scholarly journals دراسة التغير النسبي لقيم الاوزون في بغداد

2021 ◽  
Vol 2 (45) ◽  
pp. 271-284
Author(s):  
موج ضياء حسين ◽  
علي مهدي الدجيلي
Keyword(s):  
Trend Detection

المستخلص:        يعد علم المناخ من العلوم التطبيقية التي قلما نجد منافسا له في مجال ارتباطه الوثيق بحياة الانسان ومظاهر نشاطه المختلفة , كونه من العوامل الاساسية المؤثرة في النشاطات الحياتية لمختلف الكائنات الحية , وعلية فهو يمكن الباحثين من فهم مختلف الظواهر الحيوية وتفسيرها بعد فهم محيطها , وبالتالي وضع استراتيجيات , تكون بمثابة واق للبيئة من خطر التدهور والتلوث , وعند ذلك يكون قد حافظ على الحياة الطبيعية . يرمي البحث الى تحديد قيم الأوزون الكلية في العراق , من خلال استخدام الطرق الاحصائية واختيار أهم الطرائق التي يمكن من خلالها حساب الأوزون في منطقة الدراسة , وبالتالي تحليل التغير النسبي في منطقة الدراسة تم تطبيق هذا البحث في محطة (بغداد) , لذ سوف يتم في هذا الفصل الاعتماد على استخدام أسلوب الاتجاه العام ومعدل التغير النسبي من اجل إيضاح التغيرات الحاصلة في قيم الأوزون بمنطقة الدراسة, في وللكشف عن الاتجاه العام ومعدل التغير منطقة الدراسة   (Trend Detection) تم حساب الاتجاه العام للمعدلات السنوية للسلاسل الزمنية  (لعناصر المناخ), وتم التعبير عن معامل الاتجاه بالنسبة المئوية لمجمل المتغيرات في عناصر المناخ ,وكذلك بالنسبة لمعدلات التغير السنويAnnual Change)) وفق المعادلة الآتية :.   حيث ان :                                                                                                                           c  = معدل التغير النسبي السنوي*   bi = معامل الاتجاه y = المتوسط الحسابي   ويمكن استخراج ( **bi ) من المعادلة التالية: `X2-`X1=الفرق بين الوسطين T2-T1= الفرق بين الزمنين

scholarly journals Automatic trend detection: Time-biased document clustering

2021 ◽  
pp. 106907
Author(s):  
Sahar Behpour ◽  
Mohammadmahdi Mohammadi ◽  
Mark V. Albert ◽  
Zinat S. Alam ◽  
Lingling Wang ◽  
...  
Keyword(s):  
Document Clustering ◽  
Trend Detection ◽  
Detection Time

ExpanDrogram: Dynamic Visualization of Big Data Segmentation over Time

2021 ◽  
Vol 13 (2) ◽  
pp. 1-27
Author(s):  
A. Khalemsky ◽  
R. Gelbard
Keyword(s):  
Big Data ◽  
Visual Presentation ◽  
Trend Detection ◽  
Dynamic Visualization ◽  
Segmentation Process ◽  
Key Points ◽  
Wide Range ◽  
History Of ◽  
The Individual ◽  
Over Time

In dynamic and big data environments the visualization of a segmentation process over time often does not enable the user to simultaneously track entire pieces. The key points are sometimes incomparable, and the user is limited to a static visual presentation of a certain point. The proposed visualization concept, called ExpanDrogram, is designed to support dynamic classifiers that run in a big data environment subject to changes in data characteristics. It offers a wide range of features that seek to maximize the customization of a segmentation problem. The main goal of the ExpanDrogram visualization is to improve comprehensiveness by combining both the individual and segment levels, illustrating the dynamics of the segmentation process over time, providing “version control” that enables the user to observe the history of changes, and more. The method is illustrated using different datasets, with which we demonstrate multiple segmentation parameters, as well as multiple display layers, to highlight points such as new trend detection, outlier detection, tracking changes in original segments, and zoom in/out for more/less detail. The datasets vary in size from a small one to one of more than 12 million records.

scholarly journals Detecting bursty terms in computer science research

2019 ◽  
Vol 122 (1) ◽  
pp. 681-699 ◽  
Author(s):  
E. Tattershall ◽  
G. Nenadic ◽  
R. D. Stevens
Keyword(s):  
Computer Science ◽  
Historical Data ◽  
Science Research ◽  
Detection Algorithm ◽  
Trend Detection ◽  
Free Text ◽  
Research Topics ◽  
Computer Science Research ◽  
Insight Into ◽  
Over Time

AbstractResearch topics rise and fall in popularity over time, some more swiftly than others. The fastest rising topics are typically called bursts; for example “deep learning”, “internet of things” and “big data”. Being able to automatically detect and track bursty terms in the literature could give insight into how scientific thought evolves over time. In this paper, we take a trend detection algorithm from stock market analysis and apply it to over 30 years of computer science research abstracts, treating the prevalence of each term in the dataset like the price of a stock. Unlike previous work in this domain, we use the free text of abstracts and titles, resulting in a finer-grained analysis. We report a list of bursty terms, and then use historical data to build a classifier to predict whether they will rise or fall in popularity in the future, obtaining accuracy in the region of 80%. The proposed methodology can be applied to any time-ordered collection of text to yield past and present bursty terms and predict their probable fate.

