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Journal articles on the topic 'The observational method'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Heath, Sue, Lynne Chapman, and The Morgan Centre Sketchers. "Observational sketching as method." International Journal of Social Research Methodology 21, no. 6 (July 16, 2018): 713–28. http://dx.doi.org/10.1080/13645579.2018.1484990.

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2

Jamroz, Dariusz. "The perspective-based observational tunnels method: A new method of multidimensional data visualization." Information Visualization 16, no. 4 (January 24, 2017): 346–60. http://dx.doi.org/10.1177/1473871616686634.

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The article describes a new unique method of multidimensional data visualization. It has been developed as modified observational tunnels method, which was previously known and used many times. The modification consists in supplementing the observational tunnels method used for visualization of multidimensional data with the concept of perspective. In this way, the orientation and navigation in multidimensional space are largely facilitated. The differences in effects of observational tunnels method and perspective-based observational tunnels method have been presented. The effectiveness of the new visualization method has been compared with selected four well-known methods of multidimensional data visualization: parallel coordinates, orthogonal projection, principal component analysis, and multidimensional scaling. The research revealed that the perspective-based observational tunnels method sometimes makes it possible to obtain information about significant features of analyzed data even when other methods selected for comparative studies are not able to show it. This article includes a presentation of the views of 5-dimensional data obtained from the print recognition process, which allowed the author to state that the features chosen for the development of spatial features are, in this case, sufficient for the correct recognition process. The previously published ranking presenting seven different methods of multidimensional data visualization was supplemented with the perspective-based observational tunnels method. This ranking was conducted using 7-dimensional data describing different types of coal. Thus, it was shown that, in this case, the presented method constitutes the efficient tool among other qualitative visualization analysis methods.

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3

Powderham, A. J. "The observational method?learning from projects." Geotechnical Engineering 155, no. 1 (January 2002): 59–69. http://dx.doi.org/10.1680/geng.155.1.59.40628.

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4

Peck, R. B. "The observational method can be simple." Proceedings of the Institution of Civil Engineers - Geotechnical Engineering 149, no. 2 (April 2001): 71–74. http://dx.doi.org/10.1680/geng.2001.149.2.71.

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Powderham, A. J. "The observational method—learning from projects." Proceedings of the Institution of Civil Engineers - Geotechnical Engineering 155, no. 1 (January 2002): 59–69. http://dx.doi.org/10.1680/geng.2002.155.1.59.

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6

Schubert, Wulf. "The Development of the Observational Method." Geomechanik und Tunnelbau 1, no. 5 (October 2008): 352–57. http://dx.doi.org/10.1002/geot.200800035.

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7

Zhang, Tong-Jie, Cong Ma, and Tian Lan. "Constraints on the Dark Side of the Universe and Observational Hubble Parameter Data." Advances in Astronomy 2010 (2010): 1–14. http://dx.doi.org/10.1155/2010/184284.

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This paper is a review on the observational Hubble parameter data that have gained increasing attention in recent years for their illuminating power on the dark side of the universe: the dark matter, dark energy, and the dark age. Currently, there are two major methods of independent observationalH(z)measurement, which we summarize as the “differential age method” and the “radial BAO size method.” Starting with fundamental cosmological notions such as the spacetime coordinates in an expanding universe, we present the basic principles behind the two methods. We further review the two methods in greater detail, including the source of errors. We show how the observationalH(z)data present itself as a useful tool in the study of cosmological models and parameter constraint, and we also discuss several issues associated with their applications. Finally, we point the reader to a future prospect of upcoming observation programs that will lead to some major improvements in the quality of observationalH(z)data.

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Hardy, Stuart, Duncan Nicholson, Peter Ingram, Asim Gaba, Ying Chen, and Giovanna Biscontin. "New observational method framework for embedded walls." Geotechnical Research 5, no. 3 (September 2018): 122–29. http://dx.doi.org/10.1680/jgere.18.00013.

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Prakash, K., A. Sridharan, and M. Sheshashayana. "Appraisal of observational method for consolidation analysis." Proceedings of the Institution of Civil Engineers - Geotechnical Engineering 167, no. 6 (December 2014): 518–25. http://dx.doi.org/10.1680/geng.13.00006.

