 0.114885 to 0.101773. For Table 6 the value for the cut-off 4/7 changes from 0.125528 to 0.122144 and the value for the cut-off 2/3 changes from 0.099488 to 0.090828. The sentence on p. 141 “E.g. for block size 4 and q = 2/3 the type I error rate is 18% (Table 4).” has to be replaced by “E.g. for block size 4 and q = 2/3 the type I error rate is 15.3% (Table 4).”. There were only minor changes smaller than 0.03. These changes do not affect the interpretation of the results or our recommendations.

Yellow Sea Acoustic Uncertainty Caused by Hydrographic Data Error

2003 ◽  
Author(s):  
Peter C. Chu ◽  
Carlos J. Cintron ◽  
Steven D. Haeger ◽  
David Schneider ◽  
Ruth E. Keenan ◽  
...  
Keyword(s):  
Yellow Sea ◽  
Hydrographic Data ◽  
Data Error

scholarly journals Smoothing properties in multistep backward difference method and time derivative approximation for linear parabolic equations

2005 ◽  
Vol 2005 (4) ◽  
pp. 523-536
Author(s):  
Yubin Yan
Keyword(s):  
Difference Method ◽  
Parabolic Equations ◽  
Parabolic Problem ◽  
Approximation Scheme ◽  
Time Derivative ◽  
Nonsmooth Data ◽  
Smoothing Property ◽  
Linear Parabolic Equations ◽  
Linear Parabolic Problem ◽  
Data Error

A smoothing property in multistep backward difference method for a linear parabolic problem in Hilbert space has been proved, where the operator is selfadjoint, positive definite with compact inverse. By using the solutions computed by a multistep backward difference method for the parabolic problem, we introduce an approximation scheme for time derivative. The nonsmooth data error estimate for the approximation of time derivative has been obtained.

4D-Quantification of alpine permafrost degradation in a bedrock ridge using multiple inversion schemes and deformation measurements

2021 ◽  
Author(s):  
Maike Offer ◽  
Riccardo Scandroglio ◽  
Daniel Draebing ◽  
Michael Krautblatter
Keyword(s):  
Climate Change ◽  
Mechanical Stability ◽  
Regularization Parameter ◽  
Time Lapse ◽  
Permafrost Degradation ◽  
Thermal Dynamics ◽  
Near Surface ◽  
Data Error ◽  
Near Surface Geophysics ◽  
Thawing Rate

<p>Warming of permafrost in steep rock walls decreases their mechanical stability and could triggers rockfalls and rockslides. However, the direct link between climate change and permafrost degradation is seldom quantified with precise monitoring techniques and long-term time series. Where boreholes are not possible, laboratory-calibrated Electrical Resistivity Tomography (ERT) is presumably the most accurate quantitative permafrost monitoring technique providing a sensitive record for frozen vs. unfrozen bedrock. Recently, 4D inversions allow also quantification of frozen bedrock extension and of its changes with time (Scandroglio et al., in review).</p><p>In this study we (i) evaluate the influence of the inversion parameters on the volumes and (ii) connect the volumetric changes with measured mechanical consequences.</p><p>The ERT time-serie was recorded between 2006 and 2019 in steep bedrock at the permafrost affected Steintälli Ridge (3100 m asl). Accurately positioned 205 drilled-in steel electrodes in 5 parallel lines across the rock ridge have been repeatedly measured with similar hardware and are compared to laboratory temperature-resistivity (T–ρ) calibration of water-saturated samples from the field. Inversions were conducted using the open-source software BERT for the first time with the aim of estimating permafrost volumetric changes over a decade.</p><p>(i) Here we present a sensitivity analysis of the outcomes by testing various plausible inversion set-ups. Results are computed with different input data filters, data error model, regularization parameter (λ), model roughness reweighting and time-lapse constraints. The model with the largest permafrost degradation was obtained without any time-lapse constraints, whereas constraining each model with the prior measurement results in the smallest degradation. Important changes are also connected to the data error estimation, while other setting seems to have less influence on the frozen volume. All inversions confirmed a drastic permafrost degradation in the last 13 years with an average reduction of 3.900±600 m<sup>3</sup> (60±10% of the starting volume), well in agreement with the measured air temperatures increase.</p><p>(ii) Average bedrock thawing rate of ~300 m<sup>3</sup>/a is expected to significantly influence the stability of the ridge. Resistivity changes are especially evident on the south-west exposed side and in the core of the ridge and are here connected to deformations measured with tape extensometer, in order to precisely estimate the mechanical consequences of bedrock warming.</p><p>In summary, the strong degradation of permafrost in the last decade it’s here confirmed since inversion settings only have minor influence on volume quantification. Internal thermal dynamics need correlation with measured external deformation for a correct interpretation of stability consequences. These results are a fundamental benchmark for evaluating mountain permafrost degradation in relation to climate change and demonstrate the key role of temperature-calibrated 4D ERT.</p><p> </p><p>Reference:</p><p>Scandroglio, R. et al. (in review) ‘4D-Quantification of alpine permafrost degradation in steep rock walls using a laboratory-calibrated ERT approach’, <em>Near Surface Geophysics</em>.</p>

scholarly journals Technical and Humanities Students’ Perspectives on the Development and Sustainability of Artificial Intelligence (AI)

2018 ◽  
Vol 10 (9) ◽  
pp. 3066 ◽  
Author(s):  
Vasile Gherheș ◽  
Ciprian Obrad
Keyword(s):  
Artificial Intelligence ◽  
Social Life ◽  
Research Method ◽  
Quantitative Research ◽  
Positive Attitude ◽  
Negative Impact ◽  
Representation Error ◽  
The Social ◽  
The Future ◽  
Humanities Students

This study investigates how the development of artificial intelligence (AI) is perceived by the students enrolled in technical and humanistic specializations at two universities in Timisoara. It has an emphasis on identifying their attitudes towards the phenomenon, on the connotations associated with it, and on the possible impact of artificial intelligence on certain areas of the social life. Moreover, the present study reveals the students’ perceptions on the sustainability of these changes and developments, and therefore aims to reduce the possible negative impact on consumers, and at anticipate the changes that AI will produce in the future. In order to collect the data, the authors have used a quantitative research method. A questionnaire-based sociological survey was completed by 928 students, with a representation error of only ±3%. The analysis has shown that a great number of respondents have a positive attitude towards the emergence of AI, who believe it will influence society for the better. The results have also underscored underlying differences based on the respondents’ type of specialization (humanistic or technical), and their gender.

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