scholarly journals Case studies of the impact of orbital sampling on stratospheric trend detection and derivation of tropical vertical velocities: solar occultation vs. limb emission sounding

2016 ◽  
Vol 16 (18) ◽  
pp. 11521-11534 ◽  
Author(s):  
Luis F. Millán ◽  
Nathaniel J. Livesey ◽  
Michelle L. Santee ◽  
Jessica L. Neu ◽  
Gloria L. Manney ◽  
...  
Keyword(s):  
Case Studies ◽  
Atmospheric Chemistry ◽  
Stratospheric Ozone ◽  
Sampling Bias ◽  
Nonuniform Sampling ◽  
Trend Detection ◽  
Trace Gas ◽  
Uniform Sampling ◽  
Solar Occultation ◽  
The Impact

Abstract. This study investigates the representativeness of two types of orbital sampling applied to stratospheric temperature and trace gas fields. Model fields are sampled using real sampling patterns from the Aura Microwave Limb Sounder (MLS), the HALogen Occultation Experiment (HALOE) and the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS). The MLS sampling acts as a proxy for a dense uniform sampling pattern typical of limb emission sounders, while HALOE and ACE-FTS represent coarse nonuniform sampling patterns characteristic of solar occultation instruments. First, this study revisits the impact of sampling patterns in terms of the sampling bias, as previous studies have done. Then, it quantifies the impact of different sampling patterns on the estimation of trends and their associated detectability. In general, we find that coarse nonuniform sampling patterns may introduce non-negligible errors in the inferred magnitude of temperature and trace gas trends and necessitate considerably longer records for their definitive detection. Lastly, we explore the impact of these sampling patterns on tropical vertical velocities derived from stratospheric water vapor measurements. We find that coarse nonuniform sampling may lead to a biased depiction of the tropical vertical velocities and, hence, to a biased estimation of the impact of the mechanisms that modulate these velocities. These case studies suggest that dense uniform sampling such as that available from limb emission sounders provides much greater fidelity in detecting signals of stratospheric change (for example, fingerprints of greenhouse gas warming and stratospheric ozone recovery) than coarse nonuniform sampling such as that of solar occultation instruments.

Spatio-temporal variability of temperature and potential evapotranspiration over India

2016 ◽  
Vol 7 (4) ◽  
pp. 810-822 ◽  
Author(s):  
P. Sonali ◽  
D. Nagesh Kumar
Keyword(s):  
Hydrological Cycle ◽  
Potential Evapotranspiration ◽  
Grid Point ◽  
Rank Correlation ◽  
Trend Detection ◽  
Detection Analysis ◽  
Change Impact ◽  
Spatio Temporal ◽  
Grid Points ◽  
Significant Factors

Worldwide, major changes in the climate are expected due to global warming, which leads to temperature variations. To assess the climate change impact on the hydrological cycle, a spatio-temporal change detection study of potential evapotranspiration (PET) along with maximum and minimum temperatures (Tmax and Tmin) over India have been performed for the second half of the 20th century (1950–2005) both at monthly and seasonal scale. From the observed monthly climatology of PET over India, high values of PET are envisioned during the months of March, April, May and June. Temperature is one of the significant factors in explaining changes in PET. Hence seasonal correlations of PET with Tmax and Tmin were analyzed using Spearman rank correlation. Correlation of PET with Tmax was found to be higher compared to that with Tmin. Seasonal variability of trend at each grid point over India was studied for Tmax, Tmin and PET separately. Trend Free Pre-Whitening and Modified Mann Kendall approaches, which consider the effect of serial correlation, were employed for the trend detection analysis. A significant trend was observed in Tmin compared to Tmax and PET. Significant upward trends in Tmax, Tmin and PET were observed over most of the grid points in the interior peninsular region.

scholarly journals Of the trend detection problem for a stochastic sequence

1972 ◽  
Vol 12 (4) ◽  
pp. 113-123
Author(s):  
I. Legostayeva
Keyword(s):  
Trend Detection ◽  
Detection Problem

The abstracts (in two languages) can be found in the pdf file of the article. Original author name(s) and title in Russian and Lithuanian: И. Легостаева. К задаче выделения тренда случайной последовательности I. Legostajeva. Apie atsitiktinės sekos trendo išskyrimo uždavinį

scholarly journals Determination of the Starting Point in Time Series for Trend Detection Based on Overlapping Trend

2016 ◽  
Vol 16 (6) ◽  
pp. 98-110
Author(s):  
Gao Xuedong ◽  
Gu Kan
Keyword(s):  
Time Series ◽  
Piecewise Linear ◽  
Negative Influence ◽  
Trend Detection ◽  
Computational Accuracy ◽  
Starting Point ◽  
Time Series Studies ◽  
Definition Of ◽  
Stage Characteristic

Abstract The traditional time series studies consider the time series as a whole while carrying on the trend detection; therefore not enough attention is paid to the stage characteristic. On the other hand, the piecewise linear fitting type methods for trend detection are lacking consideration of the possibility that the same node belongs to multiple trends. The above two methods are affected by the start position of the sequence. In this paper, the concept of overlapping trend is proposed, and the definition of milestone nodes is given on its base; these way not only the recognition of overlapping trend is realized, but also the negative influence of the starting point of sequence is effectively reduced. The experimental results show that the computational accuracy is not affected by the improved algorithm and the time cost is greatly reduced when dealing with the processing tasks on dynamic growing data sequence.

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