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10

Finno, Richard J., and Michele Calvello. "Supported Excavations: Observational Method and Inverse Modeling." Journal of Geotechnical and Geoenvironmental Engineering 131, no. 7 (July 2005): 826–36. http://dx.doi.org/10.1061/(asce)1090-0241(2005)131:7(826).

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11

Grandov, Zorka, Verica Jovanovic, and Maja Djokic. "Implementation of observational method in market research." Poslovna ekonomija 10, no. 1 (2016): 139–56. http://dx.doi.org/10.5937/poseko1601139g.

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12

K.N, Girish, Praveen Patil, and Raj Shekhar. "Acromio Axillo Suprasternal Notch Index a New Method of Predicting Difficult Intubation: Prospective Observational Study." Indian Journal of Anesthesia and Analgesia 5, no. 10 (2018): 1740–45. http://dx.doi.org/10.21088/ijaa.2349.8471.51018.24.

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13

Arulrajah, A., H. Nikraz, and M. W. Bo. "Observational method of assessing improvement of marine clay." Proceedings of the Institution of Civil Engineers - Ground Improvement 8, no. 4 (January 2004): 151–69. http://dx.doi.org/10.1680/grim.2004.8.4.151.

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14

Sasar, Mohammadhasan, and S. Mohsen Haeri. "Improving final settlement predictions of the observational method." Proceedings of the Institution of Civil Engineers - Geotechnical Engineering 166, no. 6 (December 2013): 601–9. http://dx.doi.org/10.1680/geng.10.00089.

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15

Beadman, D., R. Bailey, T. Roberts, S. Welman, P. Thurlow, and M. Fauschou. "The Copenhagen Metro—observational method at Norreport station." Proceedings of the Institution of Civil Engineers - Geotechnical Engineering 149, no. 4 (October 2001): 231–36. http://dx.doi.org/10.1680/geng.2001.149.4.231.

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16

Brown, Stuart M., David R. Lincoln, and William A. Wallace. "Application of Observational Method to Hazardous Waste Engineering." Journal of Management in Engineering 6, no. 4 (October 1990): 479–500. http://dx.doi.org/10.1061/(asce)9742-597x(1990)6:4(479).

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17

Eager, Carolyn, and Charles Oppenheim. "An observational method for undertaking user needs studies." Journal of Librarianship and Information Science 28, no. 1 (January 1996): 15–23. http://dx.doi.org/10.1177/096100069602800103.

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18

Nishiura, Hiroshi. "Bayesian Modeling Method for an Observational Data Analysis." Internal Medicine 59, no. 21 (November 1, 2020): 2641–42. http://dx.doi.org/10.2169/internalmedicine.4670-20.

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19

Freeman, Joshua, Barbara F. Sharf, and Janice L. Benson. "Observational data in faculty development: An underused method." Teaching and Learning in Medicine 4, no. 2 (January 1992): 103–9. http://dx.doi.org/10.1080/10401339209539542.

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20

Santos, Carlos, and Anna Ghelli. "Observational probability method to assess ensemble precipitation forecasts." Quarterly Journal of the Royal Meteorological Society 138, no. 662 (August 11, 2011): 209–21. http://dx.doi.org/10.1002/qj.895.

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21

Abdrabbo, Fathi M., and Khaled E. Gaaver. "Applications of the observational method in deep foundations." Alexandria Engineering Journal 51, no. 4 (December 2012): 269–79. http://dx.doi.org/10.1016/j.aej.2012.10.004.

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22

Anggraheni, Dinia, and Dimas Gustoro. "Rainfall Distribution Pattern of Progo Watershed in Observational and Empirical Method." MATEC Web of Conferences 280 (2019): 05005. http://dx.doi.org/10.1051/matecconf/201928005005.

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Automatic Rainfall Recorder (ARR) has not been widely available in Indonesia, so data of rainfall is available in the form of daily rainfall. To know the pattern of rainfall distribution in a watershed, there are two methods, they are the observational and empirical method. the Observational Method can be done if it has automatic rainfall data minimal hourly-time in a long period. Meanwhile, the empirical method will be used when data is very limited. So in this research, there were two methods to develop the rainfall distribution pattern in Progo Watershed. The data were used by the observational method is from 2013 to 2015. There were two empirical methods which are used namely Alternating Block Method (ABM) and Modified Mononobe Method. The results show that heavy rainfall in Progo watershed is dominated by 7-hourly rainfall distribution. The empirical rainfall distribution pattern that is more suitable with the observation method is Modified Mononobe method than ABM method.

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23

Gouveia, Élvio R., Bruna R. Gouveia, Adilson Marques, Helder Lopes, Ana Rodrigues, Tomás Quintal, Marcelo Pestana, Miguel Peralta, Matthias Kliegel, and Andreas Ihle. "Estimation of Engagement in Moderate-to-Vigorous Physical Activity from Direct Observation: A Proposal for School Physical Education." Children 8, no. 2 (January 21, 2021): 67. http://dx.doi.org/10.3390/children8020067.

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This study aimed to test an observational momentary time sampling tool to estimate in-class moderate-to-vigorous physical activity (MVPA), in terms of validity, reliability and agreement between observational momentary time sampling and accelerometry, and to develop a regression equation to estimate MVPA from observational momentary time sampling. The sample comprised 78 pupils (38 girls), mean age 14.0 ± 1.1 years. Measurements were taken in three similar Physical Education classes, on three different days. To monitor MVPA, we applied the observational momentary time sampling method. Students wore an ActiGraph GT3X+ accelerometer. Reliabilities were determined by the intraclass correlations, the agreement between methods was analyzed using the Bland–Altman method, and a multiple regression analysis was performed to estimate the equation. The observational momentary time sampling showed good reliability across time (0.59 < r < 0.72, p < 0.001). It was significantly correlated with accelerometry (r = 0.51, p < 0.001). The MVPA assessed via accelerometer could be predicted from the following equation: Y = 44.3 + 0.47⋅(MVPA observational momentary time sampling method) + 8.0⋅(sex; with 0 = girls and 1 = boys). This observational momentary time sampling method is a stable and reliable tool to estimate MVPA. A regression equation using the score of observational momentary time sampling and sex can be used to better estimate the real MVPA.

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24

Al-Mawan, Nuran Medhat, El-Houssainy Rady, and Nasr Rashwan. "Median Double Ranked Set Sampling Method." JOURNAL OF ADVANCES IN MATHEMATICS 14, no. 1 (March 30, 2018): 7503–12. http://dx.doi.org/10.24297/jam.v14i1.7173.

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In environmental monitoring and assessment, the main focus is to achieve observational economy and to collect data with unbiased, efficient and cost-effective sampling methods. Ranked set sampling (RSS) is one traditional method that is mostly used for accomplishing observational economy. In this article, we suggested new sampling method called median double ranked set sampling (MDRSS). The newly suggested sampling method MDRSS is compare to the simple random sampling (SRS), RSS, double ranked set sampling (DRSS), median ranked set sampling (MRSS). When the underlying distributions are symmetric and asymmetric, it is shown that, the variance of the mean estimator under MDRSS is always less than the variance of the mean estimator based on SRS and the other methods.

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25

Górska, Karolina, Zbigniew Muszyński, and Jarosław Rybak. "DISPLACEMENT MONITORING AND SENSITIVITY ANALYSIS IN THE OBSERVATIONAL METHOD." Studia Geotechnica et Mechanica 35, no. 3 (September 1, 2013): 25–43. http://dx.doi.org/10.2478/sgem-2013-0028.

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Abstract This work discusses the fundamentals of designing deep excavation support by means of observational method. The effective tools for optimum designing with the use of the observational method are both inclinometric and geodetic monitoring, which provide data for the systematically updated calibration of the numerical computational model. The analysis included methods for selecting data for the design (by choosing the basic random variables), as well as methods for an on-going verification of the results of numeric calculations (e.g., MES) by way of measuring the structure displacement using geodetic and inclinometric techniques. The presented example shows the sensitivity analysis of the calculation model for a cantilever wall in non-cohesive soil; that analysis makes it possible to select the data to be later subject to calibration. The paper presents the results of measurements of a sheet pile wall displacement, carried out by means of inclinometric method and, simultaneously, two geodetic methods, successively with the deepening of the excavation. This work includes also critical comments regarding the usefulness of the obtained data, as well as practical aspects of taking measurement in the conditions of on-going construction works.

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26

Zare, Mohsen, Maxime Norval, Julie Bodin, and Yves Roquelaure. "Differences between risk situations identified using a self-reported questionnaire and an observational method." Work 68, no. 3 (March 26, 2021): 759–69. http://dx.doi.org/10.3233/wor-203409.

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Background: This study investigates work-related musculoskeletal disorders risk estimation by frequently as used as ergonomic methods in the field. Objective: To identify the difference in risk estimation by an in-house observational method and a self-reported questionnaire, and to evaluate the complementary aspects of these methods. Methods: A sample of 15 operators who worked on the assembly workstations was selected from a truck manufacturing plant. The risk assessment of these workstations (28 scenarios) was performed by the observational method and the self-reported questionnaire. The agreement between both methods to identify risk situations was measured with the weighted Kappa coefficient. Results: The observational method and the self-reported questionnaire deployed on the same activity estimated different risk situations. Conclusion: This analysis does not reveal that one tool is more powerful than the other one, but shows the probability of different risk estimation. The complementary effect of each method might be considered for further investigation concerning musculoskeletal risk factors.

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27

Amari, T., and J. J. Aly. "Observational constraints on well-posed reconstruction methods and the optimization-Grad-Rubin method." Astronomy & Astrophysics 522 (November 2010): A52. http://dx.doi.org/10.1051/0004-6361/200913058.

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28

Glass, P. R., and A. J. Powderham. "Application of the observational method at the Limehouse Link." Géotechnique 44, no. 4 (December 1994): 665–79. http://dx.doi.org/10.1680/geot.1994.44.4.665.

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29

Spross, Johan, and Fredrik Johansson. "When is the observational method in geotechnical engineering favourable?" Structural Safety 66 (May 2017): 17–26. http://dx.doi.org/10.1016/j.strusafe.2017.01.006.

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30

Chapman, Tim, and George Green. "Observational method looks set to cut city building costs." Proceedings of the Institution of Civil Engineers - Civil Engineering 157, no. 3 (August 2004): 125–33. http://dx.doi.org/10.1680/cien.2004.157.3.125.

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31

Asano, Takeshi, Mototsugu Ishihara, Yasuaki Kiyota, Hiroyuki Kurosawa, and Seiji Ebisu. "An observational excavation control method for adjacent mountain tunnels." Tunnelling and Underground Space Technology 18, no. 2-3 (April 2003): 291–301. http://dx.doi.org/10.1016/s0886-7798(03)00043-9.

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32

Moritz, Bernd, and Wulf Schubert. "Application of the observational method in geotechnical safety management." Geomechanik und Tunnelbau 2, no. 3 (June 2009): 269–81. http://dx.doi.org/10.1002/geot.200900021.

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33

Wu, Shih-Hsuan, Jianye Ching, and Chang-Yu Ou. "Probabilistic observational method for estimating wall displacements in excavations." Canadian Geotechnical Journal 51, no. 10 (October 2014): 1111–22. http://dx.doi.org/10.1139/cgj-2013-0116.

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In this study, a novel method for updating the probability distribution of the maximum wall displacement at the ith excavation stage ([Formula: see text]) based on the measurements at earlier stages is proposed. The main novelty of the proposed method is in the updating procedure, which incorporates the correlation among the estimation errors at various stages. This “stage correlation” is evident from a database of wall displacement data from 22 case histories. By incorporating the stage correlation, it is shown that the uncertainty in [Formula: see text] can be effectively reduced through a Bayesian analysis. Furthermore, the calculation steps for such updating can be easily implemented by practical engineers because these calculation steps involve only algebraic computations and chart checking. Sophisticated analyses, such as solving an optimization problem (required by the maximum likelihood method) and probabilistic analyses are not necessary because all of the Bayesian analysis results are summarized in the charts.

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34

Pontikis, C., E. Hicks, A. Rigaud, and D. Baumgardner. "A Method for Validating FSSP Measurements Using Observational Data." Journal of Atmospheric and Oceanic Technology 8, no. 6 (December 1991): 802–11. http://dx.doi.org/10.1175/1520-0426(1991)008<0802:amfvfm>2.0.co;2.

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35

Chung, S. G., H. J. Kweon, and W. Y. Jang. "Observational method for field performance of prefabricated vertical drains." Geotextiles and Geomembranes 42, no. 4 (August 2014): 405–16. http://dx.doi.org/10.1016/j.geotexmem.2014.06.005.

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36

Chander, Divyaksh Subhash, and Maria Pia Cavatorta. "An observational method for Postural Ergonomic Risk Assessment (PERA)." International Journal of Industrial Ergonomics 57 (January 2017): 32–41. http://dx.doi.org/10.1016/j.ergon.2016.11.007.

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37

Somensi, Rute Merlo, Rita Catalina Aquino Caregnato, Gustavo Henrique Cervi, and Cecilia Dias Flores. "Workload: a comparison between the online and observational methods." Revista Brasileira de Enfermagem 71, no. 4 (August 2018): 1850–57. http://dx.doi.org/10.1590/0034-7167-2017-0313.

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ABSTRACT Objective: To measure nursing Workload (WL) of nurses who work in the Inpatient Unit, as recommended by the Nursing Interventions Classification (NIC), comparing observational and online methods to propose supervision strategies for academic professionals. Method: Quantitative, descriptive, observational study performed in a Clinical/Surgical Hospital Unit. 30 direct and indirect activities. Data collected in observational and online records. Statistical analysis: SPSS 18.0 software, percentage frequencies and associated times between groups by Fisher's Exact test, 95% confidence interval, significance level 5%. Results: Comparing the activities performed with the NIC time: from the direct 16, five observational and five online, were out of range, with no significant difference between frequencies (P=0.427). Of the 14 indirect, only in the observational, two were out of the range, without significant difference (P=0.486). Conclusion: Both methods measure WL; the online method developed accompanies activities performed in real time.

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38

Dindo, Marietta, Tara Stoinski, and Andrew Whiten. "Observational learning in orangutan cultural transmission chains." Biology Letters 7, no. 2 (September 15, 2010): 181–83. http://dx.doi.org/10.1098/rsbl.2010.0637.

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Field reports suggest that orangutans acquire local traditions by observing neighbouring conspecifics. However, there is little direct evidence of social learning to support this conclusion. The present study investigated whether orangutans would learn a novel foraging method through observation of a conspecific in a diffusion-chain paradigm testing for the spread of the behaviour. A foraging box with two possible methods for extracting food was used to investigate the transmission of a foraging tradition among zoo-living subjects. In a socially housed group of five orangutans, the dominant male was trained to use one technique exclusively to retrieve food. He then performed this technique in the presence of another group member, who was then allowed to forage. After several trials, the observer became the model for the next individual. A second experimental group of six individuals was introduced to the alternative method. The model-seeded technique was successfully transmitted along both experimental chains, with significant preferences for the model method. These results are consistent with claims for social transmission of foraging methods in wild orangutans.

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39

Walton, Jamie S. "Random assignment in sexual offending programme evaluation: the missing method." Journal of Forensic Practice 20, no. 1 (February 12, 2018): 1–9. http://dx.doi.org/10.1108/jfp-08-2017-0032.

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Purpose The need for random assignment in sexual offending programme evaluation is clear. Decades of high dependence on weak-inference methodology, that of observational studies, has inhibited professional agreement regarding the effects of programmes. Observational studies have a place in evaluation research when more rigorous scientific designs precede them, as occurs in neighbouring fields of drug development and health. If, however, observational studies remain the only method used to evaluate sexual offending programmes, the field will continue to endure uncertainty with confident causal inferences regarding their effects remaining elusive. The paper aims to discuss these issues. Design/methodology/approach The paper takes the form of a literature review and discussion. Findings The case for random assignment is made alongside a rebuttal of arguments against their use. Originality/value This is an original look at the need for random assignment in sexual offending programme evaluation taking into account existing studies and discussion topics.

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40

SUZUKI, Yoshio, Yoshiaki KAWAKAMI, and Norihiro NAKAJIMA. "Investigation of error estimation method of observational data and comparison method between numerical and observational results toward V&V of seismic simulation." Mechanical Engineering Reviews 4, no. 1 (2017): 15–00525. http://dx.doi.org/10.1299/mer.15-00525.

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41

Shin, In-Soo, and Chai Hong Rim. "Stepwise-Hierarchical Pooled Analysis for Synergistic Interpretation of Meta-analyses Involving Randomized and Observational Studies: Methodology Development." Journal of Medical Internet Research 23, no. 9 (September 2, 2021): e29642. http://dx.doi.org/10.2196/29642.

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Background The necessity of including observational studies in meta-analyses has been discussed in the literature, but a synergistic analysis method for combining randomized and observational studies has not been reported. Observational studies differ in validity depending on the degree of the confounders’ influence. Combining interpretations may be challenging, especially if the statistical directions are similar but the magnitude of the pooled results are different between randomized and observational studies (the ”gray zone”). Objective To overcome these hindrances, in this study, we aim to introduce a logical method for clinical interpretation of randomized and observational studies. Methods We designed a stepwise-hierarchical pooled analysis method to analyze both distribution trends and individual pooled results by dividing the included studies into at least three stages (eg, all studies, balanced studies, and randomized studies). Results According to the model, the validity of a hypothesis is mostly based on the pooled results of randomized studies (the highest stage). Ascending patterns in which effect size and statistical significance increase gradually with stage strengthen the validity of the hypothesis; in this case, the effect size of the observational studies is lower than that of the true effect (eg, because of the uncontrolled effect of negative confounders). Descending patterns in which decreasing effect size and statistical significance gradually weaken the validity of the hypothesis suggest that the effect size and statistical significance of the observational studies is larger than the true effect (eg, because of researchers’ bias). Conclusions We recommend using the stepwise-hierarchical pooled analysis approach for meta-analyses involving randomized and observational studies.

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42

GLASS, KATHRYN, MASAHARU ADACHI, and ALISTAIR MEES. "NOISE TOLERANCE OF THE AGOY CONTROL METHOD FOR STABILIZING CHAOTIC SYSTEMS." International Journal of Bifurcation and Chaos 06, no. 07 (July 1996): 1333–40. http://dx.doi.org/10.1142/s0218127496000758.

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Presented by D. Auerbach, C. Grebogi, E. Ott, and J.A. Yorke in Auerbach et al. [1992], the AGOY control method allows us to stabilize periodic points of a chaotic map without knowledge of the underlying equations governing the system, and using only a one-dimensional time series. We are interested to discover the effect of observational noise on the success of the algorithm. In this paper, we will investigate the tolerance of this algorithm under observational noise and compare this tolerance to that of other stabilization algorithms.

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43

Ding, Yulin, Hui Lin, and Rongrong Li. "CHANGE SEMANTIC CONSTRAINED ONLINE DATA CLEANING METHOD FOR REAL-TIME OBSERVATIONAL DATA STREAM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 177–83. http://dx.doi.org/10.5194/isprsarchives-xli-b2-177-2016.

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Recent breakthroughs in sensor networks have made it possible to collect and assemble increasing amounts of real-time observational data by observing dynamic phenomena at previously impossible time and space scales. Real-time observational data streams present potentially profound opportunities for real-time applications in disaster mitigation and emergency response, by providing accurate and timeliness estimates of environment’s status. However, the data are always subject to inevitable anomalies (including errors and anomalous changes/events) caused by various effects produced by the environment they are monitoring. The “big but dirty” real-time observational data streams can rarely achieve their full potential in the following real-time models or applications due to the low data quality. Therefore, timely and meaningful online data cleaning is a necessary pre-requisite step to ensure the quality, reliability, and timeliness of the real-time observational data. <br><br> In general, a straightforward streaming data cleaning approach, is to define various types of models/classifiers representing normal behavior of sensor data streams and then declare any deviation from this model as normal or erroneous data. The effectiveness of these models is affected by dynamic changes of deployed environments. Due to the changing nature of the complicated process being observed, real-time observational data is characterized by diversity and dynamic, showing a typical Big (Geo) Data characters. Dynamics and diversity is not only reflected in the data values, but also reflected in the complicated changing patterns of the data distributions. This means the pattern of the real-time observational data distribution is not <i>stationary or static</i> but <i>changing and dynamic</i>. After the data pattern changed, it is necessary to adapt the model over time to cope with the changing patterns of real-time data streams. Otherwise, the model will not fit the following observational data streams, which may led to large estimation error. In order to achieve the best generalization error, it is an important challenge for the data cleaning methodology to be able to characterize the behavior of data stream distributions and adaptively update a model to include new information and remove old information. However, the complicated data changing property invalidates traditional data cleaning methods, which rely on the assumption of a stationary data distribution, and drives the need for more dynamic and adaptive online data cleaning methods. <br><br> To overcome these shortcomings, this paper presents a change semantics constrained online filtering method for real-time observational data. Based on the principle that the filter parameter should vary in accordance to the data change patterns, this paper embeds semantic description, which quantitatively depicts the change patterns in the data distribution to self-adapt the filter parameter automatically. Real-time observational water level data streams of different precipitation scenarios are selected for testing. Experimental results prove that by means of this method, more accurate and reliable water level information can be available, which is prior to scientific and prompt flood assessment and decision-making.

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44

Ding, Yulin, Hui Lin, and Rongrong Li. "CHANGE SEMANTIC CONSTRAINED ONLINE DATA CLEANING METHOD FOR REAL-TIME OBSERVATIONAL DATA STREAM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 177–83. http://dx.doi.org/10.5194/isprs-archives-xli-b2-177-2016.

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Recent breakthroughs in sensor networks have made it possible to collect and assemble increasing amounts of real-time observational data by observing dynamic phenomena at previously impossible time and space scales. Real-time observational data streams present potentially profound opportunities for real-time applications in disaster mitigation and emergency response, by providing accurate and timeliness estimates of environment’s status. However, the data are always subject to inevitable anomalies (including errors and anomalous changes/events) caused by various effects produced by the environment they are monitoring. The “big but dirty” real-time observational data streams can rarely achieve their full potential in the following real-time models or applications due to the low data quality. Therefore, timely and meaningful online data cleaning is a necessary pre-requisite step to ensure the quality, reliability, and timeliness of the real-time observational data. <br><br> In general, a straightforward streaming data cleaning approach, is to define various types of models/classifiers representing normal behavior of sensor data streams and then declare any deviation from this model as normal or erroneous data. The effectiveness of these models is affected by dynamic changes of deployed environments. Due to the changing nature of the complicated process being observed, real-time observational data is characterized by diversity and dynamic, showing a typical Big (Geo) Data characters. Dynamics and diversity is not only reflected in the data values, but also reflected in the complicated changing patterns of the data distributions. This means the pattern of the real-time observational data distribution is not <i>stationary or static</i> but <i>changing and dynamic</i>. After the data pattern changed, it is necessary to adapt the model over time to cope with the changing patterns of real-time data streams. Otherwise, the model will not fit the following observational data streams, which may led to large estimation error. In order to achieve the best generalization error, it is an important challenge for the data cleaning methodology to be able to characterize the behavior of data stream distributions and adaptively update a model to include new information and remove old information. However, the complicated data changing property invalidates traditional data cleaning methods, which rely on the assumption of a stationary data distribution, and drives the need for more dynamic and adaptive online data cleaning methods. <br><br> To overcome these shortcomings, this paper presents a change semantics constrained online filtering method for real-time observational data. Based on the principle that the filter parameter should vary in accordance to the data change patterns, this paper embeds semantic description, which quantitatively depicts the change patterns in the data distribution to self-adapt the filter parameter automatically. Real-time observational water level data streams of different precipitation scenarios are selected for testing. Experimental results prove that by means of this method, more accurate and reliable water level information can be available, which is prior to scientific and prompt flood assessment and decision-making.

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Roberts, T. O. L., and M. Preene. "The design of groundwater control systems using the observational method." Géotechnique 44, no. 4 (December 1994): 727–34. http://dx.doi.org/10.1680/geot.1994.44.4.727.

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Choa, V. "Application of the observational method to hydraulic fill reclamation projects." Géotechnique 44, no. 4 (December 1994): 735–45. http://dx.doi.org/10.1680/geot.1994.44.4.735.

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Wakita, E., and M. Matsuo. "Observational design method for earth structures constructed on soft ground." Géotechnique 44, no. 4 (December 1994): 747–55. http://dx.doi.org/10.1680/geot.1994.44.4.747.

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Guo, W., and J. Chu. "New observational method for prediction of one-dimensional consolidation settlement." Géotechnique 67, no. 6 (June 2017): 516–22. http://dx.doi.org/10.1680/jgeot.16.p.089.

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Ingram, Adam R., and Thomas J. Maccarone. "An observational method for fast stochastic X-ray polarimetry timing." Monthly Notices of the Royal Astronomical Society 471, no. 4 (July 24, 2017): 4206–17. http://dx.doi.org/10.1093/mnras/stx1881.

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Sakurai, Shunsuke, Shinichi Akutagawa, Kunifumi Takeuchi, Masato Shinji, and Norikazu Shimizu. "Back analysis for tunnel engineering as a modern observational method." Tunnelling and Underground Space Technology 18, no. 2-3 (April 2003): 185–96. http://dx.doi.org/10.1016/s0886-7798(03)00026-9.

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